CN112904800A - Intelligent machine tool optimization method and auxiliary system - Google Patents
Intelligent machine tool optimization method and auxiliary system Download PDFInfo
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- CN112904800A CN112904800A CN202110073851.9A CN202110073851A CN112904800A CN 112904800 A CN112904800 A CN 112904800A CN 202110073851 A CN202110073851 A CN 202110073851A CN 112904800 A CN112904800 A CN 112904800A
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- G—PHYSICS
- 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
- G05B19/406—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 monitoring or safety
- G05B19/4065—Monitoring tool breakage, life or condition
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/36—Nc in input of data, input key till input tape
- G05B2219/36044—Program modified after breakage, crash, jamming
Abstract
The invention discloses an intelligent optimization method for a machine tool, which comprises the following steps: s1, arranging a sensor and a human-computer interaction device; setting an executing device for adjusting the working parameters of the machine tool; setting a calculating device for calculating the processing parameters; s2, setting the working mode of the machine tool and the normal range of the working parameters of the machine tool in each working mode; s3, acquiring real-time numerical values of the working parameters of the machine tool; s4, analyzing the range and the change rate of real-time numerical values of the working parameters of the machine tool; s5, adjusting the working parameter value of the machine tool by using the execution device according to the calculation result in the step S4; and S6, generating a processing process report, and optimizing a calculation model of the calculation device according to the condition of automatically adjusting working parameters in the processing process. Therefore, the intelligent optimization method of the machine tool can conveniently carry out intelligent upgrading and reconstruction on the traditional machine tool, the service life of the machine tool is prolonged, the machining quality of the machine tool is improved, and the invention also discloses an intelligent optimization auxiliary system of the machine tool.
Description
Technical Field
The invention relates to the field of machining, in particular to an intelligent optimization method and an intelligent optimization auxiliary system for a machine tool.
Background
Although the automation degree in the field of machining is continuously improved at present, most of workload in the whole industry still needs to be operated by operators, the technical levels of the operators are different, and the difference of experience causes different people to process the same workpiece according to different process parameters, so that the processing time, the processing quality and the like are different. And the experience has no obvious index and is not easy to teach.
Each equipment has a normal working range, new operators cannot master the specifications usually, parameters exceeding the normal working range are inevitably set in the machining process, abnormal abrasion of the equipment is caused, the service life of the equipment is further shortened, if the change of the parameters can be found in time, the machining waste is reduced, and the product yield is improved.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: an intelligent optimization method for a machine tool is provided.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
an intelligent optimization method for a machine tool comprises the following steps:
s1, setting a sensor and a man-machine interaction device to obtain the working parameters of the machine tool and the parameters of the workpiece to be processed;
setting an executing device for adjusting the working parameters of the machine tool;
setting a computing device, wherein a computing model is arranged in the computing device and is used for computing processing parameters;
s2, setting the working mode of the machine tool and the normal range of the working parameters of the machine tool in each working mode;
s3, acquiring real-time numerical values of the working parameters of the machine tool by using a sensor;
s4, analyzing the range and the change rate of the real-time numerical values of the working parameters of the machine tool by using a computing device, and comparing the range and the change rate with the normal range of the working parameters;
s5, adjusting the working parameter value of the machine tool by using the execution device according to the calculation result in the step S4;
and S6, generating a processing process report, and optimizing a calculation model of the calculation device according to the condition of automatically adjusting working parameters in the processing process.
Compared with the prior art, the invention has the following technical effects:
by means of the method, the change of the working state of the machine tool can be obtained in time by utilizing the sensor module in the machining process of the machine tool, the reaction can be made in millisecond-level time, the working state of the machine tool can be adjusted in time when abnormal conditions occur, or the machine tool can be simply adjusted to be under the optimal working parameter when a new hand operates a machining standard component, so that the highest machining efficiency and yield can be obtained, the intelligent improvement of the traditional machine tool is realized, the service life of the machine tool is prolonged, and the yield and the machining efficiency of machined products are improved.
On the basis of the technical scheme, the invention can be further improved as follows.
Preferably, the working parameters of the machine tool include real-time working voltage and working current of the main driving motor, the rotating speed of the main shaft, the vibration value of the machine tool, the position of the tool rest, the feeding speed and the type of the tool.
The method has the advantages that the parameters can comprehensively represent the working state of the machine tool, wherein the cutter type information comprises the material, hardness, cutter width, cutter depth and other parameters of the cutter.
Preferably, the parameters of the workpiece to be processed include material, shape, size and three-dimensional model before and after processing.
The method has the advantages that the processing procedure of the workpiece can be deduced by using the program by means of the parameters, the working parameters of the machine tool are combined, the actually required working process of the machine tool and the effect which can be achieved by each step can be calculated, and the tracking analysis in the processing process is convenient.
