阅读说明：本技术 一种基于递推最小二乘法的铣削力在线识别方法及系统 (Online milling force identification method and system based on recursive least square method ) 是由 曹宏瑞 厉琦 史江海 李登辉 陈雪峰 于 2021-07-13 设计创作，主要内容包括：本发明公开了一种基于递推最小二乘法的铣削力在线识别方法及系统,在机床坐标系下通过多次模态测试取平均值的方法获得X、Y方向上的频响函数,并将频响函数转换成时域上的单位脉冲响应函数；通过在X、Y合适的位置布置加速度传感器,获取铣削加工过程中加速度响应；根据铣削加工过程中的转速,选择对应的刀齿通过频率及其倍频对加速度信号进行梳状滤波处理；利用获得的单位脉冲响应函数,采用递推最小二乘法配合滑动矩形窗对滤波后的加速度信号进行处理,识别出铣削力。本发明采用递推最小二乘法并结合矩形窗,对输入加速度信号进行不断的更新,以保证识别切削力的实时性,为铣削力的在线识别提供了新思路。(The invention discloses a milling force online identification method and a milling force online identification system based on a recursive least square method.A frequency response function in the X, Y direction is obtained by a method of averaging through multiple modal tests in a machine tool coordinate system, and the frequency response function is converted into a unit impulse response function in the time domain; acceleration response in the milling process is obtained by arranging an acceleration sensor at an appropriate position X, Y; according to the rotating speed in the milling process, selecting corresponding cutter teeth to perform comb filtering processing on the acceleration signals through frequency and frequency multiplication of the cutter teeth; and processing the filtered acceleration signal by using the obtained unit impulse response function and adopting a recursive least square method in combination with a sliding rectangular window to identify the milling force. The invention adopts a recursive least square method and combines a rectangular window to continuously update the input acceleration signal so as to ensure the real-time property of cutting force identification and provide a new idea for the online identification of the milling force.)

1. A milling force online identification method based on a recursive least square method is characterized by comprising the following steps:

s1, respectively carrying out a force hammer knocking experiment along the feed direction X, Y under a milling machine coordinate system to obtain a plurality of groups of acceleration frequency response functions, and averaging frequency responses in each direction to obtain a cross-point acceleration frequency response function;

s2, milling, selecting sampling frequency, measuring an acceleration signal in the feed direction of X, Y in the machining process, and performing filtering pretreatment on the acceleration signal;

and S3, performing online identification on the milling force by using the cross-point acceleration frequency response function obtained in the step S1 and the milling process acceleration signal subjected to filtering pretreatment in the step S2 and adopting a recursive least square method.

2. The method according to claim 1, wherein step S1 is specifically:

s101, respectively installing an acceleration sensor close to a front end bearing in the direction of a main spindle box X, Y;

s102, knocking the cutter point of the milling cutter by the force hammer along the direction X, Y respectively to obtain a cross-point acceleration frequency response function from the cutter point to the main spindle box body.

3. The method according to claim 2, wherein in step S102, the cross-point acceleration frequency response function from the tool tip to the main headstock body represents the relationship between the milling force at the tool tip and the acceleration response at the headstock body, specifically:

wherein H (w) is an acceleration frequency response function, X (w) is an acceleration response at the box body, and F (w) is the milling force at the tool nose.

4. The method according to claim 1, wherein step S2 is specifically:

keeping the position of the acceleration sensor consistent with the mounting position finally determined at step S1; determining processing parameters according to actual process requirements and a milling stability lobe graph; acquiring an acceleration signal in the milling process; and calculating the passing frequency of the cutter teeth according to the rotating speed of the main shaft, performing comb filtering on the acceleration signal according to the passing frequency of the cutter teeth, reserving the first 3-5 times of the passing frequency of the cutter teeth, and acquiring the filtered acceleration signal.

5. The method of claim 4, wherein the tooth pass frequency TPF is calculated as follows:

wherein omega is the rotating speed rpm of the main shaft, and N is the number of teeth of the milling cutter.

