CN113552840B - Machining control system - Google Patents
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- CN113552840B CN113552840B CN202110872871.2A CN202110872871A CN113552840B CN 113552840 B CN113552840 B CN 113552840B CN 202110872871 A CN202110872871 A CN 202110872871A CN 113552840 B CN113552840 B CN 113552840B
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- 238000003754 machining Methods 0.000 title claims abstract description 30
- 238000012545 processing Methods 0.000 claims abstract description 72
- 238000004088 simulation Methods 0.000 claims abstract description 48
- 230000002159 abnormal effect Effects 0.000 claims abstract description 20
- 238000012544 monitoring process Methods 0.000 claims abstract description 16
- 238000000034 method Methods 0.000 claims description 19
- 238000012549 training Methods 0.000 claims description 16
- 238000004458 analytical method Methods 0.000 claims description 9
- 238000013528 artificial neural network Methods 0.000 claims description 8
- 238000004891 communication Methods 0.000 claims description 8
- 230000007613 environmental effect Effects 0.000 claims description 7
- 238000012360 testing method Methods 0.000 claims description 3
- 230000005856 abnormality Effects 0.000 abstract description 6
- 238000001514 detection method Methods 0.000 abstract description 4
- 230000001276 controlling effect Effects 0.000 abstract 1
- 230000001105 regulatory effect Effects 0.000 abstract 1
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
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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 existing scheme is incomplete in fault detection range, and the abnormal state of a numerical control machine tool is not clearly divided, so that proper regulation and control cannot be performed according to the abnormal state; the system comprises a plurality of numerical control machine tools and a monitoring system for controlling the numerical control machine tools, wherein the monitoring system comprises a data acquisition module, a processor and an execution control module; according to the invention, whether the operation of the numerical control machine tool is abnormal or not is judged by comparing and analyzing the processing parameter simulation curve and the processing parameter real-time curve, the actual operation is monitored from the simulation, the abnormality of the numerical control machine tool is divided into different types, the operation is regulated by the preset processing parameters when the operation is abnormal, the operation is directly stopped and early-warned when the operation is abnormal, the abnormal state of the numerical control machine tool is definitely divided, the automatic stop when the abnormality is avoided, and the improvement of the working efficiency of the numerical control machine tool is facilitated.
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 intelligent degree of manufacturing equipment are continuously improved, however, due to the complexity of the structure of the complex equipment, a series of difficulties are brought to the aspects of reliability, safety, availability, economy and the like of a system when the performance or the function of the complex equipment are improved, the potential possibility of failure or failure of the system is also increased, so that the machining process is monitored in real time, and the operation safety and the machining efficiency of the system can be ensured by timely correcting according to the monitoring result.
The prior proposal (publication number is CN 11139944A) acquires and processes data of certain working state data in the machining process of the machine tool, and compares the actual characteristic parameters with normal values so as to master the actual working state of the machine tool, thereby achieving the purposes of fault diagnosis and state prediction.
The scheme judges the fault state of the machine tool through comparing the actual characteristic parameters with normal values, but in the actual operation process, the detection mode can not timely and comprehensively monitor the fault state of the machine tool; therefore, there is a need for a control system that can fully monitor and feed back in real time during machining.
Disclosure of Invention
The invention provides a machining control system which is used for solving the technical problems that the existing scheme is incomplete in fault detection range and is not clear in abnormal state division of a numerical control machine tool, so that proper regulation and control cannot be carried out according to abnormal states.
The aim of the invention can be achieved by the following technical scheme: a mechanical processing control system comprises a plurality of numerical control machine tools and a monitoring system for controlling the numerical control machine tools;
the monitoring system is in communication and/or electrical connection with a plurality of numerical control machine tools; 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 is combined with preset processing parameters and a set program to simulate the processing process of the numerical control machine tool, and a processing parameter simulation curve is obtained;
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 processing parameter simulation curve and the processing parameter real-time curve to obtain an analysis result, combining the analysis result and the fault prediction model to obtain a fault label of the numerical control machine tool, and carrying out 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 content, and each of the parameters comprises speed, angle and position.
Preferably, the processing parameter simulation curve comprises a speed simulation curve, an angle simulation curve and a position simulation curve, the independent variable of the processing parameter simulation curve is time, and the numerical control machine tool can process the actual workpiece after the processing parameter simulation curve meets the requirement.