Preferably, step S2 further includes setting the optimal operating parameters of the standard process of the machine tool.
The beneficial effect of adopting above-mentioned further scheme is that when operating personnel master the skill and is unfamiliar, through the optimum working parameter of the standard process of setting for, when the machined part is the standard part, when the manufacturing procedure is the standard process, can automatic tune in optimum working parameter to realize the high-efficient processing of work piece, and do not rely on operating personnel's proficiency.
Preferably, if the change rate of the working parameters changes suddenly, the cutter is determined to be abnormal, the machining parameters are automatically adjusted, and alarm information is sent out and recorded in the machining report.
The further scheme has the advantages that the tool abnormity can be judged in time and recorded, after machining is finished, workers can check the machine tool in a targeted mode according to the mutation situation of the working parameters recorded in the machining report and discover hidden equipment hazards as soon as possible, and under the condition that no detection equipment exists, the slight abnormity of the tool is difficult to perceive by the workers under the general condition.
Preferably, if the working parameters are larger than the optimal working parameters of the standard part all the time, the material of the workpiece to be processed is determined, the working parameters are automatically adjusted, the feeding speed is reduced, and the feeding speed is recorded in the processing report.
The further scheme has the advantages that the change of the material of the workpiece to be machined can be judged according to the change condition of the working parameters, the working parameters of the machine tool can be adjusted in time, the machine tool is prevented from being damaged, and the service life of the machine tool is prolonged.
An intelligent optimization auxiliary system for a machine tool comprises a machine tool end module, an edge calculation end module and a cloud end module which are sequentially in communication connection;
the machine tool end module comprises a sensor module and a parameter correction module; the sensor module is used for acquiring real-time numerical values of working parameters of the machine tool, and the parameter correction module is used for adjusting the working parameters of the machine tool;
the edge calculation end module comprises a man-machine interaction module and a processing parameter calculation module, the man-machine interaction module is used for inputting the normal range of the working parameters of the machine tool and the parameters of the workpiece to be processed, and the processing parameter calculation module calculates the processing parameters of the workpiece to be processed according to a built-in algorithm model and generates a processing report;
the cloud module comprises a data recording module and an optimization feedback module, the data recording module is used for analyzing the processing report, generating early warning information and generating historical trend information, and the optimization feedback module is used for analyzing the processing report and optimizing a processing parameter calculation module in the edge calculation end module.
Compared with the prior art, the technical scheme has the following technical effects:
the intelligent improvement can be conveniently carried out to stock traditional lathe, improves the degree of automation of lathe, promotes lathe work efficiency.
Further, the sensor module is used for acquiring real-time working voltage and working current of the main driving motor, the rotating speed of the main shaft, the vibration value of the machine tool, the position of the tool rest, the feeding speed and the type of the tool.
The beneficial effect of adopting the further scheme is that: the parameters are easy to obtain, and based on the parameters, the working state of the machine tool can be comprehensively known.
Further, the parameters of the workpiece to be processed include material, shape, size and three-dimensional model before and after processing.
The beneficial effect of adopting the further scheme is that: the actual machining process of the workpiece to be machined and the theoretical state of the workpiece in each machining process can be calculated by means of the parameters and the working parameters of the machine tool, so that the actual working condition of the machine tool can be tracked and judged.
Drawings
FIG. 1 is a schematic structural diagram of a machine tool intelligent optimization auxiliary system framework in an embodiment.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Example 1: an intelligent optimization auxiliary system for a machine tool comprises a machine tool end module, an edge calculation end module and a cloud end module which are sequentially in communication connection;
the machine tool end module comprises a sensor module and a parameter correction module; the sensor module is used for acquiring real-time numerical values of working parameters of the machine tool, and the parameter correction module is used for adjusting the working parameters of the machine tool; the sensor module is used for acquiring real-time working voltage and working current of the main driving motor, the rotating speed of the main shaft, the vibration value of the machine tool, the position of the tool rest, the feeding speed, the type of the tool and the like.
The edge calculation end module comprises a human-computer interaction module and a processing parameter calculation module, the human-computer interaction module is used for inputting the normal range of the working parameters of the machine tool and the parameters of the workpiece to be processed, and the parameters of the workpiece to be processed comprise the material, the shape, the size and the three-dimensional model before and after processing; the processing parameter calculation module calculates the processing parameters of the workpiece to be processed according to a built-in algorithm model and generates a processing report;
the cloud module comprises a data recording module and an optimization feedback module, the data recording module is used for analyzing the processing report, generating early warning information and generating historical trend information, and the optimization feedback module is used for analyzing the processing report and optimizing a processing parameter calculation module in the edge calculation end module.