6. The method according to claim 1, wherein step S3 is specifically:

s301, the cross-point acceleration frequency response function H (w) obtained in the step S1_xx、H(w)_xy、H(w)_yx、H(w)_yyConversion to unit impulse response function h (t) in time domain_xx、h(t)_xy、h(t)_yx、h(t)_yy；

S302, constructing a least square measurement model according to the discretization Duhami integration by using the unit impulse response function in the step S301, and expanding the least square measurement model to the direction of X, Y for deformation;

s303, selecting the data length, performing recursive least square calculation according to the least square measurement model in the step S302, respectively calculating a gain matrix, and performing state recursive and variance matrix recursive;

s304, identifying the milling force in the direction of X, Y from an identification state vectorSeparating to obtain X, Y direction identification milling force;

and S305, introducing a rectangular window to cover the previous section of invalid data, receiving real-time acceleration data, and updating the input signal in real time.

7. The method of claim 6, wherein the step S302 of expanding the least squares metric model to X, Y direction and deforming comprises:

wherein x is_n、y_nX, Y direction nth time acceleration response respectively; h is_xxn、h_xyn、h_yxnAnd h_yynRespectively, unit impulse response function h (t)_xx、h(t)_xy、h(t)_yxAnd h (t)_yyValue at time n, f_xn、f_ynRespectively X, Y direction at the nth time.

8. The method of claim 6, wherein in step S303, the gain matrix is:

the state recursion is:

the variance matrix recursion is:

P_k＝(I-K_kH_k)P_k-1

wherein the content of the first and second substances,representing the identified milling force, k representing the current time; z_kRepresenting an acceleration signal, R_kTo measure the noise matrix, K_kAs a gain matrix, P_kIs a variance matrix, H_KIs the unit impulse response matrix at time k.

9. The method of claim 6, wherein in step S305, the method is performed according to an impulse response function h (t)_xx、h(t)_xy、h(t)_yx、h(t)_yyThe time when the attenuation is close to zero is determined The magnitude of the medium k value; at time k +1, the state recurrence formula is:

the time k +1 being used for updatingThe method specifically comprises the following steps:

wherein Z is_k+1＝[x_k+1，y_k+1]^TAcceleration signal at time K +1, K_k+1The gain matrix at time k + 1.

10. A milling force on-line identification system based on a recursive least square method is characterized by comprising the following steps:

the function module is used for performing a force hammer knocking experiment along the X, Y feeding direction respectively under a milling machine coordinate system to obtain a plurality of groups of acceleration frequency response functions, and averaging the frequency response in each direction to obtain a cross-point acceleration frequency response function;

the filtering module is used for milling, selecting sampling frequency, measuring an acceleration signal in the X, Y feeding direction in the processing process and carrying out filtering pretreatment on the acceleration signal;

and the identification module is used for identifying the milling force by using a cross-point acceleration frequency response function acquired by the function module and a milling process acceleration signal subjected to filtering pretreatment by the filtering module and adopting a recursive least square method.

Technical Field

The invention belongs to the technical field of machine tool milling, and particularly relates to a milling force online identification method and system based on a recursive least square method.

Background

The perception and monitoring of the machine tool machining state are important ways for realizing the intellectualization of the machining process and guaranteeing the machining quality. The milling force is an important parameter in the machining process of the machine tool, and has important reference values for monitoring the machining process, controlling the abrasion state and vibration of the cutter and the performance of the machine tool. Therefore, the method has important significance for identifying and researching the milling force in the machining process.

