阅读说明：本技术 基于构建件的位置和方向仿真优化表面质量的系统和方法 (System and method for optimizing surface quality based on position and direction simulation of building piece ) 是由 埃里克·M·查普曼 达纳·A·亨肖 于 2021-04-20 设计创作，主要内容包括：本申请公开了基于构建件的位置和方向仿真优化表面质量的系统和方法。一种增材制造系统,包括一个或多个处理器,其被配置为在构建部件相对于平台的候选位置处确定构建部件的多个区段中的每个的一个或多个几何特征。一个或多个处理器被配置为基于一个或多个几何特征为候选位置处的每个区段生成质量得分。一个或多个处理器还被配置为生成候选位置处的构建部件的仿真模型以进行显示。仿真模型包括与每个区段相对应的图形指示符。图形指示符代表相应区段的质量得分。(Systems and methods for optimizing surface quality based on position and orientation simulation of a build. An additive manufacturing system includes one or more processors configured to determine one or more geometric features of each of a plurality of sections of a build component at candidate positions of the build component relative to a platform. The one or more processors are configured to generate a quality score for each section at the candidate location based on the one or more geometric features. The one or more processors are further configured to generate a simulation model of the build component at the candidate location for display. The simulation model includes a graphical indicator corresponding to each section. The graphical indicator represents a quality score for the respective segment.)

1. An additive manufacturing system, comprising:

one or more processors configured to determine one or more geometric features of each of a plurality of sections of a build component at a candidate location of a platform relative to the build component, wherein the one or more processors are configured to generate a quality score for each of the sections at the candidate location based on the one or more geometric features, an

Wherein the one or more processors are configured to generate for display a simulation model of the build component at the candidate location, the simulation model including a graphical indicator corresponding to each of the sections, the graphical indicator representing the quality score of the respective section.

2. The additive manufacturing system of claim 1, further comprising a display device operably connected to the one or more processors, the display device configured to display the simulation model of the build component.

3. The additive manufacturing system of claim 1, wherein the simulation model is three-dimensional.

4. The additive manufacturing system of claim 1, wherein the one or more geometric features comprise an angle of incidence between a beam line extending from an electromagnetic energy source of an additive manufacturing tool and a surface normal of a respective skin of the respective segment near the beam line.

5. The additive manufacturing system of claim 1, wherein the graphical indicator is represented by one or more of a color, a number, and a symbol.

6. The additive manufacturing system of claim 1, wherein the one or more processors are configured to determine one or more preferred positions of the build component relative to the platform to achieve improved quality of the build component relative to additive manufacturing the build component at the candidate positions.

7. The additive manufacturing system of claim 6, wherein the one or more geometric features comprise an angle of incidence between a beam line extending from an electromagnetic energy source of an additive manufacturing tool and a surface normal of a respective skin of the respective segment near the beam line, and the one or more processors determine the one or more preferred positions of the build component based on a position of the build component resulting in a greater reduction in the angle of incidence of the segment relative to the angle of incidence of the segment at the candidate position.

8. The additive manufacturing system of claim 6, wherein at the one or more preferred positions of the build component, a longitudinal axis of the build component is tilted toward an electromagnetic energy source of an additive manufacturing tool.

9. The additive manufacturing system of claim 6, wherein in response to receiving a selection of one of the one or more preferred positions as a final position of the build component relative to the platform, the one or more processors are configured to control an additive manufacturing tool to build the build component on the final position of the platform by sequentially depositing layers of material.

10. The additive manufacturing system of claim 1, wherein the one or more processors are further configured to aggregate the quality scores for the segments to calculate an overall location score associated with the candidate locations.

11. The additive manufacturing system of claim 10, wherein the candidate location is a first candidate location, and the one or more processors are further configured to generate quality scores for a plurality of different sections of the build component at a second candidate location on the platform for the build component at the second candidate location based on the one or more geometric features of the sections at the second candidate location, the build component at the second candidate location having at least one of a different position, tilt, and rotational orientation relative to the build component at the first candidate location, the one or more processors configured to aggregate the quality scores for the sections of the build component at the second candidate location to calculate an overall location score associated with the second candidate location, and to pair the second candidate location based on which of the first candidate location and the second candidate location has a greater overall location score The first candidate location and the second candidate location are ranked.

12. The additive manufacturing system of claim 1, further comprising an input device operatively connected to the one or more processors, wherein the one or more processors are configured to receive the candidate locations of the build component on the platform in response to operator commands provided using the input device.

13. A method of additive manufacturing, comprising:

determining one or more geometric features of each of a plurality of sections of a build component at candidate positions of the build component relative to a platform;

generating a quality score for each of the sections at the candidate location based on the one or more geometric features; and

generating for display a simulation model of the build component at the candidate location, the simulation model including a graphical indicator corresponding to each section, the graphical indicator representing the quality score of the respective section.

14. The additive manufacturing method of claim 13, further comprising displaying the simulation model of the build component on a display device.

15. The additive manufacturing method of claim 13, further comprising determining one or more preferred positions of the build component relative to the platform to achieve improved quality of the build component relative to additive manufacturing the build component at the candidate positions.

16. The additive manufacturing method of claim 15, further comprising controlling an additive manufacturing tool to additive manufacture the build component at one of the one or more preferred locations on the platform.

17. The additive manufacturing method of claim 13, wherein the one or more geometric features comprise an angle of incidence between a beam line extending from an electromagnetic energy source of an additive manufacturing tool and a surface normal of a respective segment of a respective skin near the beam line, and a segment of the build component having a lower angle of incidence has a higher quality score than a segment of the build component having a larger angle of incidence.

18. The additive manufacturing method of claim 13, further comprising aggregating the quality scores of the segments to calculate an overall location score associated with the candidate locations.

19. The additive manufacturing method of claim 18, wherein the candidate location is a first candidate location, the additive manufacturing method further comprising:

generating quality scores for a plurality of different sections of the build component at a second candidate location on the platform for the build component based on the one or more geometric features of the sections at the second candidate location, the build component at the second candidate location having at least one of a different position, tilt, and rotational orientation relative to the build component at the first candidate location;

aggregating the quality scores of the sections of the build component at the second candidate location to calculate an overall location score associated with the second candidate location; and

ranking the first candidate location and the second candidate location based on which of the first candidate location and the second candidate location has a greater overall location score.

20. An additive manufacturing system, comprising:

one or more processors configured to determine, at a candidate position of a build component relative to a platform, one or more geometric features of each of a plurality of sections of the build component, the one or more geometric features comprising an angle of incidence between a beam line extending from an electromagnetic energy source of an additive manufacturing tool and a surface normal of a respective skin of the respective section proximate the beam line,

wherein the one or more processors are configured to determine a quality score for each segment at the candidate location based on the one or more geometric features such that segments of the build component with lower angles of incidence have higher quality scores than segments of the build component with larger angles of incidence, and

wherein the one or more processors are configured to compare the quality score of the section at the candidate location to determined quality scores of the sections of the build component at other candidate locations to provide one or more preferred locations of the build component to achieve improved quality of the build component by additive manufacturing the build component at one of the one or more preferred locations relative to additive manufacturing the build component at the candidate location.

Technical Field

Embodiments of the present disclosure generally relate to additive manufacturing of three-dimensional build components.

Background

Additive manufacturing refers to any process of manufacturing a three-dimensional build part, wherein successive layers of a base material are deposited under computer control. The deposited layers are selectively fused by the application of a focused energy source (e.g., a laser) that heats and bonds the material. The size and shape of the build component may be based on a three-dimensional computer model or another source of electronic data. Additive manufacturing can be used to manufacture objects with complex structures and shapes. Additive manufacturing techniques for manufacturing metal build components may allow greater design freedom and produce more accurate and repeatable finished products than conventional metal manufacturing techniques (e.g., die casting, extrusion, etc.).

The setup for the additive manufacturing build process comprises selecting a design of a build part to be built and specifying a position of the build part on a build platform of the additive manufacturing tool. Positioning may refer to the position of the building element relative to the building platform, e.g. relative to the centre and/or edge of the platform, and the orientation of the planned building element relative to the platform, e.g. the direction of rotation about the vertical axis of the building element and/or the angular direction (e.g. inclination or slope). Typically, few factors are considered in determining the position of the building element relative to the platform. One known consideration involves arranging multiple build components on a platform to increase the total number of build components that can be printed in a common build process.