Example 2:
the intelligent machine tool optimizing method includes the following steps:
s1, setting a sensor and a man-machine interaction device to obtain the working parameters of the machine tool and the parameters of the workpiece to be processed;
setting an executing device for adjusting the working parameters of the machine tool;
setting a computing device, wherein a computing model is arranged in the computing device and is used for computing processing parameters;
s2, setting the working mode of the machine tool and the normal range of the working parameters of the machine tool in each working mode;
s3, acquiring real-time numerical values of the working parameters of the machine tool by using a sensor;
s4, analyzing the range and the change rate of the real-time numerical values of the working parameters of the machine tool by using a computing device, and comparing the range and the change rate with the normal range of the working parameters;
s5, adjusting the working parameter value of the machine tool by using the execution device according to the calculation result in the step S4;
and S6, generating a processing process report, and optimizing a calculation model of the calculation device according to the condition of automatically adjusting working parameters in the processing process.
Example 3, example 1 was combined with example 2:
and arranging a sensor module on site, adding a voltage sensor and a current sensor to each main shaft power cable of the machine tool, acquiring the voltage and the current of the power cable in real time, and transmitting the result to an edge calculation terminal (corresponding to the edge calculation terminal module) in real time. The edge terminal is connected with the machine tool through a network cable to obtain the working parameters of the machine tool, and the working parameters of the machine tool comprise: feed speed, spindle load, tool compensation, machining program, servo current, voltage and other information.
The method comprises the steps of inputting parameters of a workpiece to be machined, the model of a machine tool, selecting basic operation modes such as turning/milling/drilling and the like, setting a tool number, the diameter of a cutter, cutter teeth, the size of the workpiece (3D drawing), raw material information and the like by means of a human-computer interaction module.
The edge calculation end module automatically leads in an equipment setting corresponding table according to the information, the equipment setting corresponding table stores the optimal working parameters of the standard working procedures of the machine tool, and the optimal working parameters corresponding to the drilling working procedures are as follows:
the feeding speed is 200mm/s
Main shaft rotating speed 3000RAP/Min
The load of the main shaft is 70 percent
Current of 3A
The voltage is 380V
And automatically acquiring a formula for calculating the torque of the main shaft and the metal removal rate through the model of the machine tool.
The edge computing terminal obtains an NC machining program (the NC machining program is an executive program of a numerical control system and is an explanatory language, the numerical control system compiles a specific code to generate an executable instruction to carry out numerical control machining), and the NC machining program is utilized to calculate a cutting track in the workpiece machining process, obtain theoretical machining parameters and establish a corresponding relation between tool rest coordinates and the parameters. Such as: the cutting method comprises the following steps of tool rest coordinates, cutting states, spindle rotating speed, feeding speed, current, voltage and the like, and the state is subjected to slicing processing according to the slicing principle that the cutting action corresponding to each line of NC machining program is one slice.
The method comprises the following steps of acquiring working parameters of the machine tool in real time by means of a sensor module, such as: the method comprises the steps of carrying out current machining procedures (corresponding to the number of lines of an NC machining program), tool rest coordinates, spindle rotating speed, feeding speed, current, voltage and the like, wherein the collection period is 20-50 ms, storing data into a cache library, transferring the data to a local high-capacity relational database, and carrying out characteristic value matching through a linear regression algorithm.
And the edge calculation end module deduces theoretical machining data through the imported basic information such as the tool number, the workpiece size and the like, compares the theoretical machining data with actual machining data to obtain cutting information, cutting depth and cutting width so as to obtain metal removal rate, further obtains power required by a spindle driving motor, and compares the power with real-time load. If the real-time load is larger than the power required by the driving motor, the machining tool possibly has abnormity at the moment, or the material quality of the raw material is hard. And the edge calculation end module automatically records the abnormal nodes and feeds the abnormal nodes back to machine tool processing personnel in time. If the cutter is abnormal, the abnormal condition is instantaneous, the edge calculation end module can automatically remind abnormal processing information according to the data model, the processing parameters are automatically adjusted, the damage degree of equipment is reduced, and personnel can check the abnormal condition according to prompts after finishing processing. If the raw material is replaced, the whole body is generally larger, and the edge calculation end module can automatically adjust parameters, reduce parameters such as the rotating speed of equipment and the like.
After machining is completed, the edge calculation end module generates a machining report of the machining process and feeds the machining report back to the cloud end module, and the cloud end module records raw materials, cutter numbers and program numbers recorded in the machining report. And if the working parameter automatic adjustment item is recorded in the processing report, generating warning information and prompting maintenance personnel to further check. The cloud module is used for further optimizing the processing parameters for processing the workpiece by comparing the dimensional information, generating an optimization report and feeding the optimization report back to the processing personnel, and assisting the personnel to quickly optimize the parameters.
