阅读说明：本技术 一种基于能耗优化的数控机床控制方法及数控机床 (Numerical control machine tool control method based on energy consumption optimization and numerical control machine tool ) 是由 艾莉 闫森 刁微之 罗瑞 于 2019-11-12 设计创作，主要内容包括：本发明公开了一种基于能耗优化的数控机床控制方法,包括以下步骤：步骤1,对数控机床的存储堆栈、通信网络及外设模块进行初始化,验证存储堆栈的有效性,设置所述通信网络中的总线通信速率的最大值为以太网总线循环周期,读取当前主轴切削的能耗区间；步骤2,实时监控主轴切削的能耗状态,并记录信息,其中,所述信息包括切削的热功率误差及对所述热功率误差进行补偿执行的额外功率和对应时刻的温湿度；步骤3,将记录的所述信息输入预先构建决策表,以当前主轴切削的能耗区间最为约束条件生成所有的功率补偿方案,控制数控机床的进给速率,并调整温度及湿度,使得功耗达到最小。(The invention discloses a numerical control machine tool control method based on energy consumption optimization, which comprises the following steps of: step 1, initializing a storage stack, a communication network and a peripheral module of a numerical control machine tool, verifying the validity of the storage stack, setting the maximum value of a bus communication rate in the communication network as an Ethernet bus cycle period, and reading an energy consumption interval of current spindle cutting; step 2, monitoring the energy consumption state of the spindle cutting in real time, and recording information, wherein the information comprises a thermal power error of the cutting, extra power for compensating the thermal power error and temperature and humidity at a corresponding moment; and 3, inputting the recorded information into a pre-constructed decision table, generating all power compensation schemes by using the energy consumption interval of the current spindle cutting as a constraint condition, controlling the feed rate of the numerical control machine tool, and adjusting the temperature and the humidity to minimize the power consumption.)

1. A numerical control machine tool control method based on energy consumption optimization is characterized by comprising the following steps:

step 1, initializing a storage stack, a communication network and a peripheral module of a numerical control machine tool, verifying the validity of the storage stack, setting the maximum value of a bus communication rate in the communication network as an Ethernet bus cycle period, and reading an energy consumption interval of current spindle cutting;

step 2, establishing a monitoring module, monitoring the energy consumption state of the cutting of the spindle in real time, and recording information, wherein the information comprises a thermal power error of the cutting, extra power for compensating the thermal power error and temperature and humidity at a corresponding moment;

and 3, inputting the recorded information into a pre-constructed decision table, generating all power compensation schemes by using the energy consumption interval of the current spindle cutting as a constraint condition, controlling the feed rate of the numerical control machine tool, and adjusting the temperature and the humidity to minimize the power consumption.

2. The numerical control machine tool control method based on energy consumption optimization according to claim 1, wherein reading the energy consumption interval of the current spindle cutting further comprises: acquiring parameter selection conditions of all cutting, rapidly recording energy consumption information during idle cutter walking through the storage stack, cutting according to parameter selection, setting a program of segmented numerical control machining, dividing the speed change conditions in the cutting process into a plurality of sub-intervals with equal time intervals, adding a monitoring instruction to the program of each sub-interval, acquiring the power of the numerical control machining program of each sub-interval, and recording the cutting energy consumption of the machine tool of each sub-interval in real time.

3. The numerical control machine tool control method based on energy consumption optimization according to claim 2, wherein the real-time monitoring of the energy consumption state of the spindle cutting further comprises: and the monitoring module allocates codes for each sub-section and controls the feeding rate of the feeding shaft.

4. The numerical control machine tool control method based on energy consumption optimization according to claim 3, wherein the setting the communication network further comprises: the monitoring module is connected to a control base station through a wireless network bridge, reads the real-time information of the numerical control machine tool, and the base station sends an operation instruction to the peripheral module.

5. The numerical control machine tool control method based on energy consumption optimization according to claim 4, wherein the decision table further comprises: recording data acquired when the machine tool works, establishing a training sample, and establishing a corresponding decision table according to the training sample; selecting a resolution function corresponding to a decision table to obtain all reductions of the decision table; a primary neural network is established and the reduced information is used as input to train the neural network.