Typically, the location of a build component is determined without regard to how positioning will affect the surface and subsurface qualities (e.g., surface roughness, porosity, and other surface quality characteristics) of the final manufactured build component. After the additive manufacturing process, the build component is typically inspected and the rough surface of the build component is ground to increase smoothness. Post-processing tasks (e.g., grinding the rough surface of the build component) can be expensive, difficult, time consuming, and/or labor intensive. The grinding of rough surfaces and additional post-processing tasks to improve the surface quality of the build part reduces manufacturing efficiency and increases production costs. In addition, typical post-processing may be ineffective or impossible for parts having inaccessible areas, and current methods may be inadequate. Furthermore, if the surface quality, sub-surface quality or dimensional accuracy of a build part is sufficiently degraded during additive manufacturing, the entire build part may need to be scrapped, which is time and resource consuming.

Disclosure of Invention

In one or more embodiments, an additive manufacturing system is provided that includes one or more processors configured to determine one or more geometric features of each of a plurality of sections of a build component at candidate positions of the build component relative to a platform. The one or more processors are configured to generate a quality score for each section at the candidate location based on the one or more geometric features. The one or more processors are further configured to generate a simulation model of the build component at the candidate location for display. The simulation model includes a graphical indicator corresponding to each section. The graphical indicator represents a quality score for the respective segment.

In one or more embodiments, a method (e.g., simulating surface quality of an additively manufactured build component) is provided. The method comprises the following steps: one or more geometric features of each of a plurality of sections of the build component at candidate positions of the build component relative to the platform are determined. The method also includes generating a quality score for each section at the candidate location based on the one or more geometric features and generating a simulation model of the build component at the candidate location for display. The simulated image includes a graphical indicator corresponding to each section. The graphical indicator represents a quality score for the respective segment.

In one or more embodiments, an additive manufacturing system is provided that includes one or more processors configured to determine one or more geometric features of each of a plurality of sections of a build component at candidate positions of the build component relative to a platform. The one or more geometric features include an angle of incidence between a beam line extending from an electromagnetic energy source of the additive manufacturing tool and a surface normal of a respective skin of the respective section proximate to the beam line. The one or more processors are further configured to determine a quality score for each section at the candidate location based on the one or more geometric features such that sections of the building components having lower incidence angles have higher quality scores than sections of the building components having larger incidence angles. The one or more processors are configured to compare the quality score of the segment at the candidate location to determined quality scores of segments of the build component at other candidate locations to provide one or more preferred locations of the build component to achieve improved quality of the build component by additive manufacturing the build component at one of the one or more preferred locations relative to additive manufacturing the build component at the candidate location.

Drawings

These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

fig. 1 is a schematic diagram of an additive manufacturing system according to an embodiment of the present disclosure;

fig. 2 shows a schematic view of an additive manufacturing tool within an additive manufacturing system according to an embodiment;

FIG. 3 shows a close-up portion of the first specimen shown in FIG. 2;

fig. 4 shows a schematic of an additive manufacturing tool during a first phase of construction of a single coupon, in accordance with an embodiment of the present disclosure;

FIG. 5 shows a schematic view of an additive manufacturing tool during a second stage of construction of the coupon shown in FIG. 4;

fig. 6 shows a schematic view of an additive manufacturing tool in a third stage of construction of the test piece shown in fig. 4 and 5;

fig. 7 illustrates an additive manufacturing tool including a virtual build component, in accordance with an embodiment;

FIG. 8 illustrates a simulation model of a build component at a candidate location according to an embodiment;

FIG. 9 illustrates a simulation model of a build component at a second candidate location according to an embodiment;

fig. 10 shows an array of a plurality of build components arranged at different locations on a build platform of an additive manufacturing tool, in accordance with an embodiment; and

fig. 11 is a flow diagram of a method of simulating surface quality of an additively manufactured build component in accordance with an embodiment of the disclosure.

Detailed Description

The foregoing summary, as well as the following detailed description of certain embodiments, will be better understood when read in conjunction with the appended drawings. As used herein, an element or step recited in the singular and proceeded with the word "a" or "an" should be understood as not necessarily excluding plural elements or steps. Furthermore, references to "one embodiment" are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. Furthermore, unless explicitly stated to the contrary, embodiments "comprising" or "having" one or more elements having a particular property may include additional elements not having that property.

Given the cost, time, and labor of performing surface treatments after an additive manufacturing process, techniques for additive manufacturing build components have many advantages that inherently improve surface quality, near surface quality, and geometric accuracy, thereby eliminating or at least speeding up post-build surface treatments. Embodiments of the present disclosure provide systems (e.g., additive manufacturing systems) and methods that simulate surface quality of an additively manufactured component. For example, the system and method may generate a virtual simulation data model based on input parameters related to a proposed position within a component design and an additive manufacturing tool. The virtual simulation model (also referred to herein as a simulation model) is a representation of the build component and indicates the projected surface quality of each different section of the build component if the build component is additively manufactured at the proposed location. The simulation model is a projection generated prior to additive manufacturing of the build component. The simulation model may be generated from mathematical functions and/or historical empirical observations. The simulation model may be displayed as one or more images on a display device for viewing by an operator. The simulation of the surface quality allows for adjustments to be made before building the component based on the provided information. For example, an operator or an automated system may modify proposed positions of the build component on a build platform of the additive manufacturing tool in an effort to improve a surface quality of one or more particular surfaces of the build component and/or increase an overall surface quality of the build component. The system disclosed herein enables building components to be constructed with a high standard of surface quality and dimensional accuracy. For example, the system can produce a build product that meets quality requirements without any post-processing to improve surface quality or with only limited post-processing.

Build components described herein refer to virtual objects designed to be manufactured in additive manufacturing as well as physical objects produced via an additive manufacturing build process. The orientation or position of the build component within the additive manufacturing tool refers to the position and orientation of the build component. For example, a location represents a particular region of a build platform of an additive manufacturing tool where a build component is built layer-by-layer. Typically, a plurality of building elements are built in a common building process, and are therefore arranged at different positions along the upper surface of the building platform. The orientation of the building element generally refers to the direction in which the building element (to be constructed) faces and the inclination or declination of the building element. For example, the building elements may be oriented about a longitudinal axis. The orientation may comprise rotation of the building element about a longitudinal axis. The orientation may also include an inclination or tilt (lean) of the longitudinal axis relative to the vertical axis.

The surface quality of a given surface may refer to surface roughness, porosity content of the walls defining the given surface, structural and/or compositional uniformity of the walls, and the like. Generally, higher quality surfaces of an additively manufactured build component have fewer holes, smaller holes, and are smoother (e.g., less rough) than lower quality surfaces. One or more embodiments described herein are configured to produce a build component having desired and/or satisfactory surface and subsurface quality and dimensional accuracy to reduce scrap rates and reduce the amount of surface finish in post-processing after the build process.

In one or more embodiments disclosed herein, a system analyzes candidate locations of a specified build part on a platform. The candidate locations may be entered by an operator or selected by the system. The system may determine one or more geometric features for each of a plurality of different sections or portions of the build component based on the geometry and the candidate locations of the component. The system may use the respective one or more geometric features of each section to generate a quality score for the surface. For example, the system calculates a quality score for a first section of the build component using the geometric features determined for the first section, and the system calculates a quality score for a second section of the build component using the geometric features determined for the second section. The quality score represents the prediction accuracy, surface quality and/or subsurface quality of the respective segment. The surface quality may include a predicted roughness, porosity, etc. of the surface of the segment. The quality score is a projection or estimate of the segment quality of the physical part if the additive manufacturing tool is controlled to build the part based on the design of the input part, the position of the part relative to the platform, and the build parameters (e.g., power, speed, beam diameter, beam through split, etc.). For the reasons described herein, the system may generate different quality scores for different sections of the build member due, at least in part, to different directions and positions of the sections relative to an electromagnetic energy source emitting a focused beam of energy. One or more embodiments described herein may simulate the quality of a build component to enable one or more parameters to be adjusted prior to manufacturing the build component to enable improved accuracy, surface quality and/or subsurface quality of subsequently built build components relative to building the component at random or initially proposed locations.

The system generates a simulation model based on the component design and the quality scores of the different sections. For example, depending on the component design, the simulation model may include or represent one or more virtual images of the build component. The virtual image may be two-dimensional or three-dimensional. The simulation model may also be capable of simulating the build part at different times in the build process to show the configuration of the different layers. Optionally, the displayed virtual image may include a graphical indicator superimposed on a section of the build component. For example, the virtual image may overlay a first graphical indicator on a first section of the build component and a second graphical indicator on a second section. The graphical indicator may be a color, number, letter, symbol, etc. corresponding to the quality score. The system selects the first graphical indicator because the first graphical indicator represents the first quality score and selects the second graphical indicator because the second graphical indicator represents the second quality score. In a non-limiting example, if the first and second quality scores indicate that the first segment is presumed to have a higher surface quality than the second segment, the first graphical indicator may be a color associated with good or high quality, such as green, and the second graphical indicator may be a color of lesser or lower quality, such as orange. By displaying the virtual image on a display screen, the systems described herein can provide a visual indication of the projected surface quality along different sections of the build component to a person (e.g., an operator of the additive manufacturing tool) prior to depositing any powder layer in the additive manufacturing tool to actually construct the build component.

