CN113552840A - Machining control system - Google Patents
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- CN113552840A CN113552840A CN202110872871.2A CN202110872871A CN113552840A CN 113552840 A CN113552840 A CN 113552840A CN 202110872871 A CN202110872871 A CN 202110872871A CN 113552840 A CN113552840 A CN 113552840A
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- 238000003754 machining Methods 0.000 title claims abstract description 57
- 238000004088 simulation Methods 0.000 claims abstract description 51
- 230000002159 abnormal effect Effects 0.000 claims abstract description 23
- 238000012544 monitoring process Methods 0.000 claims abstract description 19
- 238000012545 processing Methods 0.000 claims description 49
- 238000013473 artificial intelligence Methods 0.000 claims description 18
- 238000012549 training Methods 0.000 claims description 16
- 238000000034 method Methods 0.000 claims description 12
- 238000004891 communication Methods 0.000 claims description 8
- 238000013528 artificial neural network Methods 0.000 claims description 7
- 230000007613 environmental effect Effects 0.000 claims description 7
- 238000012360 testing method Methods 0.000 claims description 3
- 238000013527 convolutional neural network Methods 0.000 claims description 2
- 230000005856 abnormality Effects 0.000 abstract description 4
- 238000001514 detection method Methods 0.000 abstract description 4
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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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
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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
- G05B2219/31—From computer integrated manufacturing till monitoring
- G05B2219/31356—Automatic fault detection and isolation
Abstract
The invention discloses a machining control system, relates to the technical field of machining implementation monitoring, and solves the technical problems that the detection range of the existing scheme on faults is incomplete, and the abnormal state of a numerical control machine tool is not clearly divided, so that the proper regulation and control cannot be carried out according to the abnormal state; the system comprises a plurality of numerical control machines and a monitoring system for controlling the numerical control machines, wherein the monitoring system comprises a data acquisition module, a processor and an execution control module; the invention judges whether the operation of the numerical control machine tool is abnormal or not by comparing and analyzing the machining parameter simulation curve and the machining parameter real-time curve, monitors the actual operation from the simulation, divides the abnormality of the numerical control machine tool into different types, adjusts the operation according to the preset machining parameters when the operation is abnormal, directly stops the operation and warns when the operation is failed, clearly divides the abnormal state of the numerical control machine tool, avoids automatic stop when the abnormality occurs, and is beneficial to improving the working efficiency of the numerical control machine tool.
Description
Technical Field
The invention belongs to the technical field of mechanical processing implementation monitoring, and particularly relates to a mechanical processing control system.
Background
The complexity and the intelligence of the manufacturing equipment are continuously improved, however, due to the structural complexity of the complex equipment, when the performance or the function of the complex equipment is improved, a series of problems can be brought to the aspects of reliability, safety, usability, economy and the like of a system, and the potential possibility of system failure or invalidation is increased, so that the real-time monitoring is carried out on the machining process, and the operation safety and the machining efficiency of the system can be ensured only by timely correcting according to the monitoring result.
In the existing scheme (with the publication number of CN11139944A), the actual working state of the machine tool is grasped by performing data acquisition and processing on some working state data in the machining process of the machine tool and comparing the actual characteristic parameters with normal values, so as to achieve the purposes of fault diagnosis and state prediction.
According to the scheme, the fault state of the machine tool is judged by comparing the actual characteristic parameters with the normal values, but in the actual operation process, the fault state of the machine tool cannot be monitored timely and comprehensively by the detection mode; therefore, a control system capable of overall monitoring and real-time feedback during the machining process is needed.
Disclosure of Invention
The invention provides a machining control system, which is used for solving the technical problems that the detection range of the existing scheme on faults is incomplete, the abnormal state of a numerical control machine tool is not clearly divided, and appropriate regulation and control cannot be carried out according to the abnormal state.
The purpose of the invention can be realized by the following technical scheme: a machining control system comprises a plurality of numerical control machines and a monitoring system for controlling the numerical control machines;
the monitoring system is in communication and/or electrical connection with a plurality of the numerical control machines; the monitoring system comprises a data acquisition module, a processor and a control execution module; the processor is also in communication and/or electrical connection with the intelligent terminal;
the processor simulates the machining process of the numerical control machine tool by combining preset machining parameters and a set program, and obtains a machining parameter simulation curve;
acquiring real-time processing parameters of the numerical control machine tool during processing according to a set program through the data acquisition module, and acquiring a real-time curve of the processing parameters;
comparing and analyzing the machining parameter simulation curve and the machining parameter real-time curve to obtain an analysis result, combining the analysis result with a fault prediction model to obtain a fault label of the numerical control machine tool, and performing early warning according to the fault label; the value of the fault label is 0 or 1;
and the control execution module adjusts the numerical control machine according to the analysis result and the fault label.
