CN110764464A - Numerical control machine tool control method based on energy consumption optimization and numerical control machine tool - Google Patents
Numerical control machine tool control method based on energy consumption optimization and numerical control machine tool Download PDFInfo
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- CN110764464A CN110764464A CN201911102414.4A CN201911102414A CN110764464A CN 110764464 A CN110764464 A CN 110764464A CN 201911102414 A CN201911102414 A CN 201911102414A CN 110764464 A CN110764464 A CN 110764464A
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- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/408—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by data handling or data format, e.g. reading, buffering or conversion of data
- G05B19/4083—Adapting programme, configuration
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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Abstract
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.
Description
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.
In the prior art, according to the change characteristic of the cutting rate, the machining process can be divided into a constant cutting rate process and a variable cutting rate process. The constant cutting rate process is a cutting process (such as excircle turning, plane milling and the like) in which the cutting elements are kept constant. The variable cutting rate process is a cutting process (end face turning, grooving) in which at least one cutting element (cutting speed vc, feed amount f, cutting depth ap) is changed. The cutting power of the constant cut rate process is also a stable value, while the power of the variable cut rate process is dynamically changing over time, which is more complex than the power characteristics of the constant cut rate process. But few studies have been reported which specifically discuss the cutting power and energy consumption of the variable cutting rate process. The material cutting power in the process of changing the cutting rate is related to cutting elements, machine tool mechanical transmission, motor power loss and the like, the power characteristics are complex, the change rule is various, and the material cutting power is closely related to the change characteristics of the cutting elements.
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.
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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
Example one
As shown in fig. 1, a numerical control machine tool control method based on energy consumption optimization includes 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.
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.
Wherein inputting the recorded information into a pre-constructed decision table machine comprises: and establishing a training sample, namely selecting a typical input-output pair as a sample set for training. The number of samples taken is related to the size of the problem and should generally be more than twice the dimension of the input information. For example, with 40 sensors, the input information dimension is 40, and at least 80 samples are selected. And then establishing a decision table, namely taking each training sample as a row vector and taking the input and the output as column vectors, establishing a comparison table of sensor information input and decision, and storing the comparison table in a computer in a matrix form. For example, with 10 sensors, 50 samples are taken, and the decision table is a 50 × 11 matrix. And solving a resolution matrix of the decision table, namely establishing an n multiplied by n matrix for the decision table with n rows, wherein the elements of x rows and y columns are a set of column numbers with different values of x rows and y rows in the decision table. And solving all reductions of the decision table by adopting a resolution function method according to the resolution matrix. For each reduction, a neural network is built and trained using the reduced information as input. In this way, the training process is completed. The result is a neural network model for each reduction. The fusion stage recombines the information according to all the reductions obtained in the training stage and the information collected by the sensors, namely extracts the sensor information corresponding to the elements contained in the reduction set for each reduction obtained in the training stage, and recombines the information into a vector of information.
Example two
The embodiment of the invention mainly describes the numerical control machine tool provided by the invention from the perspective of hardware, and provides the numerical control machine tool for optimizing energy consumption, which comprises the numerical control machine tool, a network module, a monitoring module, an external module and an analysis module, wherein a storage stack, a 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 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.
The monitoring module integrated by the PROFIBUS network is adopted, and the calculation and check are performed according to the communication rate theory as follows. The transmission rate of the PROFIBUS is 9.6 Kbit/s-12 Mbit/s, the highest rate is 187.5Kbit/s under the distance of 1000m, and the method is a half-duplex asynchronous transmission mode. The character frame is 8 bit data bits plus 3 bit control information bits. Bus cycle time T = (number of master idle frame interval bits + number of slave delay bits + number of frame headers in request and response + number of bytes of input data per slave 11+ number of bytes of output data per slave 11) number of slave bits time. In the equipment monitoring module, the communication rate of the PROFIBUS is 187.500Kbit/s, the number of slave stations is 2, the communication input is 128 bytes, and the output is 50 bytes in length. The bus cycle time is T, and is calculated theoretically as follows.
T=(33+75+11+198+178*11)*2/187500=24.3ms.
The actual test environment hardware uses CP5711 communication card master to communicate with 2 slave configuration EM277 modules S7-226 programmable controllers, using DP protocol. Meanwhile, SimaticSoftnetOPC server software is adopted in the software. The test flow is that the master station writes in the variable area of the slave station V, the slave station immediately stores the variable area, and the master station reads out and compares whether the variable area is the same or not.
The time from writing to reading is 78-125 ms when the test is carried out for 10 times. The time delay of one-time write or read through the bus is 39-62.5 ms.
If the number of the slave stations in the system is 100, the bus communication rate is selected to be the highest rate of 12Mbps of the PROFIBUS. The communication input is 128 bytes and the output is 50 bytes long. The bus cycle time T is calculated theoretically as follows.
T=(33+75+11+198+178*11)*100/12000000=19ms.
It can be seen that the PROFIBUS with the rate of 12Mbps in this embodiment is equivalent to the Ethernet bus cycle period of 100MBps in the previous example.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
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.
Although the invention has been described above with reference to various embodiments, it should be understood that many changes and modifications may be made without departing from the scope of the invention. It is therefore intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is the following claims, including all equivalents, that are intended to define the spirit and scope of this invention. The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.
Claims (10)
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.
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