Alternatively, the operator may input a second candidate location of the build component having a different location and/or a different orientation on the platform than the previously analyzed candidate location using an input device of the system. The system is configured to repeat the analysis based on the second candidate location to generate a second virtual image of the build member. The second virtual image includes a graphical indicator representing a quality score determined based on geometric features of the section of the build component at the second candidate location. The process may be repeated any number of times to generate a plurality of virtual images that describe expected quality characteristics of the build component being manufactured in different positions and/or orientations relative to the additive manufacturing tool. Predictive surface quality information presented to an operator may be used to determine more preferred (e.g., optimized) component locations within a build envelope of an additive manufacturing tool, rather than randomly placing or positioning build components based solely on spacing considerations. Once the operator selects the desired position, the system may control the additive manufacturing tool to build the build component layer by layer at the selected position (e.g., position and orientation).

In an embodiment, the system may automatically evaluate a number of potential candidate locations of the build component (without operator input) to determine a set of one or more preferred locations predicted to have a higher quality than other candidate locations evaluated. The system may then present the one or more preferred locations to the operator to enable the operator to select which preferred location to select for constructing the build component.

Fig. 1 is a schematic diagram of an additive manufacturing system 100, according to an embodiment. Additive Manufacturing (AM) system 100 includes an Additive Manufacturing (AM) tool 101, a control unit 108, an input device 134, and a display device 136. The AM tool 101 includes a build platform (or plate) 102, a housing (enclosure)104, an electromagnetic energy source 106, and a source material applicator 117. The platform 102 is a planar surface of the AM tool 101 and may be represented by a plate, a lower wall of the housing 104, or the like. AM tool 101 performs an additive manufacturing build process to form three-dimensional build component 116. Each build component 116 is constructed from the upper surface 110 of the platform 102 by selectively depositing the source material 111 in successive layers 114 and fusing the source material 111 at specified locations according to a build plan 132. Each layer 114 is relatively thin, such as no greater than 1mm, no greater than 0.5mm, no greater than 0.25mm, and the like.

The AM system 100 in fig. 1 may be used to perform powder bed molten additive manufacturing techniques. Suitable additive manufacturing processes may include, for example, barrel photopolymerization (e.g., stereolithography, digital light processing, continuous digital light processing, light emitting diodes, etc.), powder bed fusion (e.g., adhesive jetting, selective laser melting, etc.), material jetting (e.g., material jetting, nanoparticle jetting, drop on demand, etc.), and multi-jet fusion. At least most of these processes involve depositing a layer of material on the build surface and fusing selective portions of the material using energy and/or in the form of a polymer adhesive (scanning the surface based on a CAD pattern). Other processes involve powder-fed or wire-fed Directed Energy Deposition (DED), in which material is deposited only on the build member, without depositing a wide layer on the build surface, but rather selectively fusing material from that layer.

The source material 111 may be in powder form. In a non-limiting example, the powder includes one or more metals in the form of metal particles, flakes, and the like. The powder may also optionally include a non-metallic filler material mixed with the metallic material. The metal material may include various metal types such as aluminum, stainless steel, copper, nickel, cobalt, titanium, etc., and alloys of various metal types. Possible non-metallic filler materials in the powder may include ceramics, polymers (e.g., plastics), silica, and the like. The powder deposited but not fused to form a portion of the build member 116 defines a powder bed 122 of unused material 111 contained within the wall 112 of the housing 104. In an embodiment, during the build process, the part 116 is encapsulated within the powder bed 122. In an alternative embodiment, the source material 111 is free of metal.

Build element 116 is gradually formed or constructed by accumulating layers 114 along a build axis 126 in a build direction 124. As material 111 is added to the top of feature 116 one after another each time, feature 116 grows in build direction 124. The build direction 124 extends away from the platform 102. In the illustrated embodiment, the build axis 126 is orthogonal to the plane of the upper surface 110 of the platform 102.

AM system 100 creates a new layer of component 116 by spreading a thin layer or stratum of powder material 111 over the top of component 116. For example, the source material applicator 117 of the AM system 100 deposits each layer 114 of the material 111. The source material applicator 117 includes or represents a spreader or recoater device that spreads a thin layer of the material 111 uniformly, an injector that injects the material 111, or the like. The material 111 may be stored in a reservoir prior to use. The source material applicator 117 is controlled by the control unit 108.

The electromagnetic energy source 106 is then controlled by the control unit 108 to emit focused electromagnetic energy toward the source material 111 in the top surface layer 114 to fuse specified portions of the material 111 to the build component 116 to define new layers or portions of the component 116. The focused electromagnetic energy may be in the form of a beam of light that impinges on the layer of powder, causing selected portions of the layer to melt and adhere to the feature 116 to form a new top portion thereof. For example, the energy source 106 may be a laser device that generates a high-energy laser beam. In one embodiment, the electromagnetic energy source 106 is suspended above the top of the powder bed 122. For example, the electromagnetic energy source 106 may be disposed about 0.5 meters (m) above the upper surface 110 of the platform 102. The focused electromagnetic energy emitted from the energy source 106 may be directed or directed to different locations of the powder bed 122 to fuse different selected portions of the top layer 114 to the component 116. The electromagnetic energy source 106 may include a scan head that enables a beam of light to be directed to different locations within a specified coverage area without having to move (e.g., shift) the position of the electromagnetic energy source 106 relative to other components of the AM tool 101. The direction of the scan head and parameters of the energy beam (e.g., timing, energy intensity, beam width, etc.) may be controlled by the control unit 108 via control signals. The process is continuously repeated according to instructions in the build plan 132 until the build part 116 is fully formed.

AM tool 101 may be controlled according to build plan 132 to form one or more external supports 120 to structurally support overhanging features of build component 116 during the build process. One or more external supports 120 are additionally formed during the same build process that forms build component 116. For example, build member 116 and external support 120 are each comprised of a series of stacked layers of material that are fused together during the additive manufacturing process. Alternatively, the internal structure (e.g., density, lattice, etc.) and/or material composition of external support 120 may be different than build member 116. For example, the structure of the external support 120 may be less dense than the structure of the building elements 116.

The control unit 108 represents hardware circuitry that includes and/or is coupled to one or more processors 118 (e.g., one or more microprocessors, integrated circuits, microcontrollers, field programmable gate arrays, etc.) that perform the operations described in conjunction with the control unit 108. The one or more processors 118 may operate based on programmed instructions. The one or more processors 118 may include a single processor or multiple processors to perform the functions described herein. The one or more processors 118 are referred to herein in the plural as a "processor," without limiting the scope to requiring multiple processors 118. The control unit 108 also includes a tangible and non-transitory computer-readable storage medium (e.g., memory) 130. Memory 130 may store programming instructions (i.e., software) that direct the operation of processor 118. For example, the memory 130 stores a build plan 132 associated with the build component 116 being manufactured.

Memory 130 may also store a part design file 138 that builds part 116. The component design file 138 may be a Computer Aided Design (CAD) file or other data file that describes physical features of the component 116, such as the shape, size, and/or composition of the component 116. Build plan 132 may be generated based on part design files 138. For example, build plan 132 may be a data file that indicates parameters, conditions, settings, and/or operations of AM tool 101 in order to produce physical build component 116, which is a copy or replica of the virtual component defined by design file 138. The one or more parameters or settings indicated by build plan 132 may include the positioning of build component 116 on platform 102, a series of actions taken by AM tool 101 to build component 116 (e.g., the path of the focused energy beam), the position of support 120, and the like. Additional parameters specified in the build plan 132 may include settings for focused electromagnetic energy (e.g., power, beam width, etc.), offsets, layer thicknesses, gas flow parameters, etc. The control unit 108 (e.g., the processor 118 thereof) controls the operation of the electromagnetic energy source 106, the source material applicator 117, and/or other components to produce the build component 116 based on the build plan 132.

The processor 118 of the control unit 108 is communicatively connected to an input device 134 and a display device 136. Input devices 134 may include a touch pad, touch screen, keyboard, mouse, physical buttons, joystick, or the like. The input device 134 enables an operator to provide commands to the AM system 100. In a non-limiting example, the operator may select and/or modify candidate locations of the build component 116 on the platform 102 using the input device 134. The display device 136 includes a display screen configured to display the simulated image generated by the control unit 108. Alternatively, the input and display devices 134, 136 may be integrated within a single device such as a laptop computer, desktop computer, workstation, tablet computer, mobile, handheld computing device (e.g., smartphone), and so forth. The processor 118 may be operatively connected to the input device 134 and/or the display device 136 via a wired or wireless communication path.