6. A numerical control machine tool for optimizing energy consumption is characterized by comprising a numerical control machine tool, a network module, a monitoring module, a peripheral module and an analysis module, wherein a storage stack, a communication network and the peripheral module of the numerical control machine tool are initialized, the validity of the storage stack is verified, the maximum value of a bus communication rate in the communication network is set as an Ethernet bus cycle period, and an energy consumption interval of current spindle cutting is read; the monitoring module is used for monitoring the energy consumption state of the spindle cutting in real time and recording information, wherein the information comprises a thermal power error of the cutting, extra power for compensating the thermal power error and temperature and humidity at corresponding moment; and the analysis module is used for inputting the recorded information into a pre-constructed decision table, generating all power compensation schemes by using the energy consumption interval of the current spindle cutting as a constraint condition, controlling the feed rate of the numerical control machine tool and adjusting the temperature and the humidity so as to minimize the power consumption.

7. The numerical control machine tool for optimizing energy consumption according to claim 6, characterized in that parameter selection conditions of all cutting are obtained, energy consumption during idle cutter moving is recorded, cutting is performed according to the parameter selection, a segmented numerical control machining program is set, a plurality of sub-intervals with equal time intervals are divided into speed rate change conditions in the cutting process, monitoring instructions are added to the program of each sub-interval, power of the numerical control machining program of each sub-interval is collected, and cutting energy consumption of machine tools of each sub-interval is recorded in real time.

8. The numerical control machine tool for optimizing energy consumption according to claim 7, wherein the monitoring module allocates codes to each of the sub-intervals, and controls the feeding rate of the feeding shaft for different sub-intervals respectively according to a power compensation scheme while adjusting the temperature and humidity of cutting.

9. The numerical control machine tool for optimizing energy consumption of claim 8, wherein the network module further comprises a monitoring module connected to a control base station through a wireless bridge, the monitoring module reads real-time information of the numerical control machine tool, and the base station sends an operation instruction to the peripheral module.

10. The numerical control machine tool for optimizing energy consumption of claim 9, wherein the analysis module further comprises recording data collected during the operation of the machine tool and establishing training samples, and establishing corresponding decision tables according to the training samples; selecting a resolution function corresponding to a decision table to obtain all reductions of the decision table; a primary neural network is established and the reduced information is used as input to train the neural network.

Technical Field

The invention relates to the field of automatic control, in particular to a numerical control machine tool control method based on energy consumption optimization and a numerical control machine tool.

Background

Manufacturing has become one of the major sources of energy consumption and carbon emissions today, and international energy agency research has shown that nearly 1/3% of the energy consumption and 40% of the carbon dioxide emissions worldwide are attributed to manufacturing. Research of the Massachusetts institute of technology and technology shows that: one numerically controlled machine tool operated for one year produced carbon dioxide emissions equivalent to the annual carbon dioxide emissions of 61 SUVs. Numerically controlled machine tools, which are key devices in the manufacturing industry, are very significant in energy consumption and carbon emissions. Therefore, research on energy consumption prediction and energy-saving technology of the numerical control machine tool plays an important role in energy conservation and emission reduction of the manufacturing industry and even the country. The energy conservation and emission reduction problem of the manufacturing industry has attracted extensive attention of governments, enterprises and research organizations of colleges and universities. As a main processing method in the manufacturing industry, the mechanical processing technology has a large proportion of energy consumption in the overall energy consumption of the manufacturing industry. Therefore, the modeling of the energy consumption of the machining process is urgently needed, a foundation is laid for energy optimization and energy conservation of the machining process, and the development of energy conservation and emission reduction work of the manufacturing industry is further promoted.

Disclosure of Invention

The invention aims to solve the technical problems in the prior art. Therefore, the invention discloses a numerical control machine tool control method based on energy consumption optimization, which comprises the following steps of:

step 1, initializing a storage stack, a communication network and a peripheral module of a numerical control machine tool, verifying the validity of the storage stack, setting the maximum value of a bus communication rate in the communication network as an Ethernet bus cycle period, and reading an energy consumption interval of current spindle cutting;

step 2, establishing a monitoring module, monitoring the energy consumption state of the cutting of the spindle in real time, and recording information, wherein the information comprises a thermal power error of the cutting, extra power for compensating the thermal power error and temperature and humidity at a corresponding moment;

and 3, inputting the recorded information into a pre-constructed decision table, generating all power compensation schemes by using the energy consumption interval of the current spindle cutting as a constraint condition, controlling the feed rate of the numerical control machine tool, and adjusting the temperature and the humidity to minimize the power consumption.