In summary, all responses in the machining process of the machine tool are in millisecond level, and the cloud edge cooperation mode is used, so that the intelligent optimization of the machine tool is more suitable.
And after the machining is finished, analyzing and sorting the statistical data to generate an analysis report, and performing key marking on the abnormal position in the machining. And the designer receives the abnormal feedback to adjust the parameter correlation and optimize the empirical model. The processing condition can be conveniently and quickly known by personnel. The test time for the new material processing characteristics is reduced, and the misoperation of a new worker on a machine tool is reduced. The service life of the equipment is prolonged.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. An intelligent optimization method for a machine tool is characterized by comprising the following steps:
s1, setting a sensor and a man-machine interaction device to obtain the working parameters of the machine tool and the parameters of the workpiece to be processed;
setting an executing device for adjusting the working parameters of the machine tool;
setting a computing device, wherein a computing model is arranged in the computing device and is used for computing processing parameters;
s2, setting the working mode of the machine tool and the normal range of the working parameters of the machine tool in each working mode;
s3, acquiring real-time numerical values of the working parameters of the machine tool by using a sensor;
s4, analyzing the range and the change rate of the real-time numerical values of the working parameters of the machine tool by using a computing device, and comparing the range and the change rate with the normal range of the working parameters;
s5, adjusting the working parameter value of the machine tool by using the execution device according to the calculation result in the step S4;
and S6, generating a processing process report, and optimizing a calculation model of the calculation device according to the condition of automatically adjusting working parameters in the processing process.
2. The intelligent optimization method for the machine tool according to claim 1, wherein the working parameters of the machine tool comprise real-time working voltage and working current of a main driving motor, the rotating speed of a main shaft, the vibration value of the machine tool, the position of a tool rest, the feeding speed and the type of a tool.
3. The intelligent optimization method of the machine tool according to claim 1 or 2, wherein the parameters of the workpiece to be processed comprise material, shape, size and three-dimensional model before and after processing.
4. The intelligent optimization method for machine tools according to claim 1 or 2, characterized in that step S2 further comprises setting the optimal working parameters of the standard process of the machine tool.
5. The intelligent optimization method of the machine tool according to claim 4, wherein if the change rate of the working parameters changes suddenly, the tool is determined to be abnormal, the processing parameters are automatically adjusted, and alarm information is sent to the outside and recorded in the processing report.
6. The intelligent optimization method of the machine tool according to claim 4, wherein if the working parameters are larger than the optimal working parameters of the standard part all the time, the material of the workpiece to be processed is determined, the working parameters are automatically adjusted, the feeding speed is reduced, and the working parameters are recorded in the processing report.
7. An intelligent optimization auxiliary system of a machine tool is characterized by comprising a machine tool end module, an edge calculation end module and a cloud end module which are sequentially in communication connection;
the machine tool end module comprises a sensor module and a parameter correction module; the sensor module is used for acquiring real-time numerical values of working parameters of the machine tool, and the parameter correction module is used for adjusting the working parameters of the machine tool;
the edge calculation end module comprises a man-machine interaction module and a processing parameter calculation module, the man-machine interaction module is used for inputting the normal range of the working parameters of the machine tool and the parameters of the workpiece to be processed, and the processing parameter calculation module calculates the processing parameters of the workpiece to be processed according to a built-in algorithm model and generates a processing report;
the cloud module comprises a data recording module and an optimization feedback module, the data recording module is used for analyzing the processing report, generating early warning information and generating historical trend information, and the optimization feedback module is used for analyzing the processing report and optimizing a processing parameter calculation module in the edge calculation end module.
8. The intelligent optimization auxiliary system for the machine tool according to claim 7, wherein the sensor module is used for acquiring real-time working voltage and working current of a main driving motor, the rotating speed of a main shaft, the vibration value of the machine tool, the position of a tool rest, the feeding speed and the type of a tool.
9. The intelligent optimization auxiliary system for the machine tool according to claim 7 is characterized in that the parameters of the workpiece to be machined comprise material, shape, size and three-dimensional model before and after machining.
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| CN113552846A (en) * | 2021-07-27 | 2021-10-26 | 深圳市玄羽科技有限公司 | Motor rotating speed detection system and application method thereof |
| CN114054913A (en) * | 2021-11-22 | 2022-02-18 | 常州九圣焊割设备股份有限公司 | Parameter self-regulation method and system based on working condition |
| CN117161821A (en) * | 2023-11-02 | 2023-12-05 | 南通海鹰机电集团有限公司 | Numerical control drilling machine spindle self-adaptive feeding control method and system |
| CN117300184A (en) * | 2023-11-29 | 2023-12-29 | 山东正祥工矿设备股份有限公司 | Control system for processing lathe for copper casting production |
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