Preferably, the preset processing parameters and the real-time processing parameters have consistent parameter contents, and each parameter content includes a speed, an angle and a position.
Preferably, the machining parameter simulation curve comprises a speed simulation curve, an angle simulation curve and a position simulation curve, the independent variable of the machining parameter simulation curve is time, and the numerical control machine tool can machine the actual workpiece after the machining parameter simulation curve meets the requirement.
Preferably, the real-time processing parameter curve comprises a real-time speed curve, a real-time angle curve and a real-time position curve, and the real-time processing parameter curve is obtained by real-time fitting of data acquired by the data acquisition module in combination with a polynomial fitting method.
Preferably, the analyzing of the processing parameter simulation curve and the processing parameter real-time curve includes:
under the condition of ensuring the initial time to be consistent, randomly selecting N times, respectively obtaining corresponding parameter values of the N times in the processing parameter simulation curve and the processing parameter real-time curve, and respectively marking the parameter values as a simulation parameter sequence and a real-time parameter sequence; wherein N is an integer greater than 2;
extracting corresponding parameters in the simulation parameter sequence and the implementation parameter sequence, and respectively fitting to generate a speed fitting curve, an angle fitting curve and a position fitting curve;
when the slopes of the speed fitting curve, the angle fitting curve and the position fitting curve can meet the corresponding slope requirements, judging that the numerical control machine works normally; otherwise, judging whether the numerical control machine tool is abnormal in work, and judging whether the numerical control machine tool fails through a failure prediction model.
Preferably, obtaining the fault label through the fault prediction model includes:
acquiring environmental parameters of the numerical control machine tool during working in real time; the environmental parameters include temperature and humidity;
splicing the environment parameters and the real-time processing parameters to generate initial parameters, and inputting the initial parameters to a fault prediction model to obtain target parameters; and the target parameter is the fault label corresponding to the initial parameter.
Preferably, the obtaining of the fault prediction model includes:
selecting standard training data; the standard training data comprises real-time working parameters and environment parameters corresponding to the numerical control machine tool in fault and real-time working parameters and environment parameters corresponding to the numerical control machine tool in normal operation;
setting a fault label for standard training data;
constructing an artificial intelligence model; the artificial intelligence model comprises one or more of an error inverse feedback neural network, an RBF neural network and a deep convolutional neural network;
and standard training data is used as the input of the artificial intelligence model, the corresponding fault label is used as the output of the artificial intelligence model to finish the training, testing and checking of the artificial intelligence model, and the trained artificial intelligence model is marked as a fault prediction model.
Preferably, when the numerical control machine tool works abnormally and the corresponding fault label is 1, a fault early warning signal is generated and sent to the intelligent terminal; when the numerical control machine tool works abnormally and the corresponding fault label is 0, a working abnormal signal is generated and sent to the intelligent terminal.
Preferably, when the numerical control machine tool works abnormally and the corresponding fault label is 0, the numerical control machine tool is adjusted according to preset machining parameters.
Preferably, the simulation of the numerical control machine tool according to a set program is carried out through simulation software; the simulation software comprises Sporto numerical control simulation software, astronon numerical control Machining simulation software, astronavigation numerical control simulation software, Machining, VERICUT, VNUC numerical control Machining simulation software and SmarNC.
Preferably, the set program is an operation program set by a worker for the numerical control machine tool.
Preferably, the processor is respectively in communication and/or electrical connection with the data acquisition module and the control execution module.
Preferably, the intelligent terminal comprises a smart phone, a tablet computer and a notebook computer.
Compared with the prior art, the invention has the beneficial effects that:
the method comprises the steps of judging whether the operation of the numerical control machine tool is abnormal or not by comparing and analyzing a processing parameter simulation curve and a processing parameter real-time curve, acquiring a corresponding fault prediction label through a fault prediction model when the operation of the numerical control machine tool is abnormal, and adjusting and early warning the numerical control machine tool by combining a comparison analysis result and the fault label; above-mentioned scheme starts from the simulation, monitors actual operation to fall into the different grade type with the unusual of digit control machine tool, adjust through predetermineeing processing parameter when the operation is unusual, then direct stall and early warning during the operational failure divide the abnormal state of digit control machine tool clearly and definitely, avoid meetting unusual just automatic shutdown, help improving the work efficiency of digit control machine tool.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the principles of the present invention;
fig. 2 is a schematic view of the working process of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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.