Preferably, reading the energy consumption interval of the current spindle cutting further comprises: acquiring parameter selection conditions of all cutting, rapidly recording energy consumption information during idle cutter walking through the storage stack, cutting according to parameter selection, setting a program of segmented numerical control machining, dividing the speed change conditions in the cutting process into a plurality of sub-intervals with equal time intervals, adding a monitoring instruction to the program of each sub-interval, acquiring the power of the numerical control machining program of each sub-interval, and recording the cutting energy consumption of the machine tool of each sub-interval in real time.

Preferably, the monitoring the energy consumption state of the spindle cutting in real time further comprises: and the monitoring module allocates codes for each sub-section and controls the feeding rate of the feeding shaft.

Preferably, the setting up the communication network further comprises: the monitoring module is connected to a control base station through a wireless network bridge, reads the real-time information of the numerical control machine tool, and the base station sends an operation instruction to the peripheral module.

Preferably, the decision table further comprises: recording data acquired when the machine tool works, establishing a training sample, and establishing a corresponding decision table according to the training sample; selecting a resolution function corresponding to a decision table to obtain all reductions of the decision table; a primary neural network is established and the reduced information is used as input to train the neural network.

The invention also discloses a numerical control machine tool for optimizing energy consumption, which comprises a numerical control machine tool, a network module, a monitoring module, an external module and an analysis module, wherein the storage stack, the communication network and the external module of the numerical control machine tool are initialized, the validity of the storage stack is verified, the maximum value of the bus communication rate in the communication network is set as the Ethernet bus cycle period, and the energy consumption interval of the current spindle cutting is read; the monitoring module is used for monitoring the energy consumption state of the spindle cutting in real time and recording information, wherein the information comprises a thermal power error of the cutting, extra power for compensating the thermal power error and temperature and humidity at corresponding moment; and the analysis module is used for inputting the recorded information into a pre-constructed decision table, generating all power compensation schemes by using the energy consumption interval of the current spindle cutting as a constraint condition, controlling the feed rate of the numerical control machine tool and adjusting the temperature and the humidity so as to minimize the power consumption.

Preferably, parameter selection conditions of all cutting are acquired, energy consumption during idle cutter running is recorded, cutting is performed according to the parameter selection, a segmented numerical control machining program is set, the speed change conditions in the cutting process are divided into a plurality of sub-intervals with equal time intervals, a monitoring instruction is added to the program of each sub-interval, the power of the numerical control machining program of each sub-interval is acquired, and the cutting energy consumption of machine tools of each sub-interval is recorded in real time.

Preferably, the monitoring module allocates codes to each sub-interval, and respectively controls the feeding rate of the feeding shaft and simultaneously adjusts the temperature and humidity of cutting for different sub-intervals according to a power compensation scheme.

Preferably, the network module further comprises a monitoring module connected to the control base station through a wireless network bridge, the monitoring module reads the real-time information of the numerical control machine, and the base station sends the operation instruction to the peripheral module.

Preferably, the analysis module further includes recording data acquired when the machine tool operates, establishing a training sample, and establishing a corresponding decision table according to the training sample; selecting a resolution function corresponding to a decision table to obtain all reductions of the decision table; a primary neural network is established and the reduced information is used as input to train the neural network.

Compared with the prior art, the invention realizes more accurate temperature and power control. In addition, the analysis module of the invention preferably selects a neural network trained by experimental samples to make decisions, so that the energy consumption can be reduced and the efficiency can be improved under the condition of changing partial parameters.

Drawings

The invention will be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments. In the drawings, like reference numerals designate corresponding parts throughout the different views.

Fig. 1 is a flow chart of the numerical control machine tool control method based on energy consumption optimization of the invention.

Detailed Description