In one embodiment, the processor 118 of the control unit 108 is configured to generate the build plan 132. For example, the processor 118 may access a component design file 138 stored in the memory 130. The processor 118 may receive user input to select a desired location of the build member 116 on the platform 102. One or more embodiments described herein may help select a desired location of the building element 116. The processor 118 may generate the build plan 132 based on the design of the component 116 and the desired location of the component 116. For example, build plan 132 is generated to outline a series of actions of AM tool 101 to build part 116 to have a specified design and at a desired position and orientation relative to platform 102. Design files 138 for the design may be received from a remote computing device or may be generated locally through operator input on input device 134. In an alternative embodiment, the processor 118 does not generate the build plan 132, but rather implements control instructions generated remotely from the AM tool 101. For example, the machine instructions may be processed externally by a computer or processing unit and transferred to the AM tool 101 for execution by the AM tool 101.

Fig. 2 shows a schematic diagram of an AM tool 101 according to an embodiment. Fig. 2 shows a first coupon 202, a second coupon 204, and a third coupon 206 that are additive manufactured on the upper surface 110 of the platform 102. The specimens 202, 204, 206 are discrete and spaced apart from one another, but may represent different sections of a single build component (e.g., component 116 shown in fig. 1) that will subsequently be joined during the build process. The term specimen, as used herein, refers in a general non-limiting sense to an unfinished or finished additive manufactured build component and/or structure during a manufacturing process. In the illustrated embodiment, the coupons 202, 204, 206 have the same size, shape, and orientation relative to the platform 102. In addition, the coupons 202, 204, 206 are formed using the same materials and the same parameters of the energy source 106. The samples 202, 204, 206 are enclosed within the powder bed 122. The only difference between the coupons 202, 204, 206 is the positioning of the coupons 202, 204, 206 relative to the AM tool 101 (e.g., energy source 106 and stage 102).

The positioning of the specimens 202, 204, 206 may refer to the position and orientation of the specimens 202, 204, 206 relative to the energy source 106. More specifically, positioning may refer to the position and orientation of each of a plurality of different sections or layers of the specimens 202, 204, 206 relative to the energy source 106. The positioning is characterized by the angle of incidence of each section of the specimen 202, 204, 206 with respect to the energy source 106, also referred to as the angle of incidence with respect to the normal of the part surface at the point of laser light.

The angle of incidence 208 is the angle between the beam line 210 and the line 212, the line 212 being perpendicular to the skin 214 (e.g., the surface normal vector), or to the beam line 210 and to the side of the respective section near the intersection of the beam line 210 and the section. The beam line 210 represents the path of a laser beam or other focused energy beam emitted or to be emitted from the energy source 106 to the top or surface layer 216 of the corresponding specimen section to create the surface layer 216. The surface layer 216 is the layer that is most recently formed at the top (e.g., end) of the layer stack at a given time. The skin 214 represents the side or edge of the layer or layers of the respective sample immediately below the surface layer 216 and immediately adjacent to the beam line 210. The line or vector 212 is perpendicular to the epidermis 214. If the skin 214 is curved (e.g., non-planar), the lines 212 may be perpendicular to the curved skin 214 at a location directly below the surface layer 216. The build member is three-dimensional, and thus the lines 212 of different skin segments of the same or different portions may have different vertical, lateral, and/or longitudinal or depth components relative to the energy source 106. As described herein, the angle of incidence 208 is based on the location (e.g., position and orientation) of a given section of the build component relative to the energy source 106. For example, the surface normal 212 is affected by the direction of the skin 214, while the beam line 210 is affected by the position of the section (e.g., the skin 214) relative to the energy source 106.

The three specimens 202, 204, 206 in fig. 2 have the same size and shape and the same orientation with respect to the platform 102. In the illustrated embodiment, the first, second, and third specimens 202, 204, 206 are overhanging (overhand) objects. The test specimens 202, 204, 206 each include a respective lower skin 218 generally facing the platform 102 and an upper skin 220 opposite the lower skin 218. The upper skin 220 generally faces upward away from the platform 102. The upper and lower skins 218, 220 of each sample 202, 204, 206 represent the inclined skin 214, so the normal 212 is perpendicular to the area or portion of the lower and upper skins 218, 220 near the surface layer 216.

The three samples 202, 204, 206 have different positions relative to the energy source 106, which are indicated by different angles of incidence 208. For example, the first specimen 202 defines a first angle of incidence 208A between a line 212A perpendicular to the respective underlying skin 218 and the first beam line 210A. The first specimen 202 defines a second angle of incidence 208B between a line 212B normal to the respective upper skin 220 and a second beam line 210B. The second specimen 204 defines a third incident angle 208C between a line 212C perpendicular to the respective lower skin 218 and a third beam line 210C. The second specimen 204 defines a fourth angle of incidence 208D between a line 212D normal to the respective upper skin 220 and a fourth beam line 210D. The third specimen 206 defines a fifth angle of incidence 208E between a line 212E normal to the respective lower skin 218 and a fifth beam line 210E. The third specimen 206 defines a sixth incident angle 208F between a line 212F normal to the respective upper skin 220 and a sixth beam line 210F. In the illustrated embodiment, the first, third, and sixth incident angles 208A, 208C, 208F are obtuse (e.g., greater than 90 degrees). The skin 214 associated with obtuse angles of incidence is referred to herein as the outer skin for reasons provided below. The second and fifth angles of incidence 208B, 208E are acute (e.g., less than 90 degrees). The epidermis 214 associated with acute angles of incidence is referred to herein as the inner epidermis. The fourth angle of incidence 208D is a right angle (e.g., 90 degrees). The skin 214 associated with the right angle incidence represents an inflection or inflection point between the outer and inner skins.

The orientation of the skin 214 of the specimens 202, 204, 206 relative to the platform 102 represents another geometric feature that may optionally be used to simulate and predict part quality prior to the build process. The orientation of each skin 214 relative to the platform 102 may refer to the angle of inclination defined between the surface normal of the skin 214 and the upper surface 110 of the platform 102 on which the test specimens 202, 204, 206 are constructed. Typically, the surface normal of the lower skin 218 faces downward toward the platform, while the surface normal of the upper skin 220 faces upward away from the platform. The lower skins 218 of the first, second and third test samples 202, 204, 206 each have the same orientation relative to the platform 102 in fig. 2, and the upper skins 202 of the test samples 202, 204, 206 also have the same orientation relative to the platform 102 in fig. 2.

Experimental testing has shown that the angle of incidence 208 between the beam line 210 and the line 212 perpendicular to the skin 214 can significantly affect the formation of the build part, such as surface quality, near-surface quality, porosity, and dimensional accuracy. For example, in an experimental setup similar to that shown in fig. 2, it was determined that even though all test parameters were the same, the outer skin (where the angle of incidence 208 is greater than a specified threshold angle) had significantly poorer performance (e.g., surface and near-surface quality, porosity, and dimensional accuracy) than the inner skin (where the angle of incidence 208 is less than the specified threshold angle). The specified threshold angle may be an angle defined from 70 degrees to 110 degrees, such as 70 degrees, 80 degrees, 90 degrees, 100 degrees, and the like. More specifically, the threshold angle may be an angle between 80 degrees and 100 degrees. In a non-limiting embodiment, the specified threshold angle is 90 degrees. When the specified threshold angle is 90 degrees, the obtuse angle of incidence is assigned to the outer epidermis and the acute angle of incidence is assigned to the inner epidermis. The outer skins shown in fig. 2 include a lower skin 218 of the first sample 202, a lower skin 218 of the second sample 204, and an upper skin 220 of the third sample 206. The inner skin shown in fig. 2 includes an upper skin 220 of the first sample 202 and a lower skin 218 of the third sample 206. These results indicate that some of the lower skin surfaces 218 may be inner skins (e.g., the lower skin 218 of the third sample 206), while other lower skin surfaces 218 may be outer skins (e.g., the lower skins 218 of the first and second samples 202, 204), which have reduced performance relative to the inner skins. Similarly, some of the upper skin surfaces 220 may be inner skins (e.g., the upper skin 220 of the first sample 202), while other upper skin surfaces 220 may be outer skins (e.g., the upper skin 220 of the third sample 206).

As suggested by the "Position dependence of Surface Roughness in Parts from Laser Beam Melting Systems" of s.kleszzynski, a.ladewig, k.friedberger, j.zur Jacobsm uhlen, d.merhof, g.witt (2015), proceedings of international solid amorphous processing (SFF), usa, page 360-. For example, when the surface layer 216 is formed along or near the outer skin surface (e.g., which defines an angle of incidence 208 greater than 90 degrees), some of the energy of the focused beam may be absorbed into the underlying powder within the powder bed 122, which affects the melt pool.