The terminology used herein is for the purpose of describing embodiments and is not intended to be limiting and/or limiting of the present disclosure; it should be noted that the singular forms "a," "an," and "the" include the plural forms as well, unless the context clearly indicates otherwise; also, although the terms first, second, etc. may be used herein to describe various elements, the elements are not limited by these terms, which are only used to distinguish one element from another.
The complexity and the intelligence of the current manufacturing equipment are continuously improved, and when the performance or the function of the equipment is improved, a series of problems can be brought to the reliability, the safety, the usability, the economy and the like of a system. In the aspect of monitoring the abnormality of the processing equipment, the existing scheme can only compare the detected data with the existing standard data, the detection mode is single, and the accuracy is not high; faults or abnormalities are often detected due to some additional unimportant factors, so that the working efficiency of the numerical control machine tool is not high.
Referring to fig. 1-2, the present application discloses a machining control system, which includes a plurality of numerically controlled machines and a monitoring system for controlling the plurality of numerically controlled machines. The monitoring system mainly aims to monitor the numerical control machine tool, judge the abnormal state of the numerical control machine tool in real time according to the result and adopt different processing modes according to the abnormal state, and is worthy of notice.
The monitoring system is in communication and/or electrical connection with a plurality of numerical control machines; the monitoring system comprises a data acquisition module, a processor and a control execution module; the processor is also in communication and/or electrical connection with the intelligent terminal. The intelligent terminal in the embodiment mainly comprises the intelligent mobile phone, the tablet personal computer and the notebook personal computer, and the intelligent terminal can acquire and display the working state of the numerical control machine tool, and can receive abnormity or fault early warning at the first time to ensure that a worker can timely handle the abnormity or fault early warning.
The general idea of the application is that firstly, simulation is carried out through a processor according to a program, when a simulation result meets requirements, actual processing is carried out, and parameters in the actual working parameters and the simulation process are compared to judge the working state of the numerical control machine tool.
In this embodiment, the processor simulates the machining process of the numerical control machine tool by combining the preset machining parameters, the setting program and the simulation software, and obtains a machining parameter simulation curve corresponding to the preset machining parameters. The preset processing parameters are set according to the processing requirements, and the simulation software comprises one of Spowo numerical control simulation software, astronon numerical control processing simulation software, astronavigation numerical control simulation software, Machining, VERICUT, VNUC numerical control processing simulation software and SmarNC.
In this embodiment, the data acquisition module acquires real-time processing parameters of the numerical control machine tool when the numerical control machine tool is processed according to a set program, and acquires a real-time curve of the processing parameters by combining a polynomial fitting method. The data acquisition module is connected with the collection sensor communication and/or electrical connection, and the collection sensor in this embodiment includes speed sensor, temperature sensor, humidity transducer and angle sensor.
The parameter contents of the preset processing parameter and the real-time processing parameter in the embodiment are consistent, and both comprise speed, angle and position; therefore, the corresponding processing parameter simulation curves include a speed simulation curve, an angle simulation curve and a position simulation curve, and the processing parameter real-time curves include a speed real-time curve, an angle real-time curve and a position real-time curve.
The embodiment realizes the preliminary judgement to the numerical control machine tool abnormity through the comparison of the machining parameter simulation curve and the machining parameter real-time curve, and the preliminary judgement includes:
under the condition of ensuring the initial time to be consistent, randomly selecting 10 times, respectively obtaining corresponding parameter values of the 10 times in the processing parameter simulation curve and the processing parameter real-time curve, and respectively marking the parameter values as a simulation parameter sequence and a real-time parameter sequence;
extracting the simulation speed, the simulation angle and the simulation position in the simulation parameter sequence, and extracting the real-time speed, the real-time angle and the real-time position in the real-time parameter sequence; the simulation speed and the real-time speed are a group, and linear fitting is carried out to obtain a speed fitting curve; the simulation angle and the real-time angle are a group, and linear fitting is carried out to obtain an angle fitting curve; and (4) the simulation position and the real-time position are a group, and linear fitting is carried out to obtain a position fitting curve.
When the slopes of the speed fitting curve, the angle fitting curve and the position fitting curve can meet the corresponding slope requirements, judging that the numerical control machine works normally; otherwise, judging whether the numerical control machine tool is abnormal in work, and judging whether the numerical control machine tool fails through a failure prediction model.
The slope requirement in the above description is a range, such as [ 1-a, 1+ a ], a is a real number greater than 0 and smaller than 1, or [ 1-a, 1+ β ], a and β are both real numbers greater than 0 and smaller than 1.