The milling force measuring and identifying method is mainly divided into the following methods: direct measurement methods based on force sensors; a prediction method based on a milling force model; a displacement signal based identification method; identification method based on current signal. A great deal of research has been carried out by many domestic and foreign researchers, such as displacement sensors and tool stiffness calculation methods for milling force identification at great sensitivity of northwest university of industry (Wan M, Yuan H, Feng J, Zhang W H & Yin W. industry-oriented method for measuring the cutting for use on the deflection of tool width [ J ]. International Journal of Mechanical Sciences,2017,130: 315-. Wang Chenxi of the university of Western-Ann traffic performs Milling Force Identification by combining an Acceleration sensor with a conjugate gradient least square method (Wang CX, Zhang XW, Qiao BJ, Cao HR & Chen XF. dynamic Force Identification in Peripheral Milling Based on CGLS Using Filtered Access Signals and average transferred Functions [ J ]. Journal of Manufacturing Science and Engineering,2019,141(6),064501.), however, the method needs to intercept a time response signal for Identification and cannot be extrapolated over time.

Through literature research, the existing method cannot simultaneously guarantee the identification precision, reduce the equipment cost, improve the frequency domain bandwidth, and realize the requirements of online identification and the like.

Disclosure of Invention

The invention aims to solve the technical problem of providing a milling force online identification method and system based on a recursive least square method aiming at the defects in the prior art, wherein an acceleration signal is adopted to identify the milling force, and reference is provided for solving the defects of the prior milling force monitoring.

The invention adopts the following technical scheme:

a milling force online identification method based on a recursive least square method comprises the following steps:

s1, respectively carrying out a force hammer knocking experiment along the feed direction X, Y under a milling machine coordinate system to obtain a plurality of groups of acceleration frequency response functions, and averaging frequency responses in each direction to obtain a cross-point acceleration frequency response function;

s2, milling, selecting sampling frequency, measuring an acceleration signal in the feed direction of X, Y in the machining process, and performing filtering pretreatment on the acceleration signal;

and S3, performing online identification on the milling force by using the cross-point acceleration frequency response function obtained in the step S1 and the milling process acceleration signal subjected to filtering pretreatment in the step S2 and adopting a recursive least square method.

Specifically, step S1 specifically includes:

s101, respectively installing an acceleration sensor close to a front end bearing in the direction of a main spindle box X, Y;

s102, knocking the cutter point of the milling cutter by the force hammer along the direction X, Y respectively to obtain a cross-point acceleration frequency response function from the cutter point to the main spindle box body.

Further, in step S102, the cross-point acceleration frequency response function from the tool nose to the main spindle box represents a relationship between the milling force at the tool nose and the acceleration response at the box, specifically:

wherein H (w) is an acceleration frequency response function, X (w) is an acceleration response at the box body, and F (w) is the milling force at the tool nose.

Specifically, step S2 specifically includes:

keeping the position of the acceleration sensor consistent with the mounting position finally determined at step S1; determining processing parameters according to actual process requirements and a milling stability lobe graph; acquiring an acceleration signal in the milling process; and calculating the passing frequency of the cutter teeth according to the rotating speed of the main shaft, performing comb filtering on the acceleration signal according to the passing frequency of the cutter teeth, reserving the first 3-5 times of the passing frequency of the cutter teeth, and acquiring the filtered acceleration signal.

Further, the tooth pass frequency TPF is calculated as follows:

wherein omega is the rotating speed rpm of the main shaft, and N is the number of teeth of the milling cutter.

Specifically, step S3 specifically includes:

s301, the cross-point acceleration frequency response function H (w) obtained in the step S1_xx、H(w)_xy、H(w)_yx、H(w)_yyConversion to unit impulse response function h (t) in time domain_xx、h(t)_xy、h(t)_yx、h(t)_yy；

S302, constructing a least square measurement model according to the discretization Duhami integration by using the unit impulse response function in the step S301, and expanding the least square measurement model to the direction of X, Y for deformation;

s303, selecting the data length, performing recursive least square calculation according to the least square measurement model in the step S302, respectively calculating a gain matrix, and performing state recursive and variance matrix recursive;

s304, identifying the milling force in the direction of X, Y from an identification state vectorSeparating to obtain X, Y direction identification milling force;

and S305, introducing a rectangular window to cover the previous section of invalid data, receiving real-time acceleration data, and updating the input signal in real time.