Fig. 3 shows a close-up portion of the first coupon 202 shown in fig. 2. The laser beam 226 impinges on the surface layer 216 proximate the lower skin 218 of the test specimen 202. As shown in fig. 2, the lower skin 218 is classified as an outer skin because the angle of incidence 208A between the laser beam 226 and a line 212A perpendicular to the lower skin 218 is greater than 90 degrees. The high energy laser beam 226 melts the source material to form a melt pool 228. The shape of the melt pool 228 may not be able to accurately accommodate the part dimensions, at least along the area near the outer skin. For example, the melt pool 228 in FIG. 3 penetrates a depth 231 that exceeds the desired lower skin edge 232 of the test specimen 202, such that the energy of the beam 226 is blown into the powder bed 122. As the material cools and solidifies, the energy absorbed by the powder may result in the formation of additional undesirable material, referred to herein as melt extensions 230, along the lower skin surface 218. The melt extension 230 may increase surface roughness (e.g., reduce surface quality), increase porosity, and increase dimensional error. Dimensional error refers to an increase in the thickness or lateral width of the lower skin 218 relative to the thickness/lateral width defined by the desired lower skin edge 232. Note that at least at the current time during the build process, the topmost layer(s), including the surface layer 216, may be dimensionally accurate. Laser penetration will result in growth of the previously formed layer below the topmost layer. For example, in FIG. 3, the melt pool 228 causes the melt extension 230 to grow along a layer 234 of two layers below the surface layer 216. As additional layers of material are formed, melt extensions 230 accumulate during the additive build process.

With continued reference to fig. 2, the angle of incidence 208B at the upper skin 220 of the first specimen 202 is substantially different from the angle of incidence 208A at the lower skin 218 of the first specimen 202. The angle of incidence 208B is acute, indicating that the upper epidermis 220 of the first sample 202 represents the inner epidermis. The inner skin may be associated with improved quality characteristics relative to the outer skin, such as surface quality, near surface quality, porosity, and dimensional accuracy. The change in mass may be attributed to the geometry of the build feature underlying the newly deposited surface layer 216. For example, energy from the laser beam 236 directed along the beam line 210B shown in fig. 2 may be absorbed by the partially solidified or consolidated underlying material of the first specimen 202, resulting in less energy being directed beyond the upper skin 220 boundary and into the powder bed 122 (as compared to the lower skin 218). Due to the angle of the beam 236 relative to the geometry of the specimen 202, the melt pool 238 formed by the laser beam 236 may not penetrate the boundary of the upper skin 220. For example, melt pool 238 extends at least partially inward toward the lateral center of specimen 202. In essence, the partially cured underlying material of the sample 202 is used to absorb more energy in the beam 236 than in the beam 226. As a result, the powder bed 122 along the upper skin 220 heats less than the powder bed 122 along the lower skin 218, and therefore the melt formed along the upper skin surface 220 has less elongation and other non-uniformities, thereby improving surface and near surface quality, dimensional accuracy, and porosity relative to the lower skin 218.

As shown in fig. 2, the angle of incidence 208D at the upper skin 220 of the second specimen 204 is a right angle, which means that the beam line 210D is collinear with the angle of the upper skin 220 directly beneath the layer of material being deposited or most recently deposited. The upper epidermis 220 of the second sample 204 may be in the inversion or inflection region between the inner and outer epidermis. For example, the inflection region may represent the range of angles between the inner and outer epidermis. The systems disclosed herein can treat the epidermis of the turning region in a different manner than the inner and outer epidermis. The inflection region may be in a range centered at an inflection point, such as, but not limited to, 90 degrees. For example, the inflection region can be between 70 degrees and 110 degrees, between 80 degrees and 100 degrees, and the like.

In an additive manufacturing process, in which layers of material are successively deposited in a stack according to a specified build feature geometry, the angle of incidence of a given skin of the build feature with respect to the beam emitter may vary over time. For example, fig. 4-6 illustrate three different stages of the AM tool 101 in the construction of a single coupon 240 over time, according to an embodiment. The stages are chronologically arranged so that the stage shown in fig. 4 precedes the stages shown in fig. 5 and 6, and the stage shown in fig. 5 precedes the stage shown in fig. 6. Fig. 4-6 illustrate the effect of a component built with the platform 102 that gradually descends (e.g., moves away from the energy source 106) as additional layers of material are deposited. The energy source 106 is disposed at the same location at each of the three illustrated stages of the build process such that the energy source 106 is not moved. Sample 240, representing the building element in fig. 4-6, has a diamond shape with parallel linear upper 242 and lower 244 skin surfaces.

The angle of incidence 246 with respect to the energy source 106 varies over time based on the geometry of the features on the upper skin 242. As described above, the associated angle of incidence 246 is defined between the beam line 248 from the energy source 106 and a line 250 perpendicular to the portion of the upper skin 242 proximate the current surface layer 252 of the test specimen 240. In fig. 4, the angle of incidence 246 is an obtuse angle (e.g., greater than 90 degrees), which indicates that the upper skin 242 has an outer skin classification. The quality and/or accuracy of the sections of the test specimen 240 formed on or near the top skin 242 may be reduced, requiring additional grinding processing steps after manufacture to increase smoothness and/or provide proper dimensional alignment.

Fig. 5 shows that the platform 102 has been moved after the stage shown in fig. 4 and an additional portion 254 of the specimen 240 has been formed. The additional portion 254 extends from the previous surface layer 252 to the current surface layer 256. In the illustrated stage, the angle of incidence 246 based on the upper skin 242 is a right angle, indicating that the upper skin 242 is at the point of inversion or inflection between the outer and inner skin classifications. Due to the difference in the incident angles 246, the section of the specimen 240 formed at or near the upper skin 242 at the surface layer 256 is expected to have better quality and/or accuracy than the upper skin 242 at the previous surface layer 252.

Fig. 6 shows that the stage 102 has been moved farther away from the stationary energy source 106 relative to that shown in fig. 5, and an additional portion 260 of the specimen 240 has been formed after the stage shown in fig. 5. The additional portion 260 extends from the previous surface layer 256 to the current surface layer 262. In the illustrated stage, the angle of incidence 246 based on the upper skin 242 is an acute angle (e.g., less than 90 degrees), which indicates that the upper skin 242 has an inner skin classification. Due to the difference in the incident angles 246, the section of the specimen 240 formed at or near the upper skin 242 at the surface layer 262 is expected to have better quality and/or accuracy than the upper skin 242 at the previous surface layers 256, 252. Fig. 4-6 show that as the diamond shaped sample 240 becomes higher and the position of the surface layer changes relative to the energy source 106, the upper skin 242 may transition from the outer skin to the inner skin, and vice versa. Thus, the multiple layers and design of the geometry of the build component are evaluated to determine the effect of the angle of incidence on the build component. Optionally, each layer of the build part geometry along the surface of the epidermis is evaluated to classify as outer epidermis, inner epidermis or turning point.

Fig. 7 shows an additive manufacturing tool 101 comprising a virtual build part 302 according to an embodiment. The virtual build component 302 is located at a candidate location 304 on the build platform 102. The virtual build component 302 represents internal functionality of the processor 118 of the control system 108 of the AM system 100 to predict surface quality of one or more sections of the build component prior to actual additive manufacturing of the build component. Optionally, the virtual build component 302 shown in FIG. 7 is for descriptive purposes and is not actually shown to the operator. Alternatively, a virtual build component 302 or similar representation is displayed on the display device 136 (shown in FIG. 1) to indicate the operating state of the processor 118 to an operator to predict component quality.

The size and shape of virtual build component 302 is based on component design file 138 (shown in FIG. 1). Processor 118 may access, for example, component design file 138 in memory 130 to determine the design of build component 302. In the example shown, the build member 302 has a hollow conical shape oriented about a central longitudinal axis 308. The design of the building element 302 may be based on a coordinate system, such as a spherical/polar coordinate system or a cartesian coordinate system with three orthogonal axes. For example, each point of build element 302 may have a corresponding location coordinate in a coordinate system.

The candidate location 304 of the building element 302 may be selected by an operator using the input device 134 or selected by the processor 118 as one of a plurality of candidate locations to be evaluated. The candidate locations 304 refer to the position and orientation of the build component 302 relative to the AM tool 101 (e.g., for the platform 102 and the electromagnetic energy source 106). In fig. 7, the candidate location 304 is offset relative to the electromagnetic energy source 106 such that the build member 302 is not centered below the energy source 106. The processor 118 may know the location of the energy source 106 and the platform 102. The candidate locations 304 may specify particular discrete locations on the build platform 102 and particular discrete orientations of the build component 302 relative to the platform 102. For example, the candidate position 304 may indicate that the center of the build member 302 is at a position (x, y, z) on the platform 102, and that the build member 302 is oriented such that the longitudinal axis 308 is orthogonal to the upper surface 110 of the platform 102. The orientation may also specify an angle of rotation of the build member 302 relative to the AM tool 101 about the longitudinal axis 308. For example, if the modeled build component includes a protrusion, the angle of rotation characterizes the direction of the protrusion with respect to the longitudinal axis 308. The AM tool 101 may define a tool coordinate system that represents the build volume. The tool coordinate system may be a sphere/pole and/or cartesian coordinate system. Candidate locations for the build part may be defined in the tool coordinate system. In an embodiment, processor 118 may determine candidate locations 304 by mapping the location coordinates of building element 302 into a tool coordinate system. For example, processor 118 may utilize a transfer function to convert the position coordinates of build component 302 from the build component's coordinate system to the tool coordinate system.