Above-mentioned scheme is only to judge whether normal is the operating condition of digit control machine tool, therefore when the operating condition of digit control machine tool is unusual, still need further judgement, obtains the trouble label through the failure prediction model promptly, includes:
acquiring environmental parameters of the numerical control machine tool during working in real time; the environmental parameters in this embodiment include temperature and humidity;
splicing the environment parameters and the real-time processing parameters to generate initial parameters, and inputting the initial parameters to a fault prediction model to obtain target parameters; the target parameter is a fault label corresponding to the initial parameter; when the fault label is 1, indicating that the corresponding numerical control machine tool has a fault; and when the fault label is 0, the corresponding numerical control machine tool is only in abnormal working state and does not have fault. The abnormal working state of the numerical control machine tool in the embodiment means that the normal machining of the numerical control machine tool is not affected, so that the fault is not determined by the method and the device.
The fault prediction model in this embodiment may be an intelligent model, or may be a fusion of several intelligent models, specifically including one or more of an error inverse feedback neural network, an RBF neural network, and a deep convolution neural network; the training of the artificial intelligence model comprises the following steps:
selecting standard training data; the standard training data in the embodiment comprises real-time working parameters and environment parameters corresponding to the numerical control machine tool in fault and real-time working parameters and environment parameters corresponding to the numerical control machine tool in normal operation, and the model precision can be improved only if the quantity type and the data volume are ensured;
setting a fault label for standard training data; constructing an artificial intelligence model; and standard training data is used as the input of the artificial intelligence model, the corresponding fault label is used as the output of the artificial intelligence model to finish the training, testing and checking of the artificial intelligence model, and the trained artificial intelligence model is marked as a fault prediction model.
According to the method, double verification is adopted, when the numerical control machine tool works abnormally and the corresponding fault label is 1, a fault early warning signal is generated and sent to the intelligent terminal; when the numerical control machine tool works abnormally and the corresponding fault label is 0, a working abnormal signal is generated and sent to the intelligent terminal. And when the numerical control machine tool works abnormally and the corresponding fault label is 0, adjusting the numerical control machine tool according to preset machining parameters. The method can ensure comprehensive monitoring of the numerical control machine tool and avoid the efficiency reduction of the numerical control machine tool caused by some unimportant factors. When the problem that numerical control machine tool appears is less, can adjust numerical control machine tool in real time according to predetermineeing the processing parameter, when great trouble appears in numerical control machine tool, then stop the operation of numerical control machine tool and early warning.
The working principle of the invention is as follows:
the processor simulates the machining process of the numerical control machine tool by combining preset machining parameters and a set program, and obtains a machining parameter simulation curve; and acquiring real-time processing parameters of the numerical control machine tool during processing according to a set program through a data acquisition module, and acquiring a real-time curve of the processing parameters.
Under the condition of ensuring the initial time to be consistent, randomly selecting N times, respectively obtaining corresponding parameter values of the N times in the processing parameter simulation curve and the processing parameter real-time curve, and respectively marking the parameter values as a simulation parameter sequence and a real-time parameter sequence; extracting corresponding parameters in the simulation parameter sequence and the implementation parameter sequence, and respectively fitting to generate a speed fitting curve, an angle fitting curve and a position fitting curve; when the slopes of the speed fitting curve, the angle fitting curve and the position fitting curve can meet the corresponding slope requirements, judging that the numerical control machine works normally; otherwise, judging whether the numerical control machine tool is abnormal in work, and judging whether the numerical control machine tool fails through a failure prediction model.
Acquiring environmental parameters of the numerical control machine tool during working in real time; splicing the environment parameters and the real-time processing parameters to generate initial parameters, and inputting the initial parameters to a fault prediction model to obtain a fault label; when the numerical control machine tool works abnormally and the corresponding fault label is 1, generating a fault early warning signal and sending the fault early warning signal to the intelligent terminal; when the numerical control machine tool works abnormally and the corresponding fault label is 0, a working abnormal signal is generated and sent to the intelligent terminal, and meanwhile, the numerical control machine tool is adjusted according to preset machining parameters.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (10)
1. A machining control system comprises a plurality of numerical control machines and a monitoring system for controlling the numerical control machines, and is characterized in that the monitoring system is in communication and/or electrical connection with the numerical control machines; the monitoring system comprises a data acquisition module, a processor and a control execution module; the processor is also in communication and/or electrical connection with the intelligent terminal;
the processor simulates the machining process of the numerical control machine tool by combining preset machining parameters and a set program, and obtains a machining parameter simulation curve;
acquiring real-time processing parameters of the numerical control machine tool during processing according to a set program through the data acquisition module, and acquiring a real-time curve of the processing parameters;
comparing and analyzing the machining parameter simulation curve and the machining parameter real-time curve to obtain an analysis result, combining the analysis result with a fault prediction model to obtain a fault label of the numerical control machine tool, and performing early warning according to the fault label; the value of the fault label is 0 or 1;
and the control execution module adjusts the numerical control machine according to the analysis result and the fault label.