Preferably, the processing parameter real-time curve comprises a speed real-time curve, an angle real-time curve and a position real-time curve, and the processing parameter real-time curve is obtained by combining data acquired by a data acquisition module in real time with a polynomial fitting method in real time.
Preferably, the analysis of the process parameter simulation curve and the process parameter real-time curve includes:
under the condition of ensuring the consistency of initial moments, randomly selecting N moments, respectively acquiring corresponding parameter values of the N moments 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;
corresponding parameters in the simulation parameter sequence and the implementation parameter sequence are extracted, and fitting is carried out to generate a speed fitting curve, an angle fitting curve and a position fitting curve respectively;
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 tool works normally; otherwise, judging that the numerical control machine tool works abnormally, and judging whether the numerical control machine tool fails or not through a failure prediction model.
Preferably, obtaining the fault tag through the fault prediction model includes:
acquiring environmental parameters of the numerical control machine tool in real time during working; 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 into a fault prediction model to obtain target parameters; 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 comprise real-time working parameters and environment parameters corresponding to the failure of the numerical control machine tool and real-time working parameters and environment parameters corresponding to the normal operation of the numerical control machine tool;
setting a fault label for standard training data;
constructing an artificial intelligent model; the artificial intelligent model comprises one or more of an error reverse feedback neural network, an RBF neural network and a deep convolution neural network;
standard training data is used as input of the artificial intelligent model, corresponding fault labels are used as output of the artificial intelligent model to complete training, testing and checking of the artificial intelligent model, and the trained artificial intelligent model is marked as a fault prediction model.
Preferably, 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.
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 the preset processing parameters.
Preferably, the simulation of the numerical control machine tool according to a set program is performed by simulation software; the simulation software comprises a Si Wo Shukong simulation software, an astronomical Long Shukong machining simulation software, an astronomical numerical control simulation software, a Machining, VERICUT, VNUC numerical control machining simulation software and a SmarNC.
Preferably, the setting program is an operation program set by a worker for the numerical control machine tool.
Preferably, the processor is in communication and/or electrical connection with the data acquisition module, the control execution module, respectively.
Preferably, the intelligent terminal comprises a smart phone, a tablet personal computer and a notebook computer.
Compared with the prior art, the invention has the beneficial effects that:
according to the invention, whether the operation of the numerical control machine tool is abnormal or not is judged by comparing and analyzing the processing parameter simulation curve and the processing parameter real-time curve, when the operation of the numerical control machine tool is abnormal, a corresponding fault prediction label is obtained by a fault prediction model, and a comparison analysis result and the fault label are combined to regulate and early warn the numerical control machine tool; the scheme starts from simulation, monitors actual operation, divides the abnormality of the numerical control machine tool into different types, adjusts through preset processing parameters when the numerical control machine tool runs abnormally, directly stops running and gives early warning when the numerical control machine tool runs abnormally, divides the abnormal state of the numerical control machine tool into definite, avoids encountering the abnormality and automatically stops, and is beneficial to improving the working efficiency of the numerical control machine tool.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the present invention;
fig. 2 is a schematic diagram of the workflow of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terminology used herein is for the purpose of describing embodiments and is not intended to limit and/or restrict the disclosure; it should be noted that the singular forms "a", "an" and "the" include plural forms as well, unless the context clearly indicates otherwise; moreover, although the terms "first," "second," etc. may be used herein to describe various elements, the elements are not limited by these terms, and these terms are merely used to distinguish one element from another element.
The complexity and the intelligent degree of the current manufacturing equipment are continuously improved, and a series of difficulties are brought to the aspects of reliability, safety, availability, economy and the like of the system when the performance or the functions of the equipment are improved. In the aspect of abnormality monitoring of processing equipment, the existing scheme can only compare detected data with existing standard data, the detection mode is single, and the accuracy is not high; often, faults or anomalies are detected by some additional unimportant factors, resulting in inefficient operation of the numerically controlled machine tool.