Further, in step S302, the expanding the least squares measurement model to the X, Y direction and performing deformation specifically includes:

wherein x is_n、y_nX, Y direction nth time acceleration response respectively; h is_xxn、h_xyn、h_yxnAnd h_yynRespectively, unit impulse response function h (t)_xx、h(t)_xy、h(t)_yxAnd h (t)_yyValue at time n, f_xn、f_ynRespectively X, Y direction at the nth time.

Further, in step S303, the gain matrix is:

the state recursion is:

the variance matrix recursion is:

P_k＝(I-K_kH_k)P_k-1

wherein the content of the first and second substances,representing the identified milling force, k representing the current time; z_kRepresenting an acceleration signal, R_kTo measure the noise matrix, K_kAs a gain matrix, P_kIs a variance matrix, H_KIs the unit impulse response matrix at time k.

Further, in step S305, according to the impulse response function h (t)_xx、h(t)_xy、h(t)_yx、h(t)_yyThe time when the attenuation is close to zero is determinedThe magnitude of the medium k value; at time k +1, the state recurrence formula is:

the time k +1 being used for updatingThe method specifically comprises the following steps:

wherein Z is_k+1＝[x_k+1，y_k+1]^TAcceleration signal at time K +1, K_k+1The gain matrix at time k + 1.

Another technical solution of the present invention is a milling force online identification system based on a recursive least square method, comprising:

the function module is used for performing a force hammer knocking experiment along the X, Y feeding direction respectively under a milling machine coordinate system to obtain a plurality of groups of acceleration frequency response functions, and averaging the frequency response in each direction to obtain a cross-point acceleration frequency response function;

the filtering module is used for milling, selecting sampling frequency, measuring an acceleration signal in the X, Y feeding direction in the processing process and carrying out filtering pretreatment on the acceleration signal;

and the identification module is used for identifying the milling force by using a cross-point acceleration frequency response function acquired by the function module and a milling process acceleration signal subjected to filtering pretreatment by the filtering module and adopting a recursive least square method.

Compared with the prior art, the invention has at least the following beneficial effects:

the invention relates to a milling force online identification method based on a recursive least square method, which comprises the steps of respectively carrying out a force hammer knocking experiment along the X, Y feeding direction under a milling machine coordinate system to obtain a plurality of groups of acceleration frequency response functions, and averaging frequency responses in each direction to obtain the acceleration frequency response functions; milling, selecting sampling frequency, measuring an acceleration signal in the X, Y feeding direction in the processing process, and performing filtering pretreatment on the acceleration signal, wherein the interference of noise components in the acceleration signal can be eliminated through the filtering treatment, so that the identified milling force is more accurate; the milling force is identified on line by using an acceleration frequency response function and a milling process acceleration signal after filtering pretreatment and adopting a recursive least square method, the obtained frequency response can be more accurate by averaging the acceleration frequency response function obtained by a plurality of modal experiments, the influence of random errors in the frequency response function obtaining process is reduced, and the accuracy of the milling force identification result is ensured.

Furthermore, the acceleration sensor is arranged near the position of the front bearing of the main shaft, so that the vibration energy attenuation is minimum, and the influence of random errors is reduced. The cutting force transmits vibration to the housing through the spindle support bearing, which minimizes the entire transmission path of the cutting force and minimizes energy attenuation.

Furthermore, the machining parameters are determined by analyzing the lobe pattern before cutting, so that the generation of flutter can be avoided, the milling stability is improved, the influence of nonlinear factors is reduced, and the identification accuracy is ensured.

Furthermore, comb filtering preprocessing is carried out on the acquired acceleration signals, so that the influence of other frequency noises on the identification process can be reduced, and the accuracy of milling force identification is improved.

Furthermore, by calculating the purpose or the benefit of the cutter tooth setting through the frequency TPF, and giving a description of principle analysis, the main distribution frequency of the milling force can be determined, and meanwhile, a reference frequency is provided for comb filtering.

Furthermore, the frequency-domain frequency response function is converted into the time-domain impulse response function, so that the whole identification process can be mapped to the time domain, and an identification model is provided for online identification.