Upon determining (e.g., receiving, accessing, selecting, etc.) the candidate locations 304, the processor 118 is configured to determine and analyze the geometric features of the virtual build component 302 in the candidate locations 304 to simulate the quality of various sections of the build component prior to actually producing the build component. The building component 302 can be virtually divided (e.g., subdivided) into a plurality of sections. The segments may be of any size and shape. The height of each section may be as short as the thickness of one layer so that the build component may be inspected layer-by-layer. Alternatively, each zone height may represent multiple layers of material. In an embodiment, the section has a thickness or depth dimension such that the section comprises more material than the visible surface. For example, each section may have a specified depth, e.g., 0.5cm, 1.0cm, etc. Alternatively, the segments may have no thickness or depth dimension and represent only the surface of the building component. In the embodiment shown, the segments are triangles having a height representing multiple levels. For example, the processor 118 determines one or more geometric features of the first section 312 of the build member 302 and the second section 332 of the build member 302. In fig. 7, the first section 312 is shown along the right side of the building element 302 and the second section 332 is shown along the left side of the building element 302. The triangular first section 312 is defined by three corners 323, 324, and 325 having known location coordinates. The surface 310 of the first section 312 is defined between three corners 323, 324, 325. The surface 336 of the second section 332 is defined between three corners 333, 334, 335.

The geometric characteristics of the segments include an angle of incidence with respect to the electromagnetic energy source 106. For example, the first section 312 defines an angle of incidence 318 with respect to the energy source 106. The incident angle 318 is defined between a beam line 320 extending from the energy source 106 and a line 319 perpendicular to the side surface 310 (e.g., skin) of the segment 312. The angle of incidence 318 is greater than a specified threshold (e.g., 90 degrees), and thus the surface 310 of the first section 312 is classified as an outer skin surface. The second segment 332 defines an incident angle 321 with respect to the energy source 106, the incident angle being defined between the beam line 326 extending from the energy source 106 and a line 322 perpendicular to a surface 336 (e.g., the skin) of the segment 332. The angle of incidence 321 is less than a specified threshold (e.g., 90 degrees), and thus the surface 336 of the second segment 332 is classified as an inner skin surface. The processor 118 may determine a respective angle of incidence for each segmented section of the build member.

The geometric features of the segments optionally also include the angle of the respective surface relative to the upper surface 110 of the build platform 102, which indicates the inclination or tilt of the respective segment relative to horizontal. The angle may be based on a tangent or a plane of the surface. The tangent line may be determined by the processor 118 based on the location coordinates of points along the surface, such as the coordinates of the corners 323, 324, 325 of the first section 312.

In an embodiment, if the building component is constructed at a candidate location, the processor 118 predicts the quality of the segment using the determined geometric features of the segment (e.g., segments 312 and 332). The quality of a section may refer to the surface quality, sub-surface quality and/or the level of accuracy of the section relative to the build plan. The processor may generate a quality score for each section at the candidate location 304. With reference to the geometry of the angles of incidence, surfaces having angles of incidence (relative to the energy source) greater than a specified threshold angle have different quality scores than angles of incidence less than the specified threshold angle. For example, incidence angles above a specified threshold angle may generally, although not necessarily, be associated with poor quality compared to surfaces having incidence angles below the specified threshold. Thus, the outer skin surface is expected to have reduced precision, surface quality and/or subsurface quality relative to the inner skin surface. For example, the outer skin surface may be expected to have greater porosity and/or roughness than the inner skin surface. The quality score assigned to a segment represents the quality level predicted for each segment. In a simple embodiment, the quality score may be binary, such that the processor 118 assigns the segment having the inner skin surface to have good or satisfactory quality and assigns the segment having the outer skin surface to have degraded or unsatisfactory quality. In one or more other embodiments, the assignment of quality scores may be more dynamic. For example, the processor 118 may distinguish between two different surfaces based on the difference in the angle of incidence, even if both surfaces are classified as either inner or outer. A first segment having a determined angle of incidence of 40 deg. with respect to the energy source 106 may have a higher mass than a second segment having a determined angle of incidence of 70 deg.. For example, although the incident angles of both sections are less than 90 °, a first section with a smaller angle is expected to have reduced melt pool variation (e.g., less risk of forming a melt extension on the surface) and therefore is expected to have higher quality than the second section.

In an embodiment, the processor 118 may input one or more geometric features as variables into one or more functions to determine the quality score. Alternatively, the one or more functions may include other input variables that may affect surface quality, such as the type of powder or other raw material, build parameters (e.g., power, velocity, beam diameter, beam pass-through separation), settings (such as the direction of movement of the recoater arm in the AM tool 101, the direction of airflow through the AM tool 101), and so forth. One or more functions may be modeled based on simulation or prediction data. In an embodiment, the function may be derived from historical, experimental data testing the effect of different variables on surface quality. For example, various experiments may be performed in which multiple test specimens are additively manufactured, where the only difference between the test specimens is the location of the test specimens relative to the beam emitters of the additive tool. For example, different specimens in an experiment may have different angles of rotation about an axis, different angles of inclination relative to the platform, and/or different positions on the platform. By observing and recording the final surface quality, sub-surface quality and/or geometric accuracy of different samples, data relating mass to position (location) can be collected and stored in a database, such as a look-up table, mathematical model, etc. One or more functions may be derived based on the experimental data.

The quality score may be the output of one or more functions. As described herein, the surface quality score broadly represents a quantitative or qualitative measure of predicted part quality if the part is additively manufactured at the candidate location 304 according to simulated build parameters and settings. The surface quality score may be a quantitative value within a defined scale, such as a range from 1 to 10 (where 10 represents the optimal surface quality), from 1 to 100, and so forth. Alternatively, the range may be qualitative in nature, e.g., including some defined classifications. As noted above, in a non-limiting example, the classification may be binary, including "satisfactory" for the inner skin surface and "unsatisfactory" for the outer skin surface. In another example, the qualitative classification may include other categories such as "good," sufficient quality, "" poor quality, "and" poor quality. The quality score for each segment may include a plurality of sub-scores, e.g., different scores for coarseness, porosity, geometric accuracy, etc. One or more of the sub-factors may be more important than others for various reasons (e.g., the intended use of the build component), and thus generation of sub-scores may allow for selective optimization of higher weighted sub-factors. After determining the quality score for the segment, the processor 118 may store the quality score in the memory 130 such that the quality score is correlated or associated with the candidate location 304. As described above, the segments may represent various shapes and sizes, including various layers of material deposited on the build member. The segment sizes may be selected such that any inter-skin/outer-skin transition or surface quality score variation inside an individual segment is below the resolution of the additive manufacturing process.

The quality score is utilized to determine the position of the build component relative to selecting the position of the build component by another process to improve the quality of the build component additively manufactured by AM tool 101 (shown in fig. 1). In one or more embodiments, the processor 118 may calculate an overall location score associated with the candidate location 304. The overall location score is a metric based on a set of multiple quality scores for different sections of the build member 302 at the candidate location 304. For example, the processor 118 may aggregate the individual quality scores of the segments to calculate an overall location score. In a non-limiting example, the overall location score may be a sum of the individual quality scores, an average of the individual quality scores, or other statistical representation of a set of quality scores associated with the candidate location 304. Alternatively, the processor 118 may weight the quality scores of some sections more heavily than others. For example, the surface to be subsequently processed is less critical than a surface that must remain as deposited. Thus, the processor 118 may apply greater weight to the scores of the sections defining the surface that must maintain the deposition state. The weights may be adjusted by applying a weight correction to the scores. The processor 118 may store the overall location score in the memory 130 or another storage device.