Referring to fig. 1-2, the present application discloses a machining control system, which includes a plurality of numerically-controlled machine tools and a monitoring system for controlling the plurality of numerically-controlled machine tools. The main purpose of this application is to monitor the digit control machine tool to judge the abnormal state of digit control machine tool in real time according to the result, take different processing methods according to abnormal state, it is notable that the monitored control system in this application can monitor and adjust many digit control machine tools simultaneously.
The monitoring system in the application is in communication and/or electrical connection with a plurality of numerical control machine tools; 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 this embodiment mainly includes smart mobile phone, panel computer and notebook computer, and intelligent terminal both can implement to acquire and show the operating condition of digit control machine tool, also can receive unusual or trouble early warning in the first time, guarantees that the staff can in time handle.
The general idea of the method is that firstly, simulation is carried out through a processor according to a program, when a simulation result meets the requirement, actual machining is carried out, and the actual working parameters are compared with parameters in the simulation process to judge the working state of the numerical control machine tool.
In this embodiment, the processor combines a preset machining parameter, a setting program and simulation software to simulate the machining process of the numerical control machine tool, and obtains a machining parameter simulation curve corresponding to the preset machining parameter. The preset processing parameters are set according to processing requirements, and the simulation software comprises one of a S Wo Shukong simulation software, a space Long Shukong processing simulation software, an aerospace numerical control simulation software, a Machining, VERICUT, VNUC numerical control processing simulation software and a SmarNC.
In this embodiment, the data acquisition module acquires real-time processing parameters of the numerical control machine tool during processing 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 in communication and/or electrical connection with an acquisition sensor, which in this embodiment comprises a speed sensor, a temperature sensor, a humidity sensor and an angle sensor.
The preset processing parameters and the real-time processing parameters in the embodiment have consistent parameter content, and comprise speed, angle and position; therefore, the corresponding processing parameter simulation curves comprise a speed simulation curve, an angle simulation curve and a position simulation curve, and the processing parameter real-time curves comprise a speed real-time curve, an angle real-time curve and a position real-time curve.
The embodiment realizes the preliminary judgment of the abnormality of the numerical control machine tool through the comparison of the processing parameter simulation curve and the processing parameter real-time curve, and comprises the following steps:
under the condition of ensuring the consistent initial time, 10 times are arbitrarily selected, corresponding parameter values of the 10 times in the processing parameter simulation curve and the processing parameter real-time curve are respectively obtained, and are respectively marked as a simulation parameter sequence and a real-time parameter sequence;
extracting a simulation speed, a simulation angle and a simulation position in a simulation parameter sequence, and extracting a real-time speed, a real-time angle and a real-time position in a real-time parameter sequence; the simulated speed and the real-time speed are a group, and linear fitting is carried out to obtain a speed fitting curve; the simulated angle and the real-time angle are a group, and linear fitting is carried out to obtain an angle fitting curve; the simulated 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 tool works normally; otherwise, judging that the numerical control machine tool works abnormally, and judging whether the numerical control machine tool fails or not through a failure prediction model.
The slope requirement in the above description is a range, for example, [ 1-alpha, 1+alpha ], alpha is a real number greater than 0 and less than 1, and may also be [ 1-alpha, 1+beta ], where alpha and beta are real numbers greater than 0 and less than 1.
The above scheme only judges whether the working state of the numerical control machine is normal, so when the working state of the numerical control machine is abnormal, the method further needs to judge, namely, obtains a fault label through a fault prediction model, and comprises the following steps:
acquiring environmental parameters of the numerical control machine tool in real time during working; 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 into a fault prediction model to obtain target parameters; the target parameters are fault labels corresponding to the initial parameters; when the fault label is 1, the corresponding numerical control machine tool is indicated to have faults; when the fault label is 0, the corresponding numerical control machine tool is only abnormal in working state and has no fault. In this embodiment, the abnormal working state of the numerically-controlled machine tool refers to a problem that the normal machining of the numerically-controlled machine tool is not affected, so the present application does not determine that the numerical-controlled machine tool is a fault.