Further, by extending the least square model to X, Y two directions, the accuracy of identification is guaranteed. Because the milling forces in the direction X, Y are coupled to each other during milling and are not independent of each other.

Furthermore, the whole model is reordered according to the time sequence through the deformation of the least square measurement model, the original mode of ordering according to the direction is changed, the real-time updating and identification of data can be guaranteed, and the basis of online identification is realized.

In conclusion, the method adopts a recursive least square method and combines a rectangular window to continuously update the input acceleration signal so as to ensure the real-time property of cutting force identification and provide a new idea for the online identification of the milling force; the acceleration signal acquisition is low in cost and easy to obtain, and can reflect each frequency spectrum component of the cutting force.

The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.

Drawings

FIG. 1 is a schematic view of the machine tool X, Y;

FIG. 2 is a schematic diagram of the installation positions of the hammer strike and the acceleration sensor;

FIG. 3 is a flow chart of a milling force online identification method based on a recursive least square method;

FIG. 4 is a graph of simulated frequency responses in various directions for verifying the proposed milling force recognition algorithm of the present invention, wherein (a) is the frequency response in the x-x direction, (b) is the frequency response in the x-y direction, (c) is the frequency response in the y-x direction, and (d) is the frequency response in the y-y direction;

FIG. 5 is a graph of simulated milling force in the X direction versus identified milling force as used in the present invention;

fig. 6 is a graph of simulated milling forces in the Y-direction versus identified milling forces used in the present invention.

Wherein, 1, an acceleration sensor; 2. and (5) a hammer.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.

It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well,

it should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

Various structural schematics according to the disclosed embodiments of the invention are shown in the drawings. The figures are not drawn to scale, wherein certain details are exaggerated and possibly omitted for clarity of presentation. The shapes of various regions, layers and their relative sizes and positional relationships shown in the drawings are merely exemplary, and deviations may occur in practice due to manufacturing tolerances or technical limitations, and a person skilled in the art may additionally design regions/layers having different shapes, sizes, relative positions, according to actual needs.

The invention provides a milling force online identification method based on a recursive least square method, which is characterized in that a frequency response function in the X, Y direction is obtained by a method of averaging through multiple modal tests in a machine tool coordinate system, and the frequency response function is converted into a unit impulse response function in the time domain; acceleration response in the milling process is obtained by arranging an acceleration sensor at an appropriate position X, Y; according to the rotating speed in the milling process, selecting corresponding cutter teeth to perform comb filtering processing on the acceleration signals through frequency and frequency multiplication of the cutter teeth; and processing the filtered acceleration signal by using the obtained unit impulse response function and adopting a recursive least square method in combination with a sliding rectangular window to identify the milling force. The invention adopts a recursive least square method and combines a rectangular window to continuously update the input acceleration signal so as to ensure the real-time property of cutting force identification and provide a new idea for the online identification of the milling force.

The invention relates to a milling force online identification method based on a recursive least square method, which comprises the following steps:

s1, respectively carrying out a force hammer tapping experiment along the feed direction X, Y under the milling machine coordinate system shown in the figures 1 and 2, carrying out multiple tapping in each direction to obtain multiple groups of cross-point acceleration frequency response functions, and averaging frequency response in each direction to obtain more accurate cross-point acceleration frequency response functions;

s101, respectively installing an acceleration sensor 1 at a position close to a front end bearing in the direction of a main spindle box X, Y;

s102, knocking the cutter point of the milling cutter by the force hammer 2 along the direction X, Y respectively to obtain a cross-point acceleration frequency response function from the cutter point to the main spindle box body.

The cross-point acceleration frequency response function from the tool nose to the main spindle box represents the relation between the milling force at the tool nose and the acceleration response at the box body, and specifically comprises the following steps:

wherein H (w) is an acceleration frequency response function, X (w) is an acceleration response at the box body, and F (w) is the milling force at the tool nose.