The processor 118 may be configured to generate a simulation model of the build component 302 at the candidate location 304. FIG. 8 illustrates a simulation model 400 of the build component 302 at the candidate location 304, according to an embodiment. The simulation model 400 may be displayed as one or more images of the build member 302 with one or more graphical indicators 402 superimposed or layered on different sections of the build member 302. The simulation model 400 may be displayed on an output (e.g., display) device 136 to enable an operator of the AM system 100 to visualize information provided on the simulation model 400. Simulation model 400 may include an indicator 403 that represents the overall position of the electromagnetic energy source or beam emitter relative to build element 302. The simulation model 400 includes a first graphical indicator 402A superimposed on the surface 310 of the first section 312 and a second graphical indicator 402B superimposed on the surface 336 of the second section 332. Graphical indicator 402 represents (e.g., is based on) a quality score for the segment in which graphical indicator 402 is located. For example, a first graphical indicator 402A on the first section 312 represents a quality score generated for the first section 312 and a second graphical indicator 402B on the second section 332 represents a quality score generated for the second section 332.

The graphical indicators 402 displayed on the simulation model 400 may be colors, numbers, letters, words, shapes, symbols, and the like. In the illustrated embodiment, the graphical indicator 402 is an integer from 1 to 5. The integers represent different quality scores, with 5 representing the class with the highest prediction quality and 1 representing the class with the worst prediction quality relative to the other classes. For example, the first graphical indicator 402A is the number "1" and the second graphical indicator 402B is the number "4", which indicates that in the candidate position 304 of the building element 302, the surface 336 of the second segment 332 has a better expected quality than the surface 310 of the first segment 312. In another embodiment, the graphical indicator 402 may be color coded such that the high quality score is green, the low quality score is red, and so on.

The simulation model is displayed on a display device 136 for viewing by an operator of the AM system 100. Simulation model 400 may be displayed in two or three dimensions. In the example shown where simulation model 400 is three-dimensional, simulation model 400 may be rotated to view different selected portions of simulation model 400. For example, an operator may utilize input device 134 to rotate simulation model 400 to view a previously occluded surface. By viewing the simulation model 400, the operator may see the predicted quality of the different surfaces of the build component 302 in the candidate locations 304. For example, simulation model 400 provides regions that are predicted to have poor or unsatisfactory surface quality. Based on the information received from the simulation model 400, the operator may decide to adjust the position of the build component relative to the AM tool to improve the quality of the build component at the time of deposition. The simulation model may be incorporated into a virtual reality platform and/or an augmented reality platform.

In response to viewing the simulated image, the operator may utilize the input device 134 to modify the position of the build member 302 relative to the platform 102. For example, the operator may enter or select the second candidate location. The second candidate position may have a different position, a different rotational direction and/or a different tilt angle on the platform 102 than the (first) candidate position 304. FIG. 9 illustrates a simulation model 450 of the build component 302 at a second candidate location 452 according to an embodiment. As identified by indicator 403, the building element 302 at the second candidate position 452 is tilted towards the position of the electromagnetic energy source. For example, unlike the build member 302 in the candidate position 304 shown in FIG. 8, the longitudinal axis 308 of the build member 302 at the second candidate position 452 is not orthogonal to the upper surface 110 of the platform 102. Simulation model 450 may be generated and displayed on output device 136.

In an embodiment, upon determining (e.g., receiving, selecting, calculating, etc.) a second candidate location, processor 118 may repeat the above process to determine a second set of quality scores for the various sections of building block 302. The quality score may be determined based on geometric features, including angles of incidence with respect to the energy source. The processor 118 may determine an overall location score for the build component 302 at the second candidate location. A second simulation model 450 may be generated based on the determined quality score. The second simulation model 450 may be displayed simultaneously with the first simulation model 400 or, alternatively, continuously therewith to enable an operator to compare the two simulation models. The processor 118 may store the second candidate location, the second set of quality scores, the overall location score for the second candidate location, and details of the second simulation model in the memory 130 and/or another storage device.

The system described herein allows for the position of the build part relative to the AM tool to be optimized manually. For example, based on a comparison between information provided by the first and second simulation models (e.g., an overall location score), the operator may select one or more other candidate locations using input device 134. At each additional candidate location, the processor 118 is configured to repeat the analysis to generate a new simulation model and/or a new overall location score. The simulation model enables the operator to understand the projected relation between the positioning of the building element 302 and the predicted quality of the section of the building element 302. If the operator desires a particular surface region of a certain build component to have a certain threshold level of quality, the operator may adjust the position of the build component 302 until a candidate position is reached where the generated simulation model indicates that the predicted quality along the particular surface region meets the threshold. Similarly, the operator may continue to modify the candidate locations until the candidate locations are found to result in an overall location score greater than the desired score threshold.

The system described herein can provide for automatic optimization of the position of a build component relative to an AM tool. For example, the processor 118 may generate the recommended position of the build component based on an analysis of the plurality of candidate positions without utilizing operator input. The processor 118 may perform an analysis on the initial set of candidate locations to determine a simulation model and/or an overall location score for each candidate location in the initial set. The processor 118 may then select one or more preferred candidate locations from the initial set that have better results (e.g., higher overall location scores) than other candidate locations. Alternatively, the processor 118 may generate additional candidate locations based on the results of the initial analysis, and at least some preferred candidate locations may be generated by the processor 118. The processor 118 may select the best preferred candidate location as the recommended location for use in the actual build process. Alternatively, the processor 118 may present the operator with preferred candidate locations, such as the first three candidate locations, to enable the operator to decide which preferred candidate location to select for the build process.

The operator may participate in the automatic candidate location determination process. For example, the operator may specify particular sections of the build part that require higher quality and/or particular sections that do not require any quality optimization. Operator input may be represented by adjusting weights associated with different portions of the build component. For example, the weights associated with segments that require higher quality may be increased, and the weights associated with segments that do not require quality optimization may be decreased. The operator may also use the input device to specify a desired position/orientation range. For example, an operator may wish to avoid large melt areas (to prevent the build-up of residual stresses in the component) and/or may wish to locate a particular surface within a particular position and/or orientation window. The processor 118 may analyze the predicted quality of the build component at the different candidate locations based on these operator-entered constraints or parameters.

In an embodiment, the processor 118 may select the recommended location based on an overall location score of the candidate locations that have been analyzed and stored. For example, processor 118 may select the candidate location associated with the highest overall location score of the stored overall location scores associated with a particular build component 302 as the recommended location. In another embodiment, processor 118 may function as a solver algorithm to essentially "solve" for the location of build member 302 that provides the greatest possible surface quality according to the scoring system disclosed herein. For example, the processor 118 may perform multiple calculations by changing different variables to focus on a single candidate location (predicted to provide improved surface quality over at least some other potential locations of the build component on the platform). In a non-limiting example, the processor 118 may iteratively change one variable at a time to establish a large set of different candidate locations, and may then systematically generate a respective overall location score for each candidate location in the set. In another non-limiting example, the processor 118 may "intelligently" select candidate locations to test based on learned trends (e.g., a trend that a surface tilted toward the energy source 301 would be expected to have better surface quality than a surface tilted away from the energy source 301).

In the illustrated embodiment, the system predicts that a build part built at the second candidate location 452 will yield a better overall build part quality than a build part built at the first candidate location 304 in fig. 7 and 8. For example, in general, graphical indicators 402 overlaid or superimposed on different sections of build component 302 in simulation model 450 have a higher score or number than the aggregate graphical indicators 402 of simulation model 400. Tilting the build member 302 toward the energy source is expected to improve the overall surface quality of the surface by making the surface more uniform over the entire perimeter of the build member. For example, tilting the building elements 302 reduces some of the incident angles of the lines extending from the energy source to the surface of the segment. For example, graphical indicator 402A on first section 312 is a "2" in simulation model 450, which represents an improvement over the "1" shown in simulation model 400. The increased quality score may be due, at least in part, to a decrease in the angle of incidence between the skin or surface 310 of the first section 312 and a line extending from the energy source. While the overall quality score may be improved, tilting the building elements may result in increased incidence angles for certain segments, which may reduce the individual quality scores for those segments. For example, the quality score graphical indicator 402B of the second segment 332 has decreased from "4" in the simulation model 400 to "3" in the simulation model 450, which may be based in part on the increased angle of incidence.

Alternatively, different candidate positions may cause the build member 302 to have different positions on the platform 102 in addition to or instead of adjusting the orientation of the build member 302. For example, the processor 118 may recommend a candidate location that is closer to the energy source than another candidate location. Bringing the building element closer to the energy source may improve the quality of the individual segments of the building element due to the change of the angle of incidence of the energy beam from the energy source impinging on the building element.

The operator may select or confirm the final position of the building element 302 using the input device 134. The final position may be a preferred position generated by the processor 118 or may be a position selected by the operator based on a simulation model, an overall position score, and/or other information provided by the system to the operator. Once the location is determined, the processor 118 may generate a build plan 132 (shown in FIG. 1) based on the final location of the build component. The AM tool 101 may then be controlled to additive-manufacture the physical build component according to the build plan 132 such that the build component is built with a position and orientation that matches the final position.