2. A machine tool control system as claimed in claim 1, wherein the predetermined process parameters and the real-time process parameters have the same parameter content, including speed, angle and position.
3. A machining control system according to claim 2, wherein the machining parameter simulation curve includes a speed simulation curve, an angle simulation curve, and a position simulation curve, the independent variable of the machining parameter simulation curve is time, and the numerically controlled machine tool can perform machining of the actual workpiece only after the machining parameter simulation curve meets the requirement.
4. The machining control system according to claim 2, wherein the real-time processing parameter curve includes a real-time speed curve, a real-time angle curve, and a real-time position curve, and the real-time processing parameter curve is obtained by real-time fitting of data acquired by the data acquisition module in real time in combination with a polynomial fitting method.
5. A machining control system according to claim 3 or 4, wherein the analysis of the simulation profile of the machining parameters and the real-time profile of the machining parameters comprises:
under the condition of ensuring the initial time to be consistent, randomly selecting N times, respectively obtaining corresponding parameter values of the N times in the processing parameter simulation curve and the processing parameter real-time curve, and respectively marking the parameter values as a simulation parameter sequence and a real-time parameter sequence; wherein N is an integer greater than 2;
extracting corresponding parameters in the simulation parameter sequence and the implementation parameter sequence, and respectively fitting to generate a speed fitting curve, an angle fitting curve and a position fitting curve;
when the slopes of the speed fitting curve, the angle fitting curve and the position fitting curve can meet the corresponding slope requirements, judging that the numerical control machine works normally; otherwise, judging whether the numerical control machine tool is abnormal in work, and judging whether the numerical control machine tool fails through a failure prediction model.
6. A machine tool control system as claimed in claim 5, wherein obtaining a fault signature from the fault prediction model comprises:
acquiring environmental parameters of the numerical control machine tool during working in real time; the environmental parameters include temperature and humidity;
splicing the environment parameters and the real-time processing parameters to generate initial parameters, and inputting the initial parameters to a fault prediction model to obtain target parameters; and the target parameter is the fault label corresponding to the initial parameter.
7. A machine tool control system as claimed in claim 6, wherein the obtaining of the fault prediction model comprises:
selecting standard training data; the standard training data comprises real-time working parameters and environment parameters corresponding to the numerical control machine tool in fault and real-time working parameters and environment parameters corresponding to the numerical control machine tool in normal operation;
setting a fault label for standard training data;
constructing an artificial intelligence model; the artificial intelligence model comprises one or more of an error inverse feedback neural network, an RBF neural network and a deep convolutional neural network;
and standard training data is used as the input of the artificial intelligence model, the corresponding fault label is used as the output of the artificial intelligence model to finish the training, testing and checking of the artificial intelligence model, and the trained artificial intelligence model is marked as a fault prediction model.
8. The machining control system according to any one of claim 6, wherein when the numerically-controlled machine tool is abnormal and the corresponding fault label is 1, a fault early warning signal is generated and sent to the intelligent terminal; when the numerical control machine tool works abnormally and the corresponding fault label is 0, a working abnormal signal is generated and sent to the intelligent terminal.
9. A machine tool control system as claimed in claim 1, wherein the numerical control machine is adjusted according to predetermined machining parameters when the numerical control machine is out of order and the corresponding fault flag is 0.
10. A machining control system according to claim 1, wherein said setting program is an operation program set by a worker for the numerical control machine.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114442555A (en) * | 2022-01-04 | 2022-05-06 | 广东海思智能装备有限公司 | Electrical control system of numerical control machine tool |
| CN114460901A (en) * | 2022-01-04 | 2022-05-10 | 广州佳盟子机床有限公司 | Data acquisition system of numerical control machine tool |
| CN114995291A (en) * | 2022-07-18 | 2022-09-02 | 深圳市嘉鑫精密智造有限公司 | Numerical control machine tool control system and control method |
| CN116184930A (en) * | 2023-03-22 | 2023-05-30 | 中科航迈数控软件(深圳)有限公司 | Fault prediction method, device, equipment and storage medium for numerical control machine tool |
| CN116224930A (en) * | 2023-01-17 | 2023-06-06 | 扬州市职业大学(扬州开放大学) | Processing control method and system for numerically controlled grinder product |
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