S2, setting reasonable parameters for milling, selecting proper sampling frequency, measuring an acceleration signal in the feed direction of X, Y in the processing process, and performing filtering pretreatment on the acceleration signal;

s201, keeping the position of the acceleration sensor consistent with the mounting position finally determined in S103 so as to ensure the consistency of the transmission path;

s202, determining appropriate processing parameters according to actual process requirements and a milling stability lobe graph so as to avoid the influence of nonlinear effects such as flutter and the like;

s203, acquiring an acceleration signal in the milling process by using an acceleration sensor and a data acquisition system which are installed on a spindle box body;

s204, calculating the Tooth Passing Frequency (TPF) according to the rotating speed of the main shaft, carrying out comb filtering on the acceleration signal according to the TPF, reserving the first 3-5 times of the TPF, and obtaining the filtered acceleration signal;

the calculation formula of the passing frequency of the cutter teeth is as follows:

wherein omega is the rotating speed rpm of the main shaft, and N is the number of teeth of the milling cutter.

And S3, identifying the milling force by using the frequency response function obtained in the step S1 and the milling process acceleration signal obtained in the step S2 by adopting a recursive least square method.

S301, the cross-point acceleration frequency response function H (w) acquired in S102_xx、H(w)_xy、H(w)_yx、H(w)_yyConversion to unit impulse response function h (t) in time domain_xx、h(t)_xy、h(t)_yx、h(t)_yy；

S302, constructing a least square measurement model according to the discretization Duhami integration by using the unit impulse response function in the step S301, expanding the least square measurement model to the X, Y direction, and performing certain deformation so as to perform online identification;

the duhami integral formula is:

where x (t) is the acceleration response, h_xx(t) is the unit impulse response function of acceleration, F_x(t) is the milling force;

the discretization duhami integral formula is as follows:

equivalent is least squares measurement model:

x＝H_xxf_x+v

the direction of expansion to X, Y is:

wherein, Δ t is a discrete time interval; n is the number of samples; v is measurement noise; x is the acceleration response in the X direction; y is the acceleration response in the Y direction; f. of_xMilling force in X direction; f. of_yMilling force in Y direction; h_xxIndicating frequency response function, subscripts indicating frequency response function in different directions, e.g. H_xxA frequency response function representing xx directions;

the extended least squares measurement model after deformation is:

wherein x is_n、y_nX, Y direction nth time acceleration response respectively; h is_xxn、h_xyn、h_yxnAnd h_yynRespectively, unit impulse response function h (t)_xx、h(t)_xy、h(t)_yxAnd h (t)_yyValue at time n, f_xn、f_ynRespectively X, Y direction at the nth time.

S303, selecting a certain data length, performing recursive least square calculation according to the least square measurement model in the step S302, respectively calculating a gain matrix, and performing state recursive and variance matrix recursive;

the gain matrix formula is:

the state recurrence formula is:

the recursive formula of the variance matrix is as follows:

P_k＝(I-K_kH_k)P_k-1

wherein the content of the first and second substances,indicating the identified milling force, k indicating the current time, Z_k＝[x_k，y_k]^TRepresenting an acceleration signal, R_kTo measure the noise matrix, K_kAs a gain matrix, P_kIs a variance matrix, H_KIs the unit impulse response matrix at time k.

S304, identifying the milling force in the direction of X, Y from an identification state vectorSeparating to obtain X, Y direction identification milling force;

s305, according to impulse response function h (t)_xx、h(t)_xy、h(t)_yx、h(t)_yyThe time when the attenuation is close to zero is determinedThe magnitude of the medium k value.

When the time exceeds k, if the current time is at the time of k +1, the state recurrence formula is as follows:

the time being used for updatingThe milling force recognized at the first moment is required to be removed in a specific form Z_k+1＝[x_k+1，y_k+1]^TAcceleration signal at time K +1, K_k+1Is the gain matrix at time k +1, and it should be noted here that H is the time when k is exceeded_kAlways kept unchanged.

By covering the previous section of invalid data, real-time acceleration data is received, and the input signal is updated in real time, so that the real-time property of cutting force identification is guaranteed.