System 100 may also be used to plan the positioning of multiple build components to be manufactured simultaneously on the same platform in a single additive manufacturing component process. Fig. 10 illustrates an array 500 of multiple build components arranged at different locations on the build platform 102 of the AM tool 101, according to an embodiment. The building elements include a first element 502 at a first location 503, a second element 504 at a second location 505, a third element 506 at a third location 507, and a fourth element 508 at a fourth location 509. In an embodiment, the positions 503, 505, 507, 509 may be fixed (e.g., set), and the processor 118 performs a quality analysis on various candidate positions of the building elements 502, 504, 506, 508 by modifying the tilt and rotation of the building elements 502, 504, 506, 508 to define different candidate positions. Processor 118 may determine one or more preferred candidate locations for each building element 502, 504, 506, 508 based on the analysis. For example, a preferred candidate position may involve tilting of the building elements 502, 504, 506, 508 towards the position of the energy source 106, similar to the tilting of the element 302 shown in fig. 9.

The processor 118 may be configured to determine whether any of the building elements 502, 504, 506, 508 are unable to meet a minimum quality threshold. The minimum quality threshold may be specified based on criteria or usage requirements, or may be selected by operator input. For example, processor 118 may compare the overall quality score for each preferred candidate location of the plurality of building elements 502, 504, 506, 508 to a minimum quality threshold. In a non-limiting example, the third building element 506 may be the only building element in the array 500 that fails to meet the minimum quality threshold. For example, none of the candidate locations of the third building element 506 results in an overall quality score meeting or exceeding a minimum quality threshold. As a result, the processor 118 may provide notification to the operator: the third building element 506 should not be built at the setting position 507. The notification may be provided via the output device 106, such as a displayed message. In response, the processor 118 may determine another location of the build component 506 on the platform 102 for evaluation, and/or the operator may decide to initiate a build process to build only the first, second, and fourth components 502, 504, 508.

In another embodiment, the processor 118 may determine the position of the building elements 502, 504, 506, 508 on the platform 102 in addition to the rotation and tilt of the building elements 502, 504, 506, 508. For example, the processor 118 may arrange the build components 502, 504, 506, 508 in a polar array on the platform 102, wherein the build components 502, 504, 506, 508 surround the energy source 106 and are tilted towards the energy source 106.

Fig. 11 is a flow diagram of a method 600 of simulating surface quality of an additively manufactured build component in accordance with an embodiment of the present disclosure. The method 600 is configured to predict surface quality of various sections of a build component prior to additive manufacturing of the build component. The predicted surface quality may enable selective positioning of the build component during an actual additive build process to improve the surface quality of the manufactured build component relative to the surface quality that would be achieved at different locations of the build component within the manufacturing tool. The method 600 may be performed in whole or at least in part by one or more processors 118 of the control unit 108 of the AM system 100 shown in fig. 1. Optionally, some operator input may be provided in one or more steps. Alternatively, method 600 may include more steps than shown in FIG. 11, fewer steps than shown in FIG. 11, different steps not shown in FIG. 11, and/or a different arrangement or order of steps than shown in FIG. 11.

The method 600 begins at 602 with determining one or more geometric features of each of a plurality of sections of a build component at candidate positions of the build component relative to a platform. The one or more geometric features include an angle of incidence between a beam line extending from an electromagnetic energy source of the additive manufacturing tool and a surface normal of the respective skin proximate the respective section of the beam line.

At 604, a quality score for each section of the build component at the candidate location is determined based on the one or more geometric features. A section of a build feature with a lower angle of incidence may have a higher quality score than a section of a build feature with a larger angle of incidence. At 606, a simulation model of the build component at the candidate location is generated. The simulated image includes a graphical indicator corresponding to each section. The graphical indicator represents a quality score for the respective segment. At 608, the simulation model is displayed on a display device for viewing by an operator. At 610, the quality scores for the sections of the build part at the candidate locations are aggregated to calculate an overall location score for the candidate locations.

At 612, the previous steps (e.g., 602, 604, 606, 608, and 610) are repeated at least once at other candidate positions of the build part relative to the platform. After a number of cycles, the method may have data including segment quality scores, simulation models, and overall position scores for a number of candidate positions of the build component. At 614, at least one preferred position of the build part relative to the platform is determined based on the data at the different candidate positions. The preferred position may be determined to achieve improved build part quality relative to additive manufacturing of the build part at a non-preferred candidate position, such as the initial candidate position. At 616, the additive manufacturing tool is controlled to additive manufacture the build component at one of the preferred locations on the platform. The build component may automatically select a preferred location in which to additively manufacture the build component based on the overall quality score or another metric. Alternatively, the operator may select a preferred location for additive manufacturing of the build component from a plurality of preferred locations provided as options to the operator.

One or more embodiments described herein use information about the shape of a build component and the position of the build component within a build envelope of an additive manufacturing tool to predict the surface quality of various sections of the build component prior to actually forming the physical build component. For example, embodiments described herein may simulate component surface roughness for selecting an optimal or preferred component orientation and position within a machine build enclosure. The generated simulation data may be used in a layout optimization algorithm or a solver algorithm to provide a proposed position of the build component in the machine build enclosure.

Although various spatial and directional terms, such as top, bottom, lower, medial, lateral, horizontal, vertical, front, etc., are used to describe embodiments of the present disclosure, it should be understood that these terms are used only with respect to the orientations shown in the drawings. The orientation may be reversed, rotated or otherwise changed such that the upper portion is the lower portion and vice versa, horizontal to vertical, etc.

The figures of embodiments herein illustrate one or more control or processing units, such as the control unit 108 shown in fig. 1. It will be appreciated that a control or processing unit represents circuitry, or a portion thereof, implemented as hardware with associated instructions (e.g., software stored on a tangible and non-transitory computer readable storage medium such as a computer hard drive, ROM, RAM, etc.) to perform the operations described herein. The hardware may include state machine circuitry that is hardwired to perform the functions described herein. The hardware may include electronic circuitry that includes and/or is coupled to one or more logic-based devices, such as microprocessors, processors, controllers, and the like. Alternatively, the control unit 108 or one or more processors 118 thereof represent one or more processing circuits such as Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), microprocessors, quantum computing devices, and the like. The circuitry in various embodiments is configured to execute one or more algorithms to perform the functions described herein. The one or more algorithms include aspects of the embodiments disclosed herein, whether or not explicitly identified in a flowchart or a method.

As used herein, the term "control unit" and the like includes any processor-based or microprocessor-based system including systems using microcontrollers, Reduced Instruction Set Computers (RISC), Application Specific Integrated Circuits (ASICs), logic circuits, and any other circuit or processor (including hardware, software, or combinations thereof) capable of executing the functions described herein. This is exemplary only, and thus is not intended to limit the definition and/or meaning of these terms in any way. The control unit 108 shown in fig. 1 is configured to execute a set of instructions stored in one or more storage elements, such as one or more memories, in order to process data. The set of instructions includes various commands that instruct the control unit 108 (e.g., its processor 118) as a processing machine to perform specific operations, such as the methods and processes of the various embodiments of the subject matter described herein. In an embodiment, the set of instructions is in the form of a software program. The processing of input data by a processing machine is in response to a user command, in response to the results of a previous processing or in response to a request made by another processing machine. As used herein, the term "software" includes any computer program stored in memory for execution by a computer, including, but not limited to, RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory.

As used herein, a structure, limitation, or element that is "configured to" perform a task or operation is structurally formed, configured, or adapted in a manner that corresponds to the task or operation, in particular. For the sake of clarity and avoidance of doubt, an object that can only be modified to perform a task or operation is not "configured to" perform the task or operation as used herein.

It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-described embodiments (and/or aspects thereof) may be used in combination with each other. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the various embodiments of the disclosure without departing from the scope thereof. While the dimensions and types of materials described herein are intended to define the parameters of the various embodiments of the disclosure, these embodiments are by no means limiting and are exemplary embodiments. Many other embodiments will be apparent to those of ordinary skill in the art upon reviewing the above description. The scope of various embodiments of the disclosure should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms "including" and "in which" are used as the plain-english equivalents of the respective terms "comprising" and "wherein". Furthermore, the terms "first," "second," and "third," etc. are used merely as labels, and are not intended to impose numerical requirements on their objects. Furthermore, unless and until such claim limitations explicitly use the phrase "means for … …," followed by a functional statement without further structure, the limitations of the following claims are not written in the form of "function plus function" and are not intended to be interpreted according to the provisions of section 112(f), volume 35, U.S. code.

This written description uses examples to disclose various embodiments of the disclosure, including the best mode, and also to enable any person skilled in the art to practice various embodiments of the disclosure, including making and using any devices or systems and performing any incorporated methods. The patentable scope of various embodiments of the disclosure is defined by the claims, and may include other examples that occur to those of ordinary skill in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.