In another embodiment of the present invention, a milling force online identification system based on a recursive least square method is provided, which can be used to implement the above milling force online identification method based on the recursive least square method, and specifically, the milling force online identification system based on the recursive least square method includes a function module, a filtering module, and an identification module.

The function module is used for performing a force hammer knocking experiment along the X, Y feeding direction respectively under a milling machine coordinate system to obtain a plurality of groups of acceleration frequency response functions, and averaging frequency responses in each direction to obtain a cross-point acceleration frequency response function;

the filtering module is used for milling, selecting sampling frequency, measuring an acceleration signal in the X, Y feeding direction in the processing process and carrying out filtering pretreatment on the acceleration signal;

and the identification module is used for identifying the milling force by using a cross-point acceleration frequency response function acquired by the function module and a milling process acceleration signal subjected to filtering pretreatment by the filtering module and adopting a recursive least square method.

In yet another embodiment of the present invention, a terminal device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor being configured to execute the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is adapted to implement one or more instructions, and is specifically adapted to load and execute one or more instructions to implement a corresponding method flow or a corresponding function; the processor provided by the embodiment of the invention can be used for the operation of the milling force online identification method based on the recursive least square method, and comprises the following steps:

respectively carrying out a force hammer knocking experiment along the X, Y feeding direction under a milling machine coordinate system to obtain a plurality of groups of acceleration frequency response functions, and averaging the frequency response in each direction to obtain a cross-point acceleration frequency response function; milling, selecting sampling frequency, measuring an acceleration signal in the X, Y feeding direction in the machining process, and performing filtering pretreatment on the acceleration signal; and (3) carrying out online identification on the milling force by using a cross-point acceleration frequency response function and the milling process acceleration signal after filtering pretreatment and adopting a recursive least square method.

In still another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a terminal device and is used for storing programs and data. It is understood that the computer readable storage medium herein may include a built-in storage medium in the terminal device, and may also include an extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory.

One or more instructions stored in the computer-readable storage medium may be loaded and executed by the processor to implement the corresponding steps of the above-described embodiments with respect to the recursive least squares based milling force online identification method; one or more instructions in the computer-readable storage medium are loaded by the processor and perform the steps of:

respectively carrying out a force hammer knocking experiment along the X, Y feeding direction under a milling machine coordinate system to obtain a plurality of groups of acceleration frequency response functions, and averaging the frequency response in each direction to obtain a cross-point acceleration frequency response function; milling, selecting sampling frequency, measuring an acceleration signal in the X, Y feeding direction in the machining process, and performing filtering pretreatment on the acceleration signal; and (3) carrying out online identification on the milling force by using a cross-point acceleration frequency response function and the milling process acceleration signal after filtering pretreatment and adopting a recursive least square method.

In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1, a rectangular coordinate system is set for the numerically controlled milling machine according to the present invention. Two acceleration sensors are respectively arranged at the position of the X, Y shaft close to the front section bearing.

Referring to fig. 3, the force hammer strikes the position of the knife tip along direction X, Y to obtain the frequency response function from the knife tip to the box, and the average value of the frequency response function needs to be obtained through multiple modal experiments.

According to the above method, the frequency response function obtained in this embodiment is shown in fig. 4Are respectively H (w)_xx、H(w)_xy、H(w)_yx、H(w)_yy。

The milling parameters adopted in this embodiment are:

the rotation speed is 8000rpm, the feeding is 480mm, the cutting width is 1mm, and the cutting depth is 1 mm. The acceleration in the milling process is obtained through two acceleration sensors arranged on the box body, and the milling force is identified by using the method provided by the invention.

The recognition results are shown in fig. 5 and 6, which are X, Y direction recognition milling force versus measured milling force maps, respectively.

In conclusion, the milling force online identification method based on the recursive least square method is simple to operate and easy to implement, can accurately identify the milling force in the milling process, and provides a new idea for online identification of the milling force through continuous data updating.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.