The fault prediction model in the embodiment may be an intelligent model, or may be a fusion of several intelligent models, and specifically includes one or more of an error reverse feedback neural network, an RBF neural network and a deep convolutional neural network; training of artificial intelligence models includes:
selecting standard training data; the standard training data in the embodiment comprises real-time working parameters and environment parameters corresponding to the failure of the numerical control machine tool and real-time working parameters and environment parameters corresponding to the normal operation of the numerical control machine tool, and the model precision can be improved only if the quantity type and the data quantity are ensured;
setting a fault label for standard training data; constructing an artificial intelligent model; standard training data is used as input of the artificial intelligent model, corresponding fault labels are used as output of the artificial intelligent model to complete training, testing and checking of the artificial intelligent model, and the trained artificial intelligent model is marked as a fault prediction model.
In the method, double verification is adopted, and 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 the preset processing parameters. The method can ensure comprehensive monitoring of the numerical control machine tool and avoid efficiency reduction of the numerical control machine tool caused by some unimportant factors. When the problem of the numerical control machine tool is small, the numerical control machine tool can be adjusted in real time according to preset processing parameters, and when the numerical control machine tool has a large fault, the operation of the numerical control machine tool is stopped and early warning is performed.
The working principle of the invention is as follows:
the processor is combined with preset processing parameters and a set program to simulate the processing process of the digital machine tool, and a processing parameter simulation curve is obtained; 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 consistency of initial moments, randomly selecting N moments, respectively acquiring corresponding parameter values of the N moments 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; corresponding parameters in the simulation parameter sequence and the implementation parameter sequence are extracted, and fitting is carried out to generate a speed fitting curve, an angle fitting curve and a position fitting curve respectively; 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 tool works normally; otherwise, judging that the numerical control machine tool works abnormally, and judging whether the numerical control machine tool fails or not through a failure prediction model.
Acquiring environmental parameters of the numerical control machine tool in real time during working; splicing the environment parameters and the real-time processing parameters to generate initial parameters, and inputting the initial parameters into 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 processing parameters.
In the description of the present specification, the descriptions of the terms "one embodiment," "example," "specific example," and the like, 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 present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. 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 illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (6)
1. The machining control system comprises a plurality of numerical control machine tools and a monitoring system for controlling the numerical control machine tools, and is characterized in that the monitoring system is in communication and/or electrical connection with the numerical control machine tools; 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 is combined with preset processing parameters and a set program to simulate the processing process of the numerical control machine tool, and a processing parameter simulation curve is obtained;
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 processing parameter simulation curve and the processing parameter real-time curve to obtain an analysis result, combining the analysis result and the fault prediction model to obtain a fault label of the numerical control machine tool, and carrying out early warning according to the fault label; the value of the fault label is 0 or 1;
the control execution module adjusts the numerical control machine according to the analysis result and the fault label;
the preset processing parameters and the real-time processing parameters are consistent in parameter content and comprise speed, angle and position; 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 process an actual workpiece after the machining parameter simulation curve meets the requirement;
the processing parameter real-time curve comprises a speed real-time curve, an angle real-time curve and a position real-time curve, and the processing parameter real-time curve is obtained by combining data acquired by a data acquisition module in real time with a polynomial fitting method in real time;
the analysis of the process parameter simulation curve and the process parameter real-time curve includes:
under the condition of ensuring the consistency of initial moments, randomly selecting N moments, respectively acquiring corresponding parameter values of the N moments 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;
corresponding parameters in the simulation parameter sequence and the implementation parameter sequence are extracted, and fitting is carried out to generate a speed fitting curve, an angle fitting curve and a position fitting curve respectively;
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 tool works normally; otherwise, judging that the numerical control machine tool works abnormally, and judging whether the numerical control machine tool fails or not through a failure prediction model.
2. The machining control system of claim 1, wherein obtaining a fault signature from the fault prediction model comprises:
acquiring environmental parameters of the numerical control machine tool in real time during working; 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 into a fault prediction model to obtain target parameters; the target parameter is the fault label corresponding to the initial parameter.
3. The machining control system according to claim 2, wherein the obtaining of the failure prediction model includes:
selecting standard training data; the standard training data comprise real-time working parameters and environment parameters corresponding to the failure of the numerical control machine tool and real-time working parameters and environment parameters corresponding to the normal operation of the numerical control machine tool;
setting a fault label for standard training data;
constructing an artificial intelligent model; the artificial intelligent model comprises one or more of an error reverse feedback neural network, an RBF neural network and a deep convolution neural network;
standard training data is used as input of the artificial intelligent model, corresponding fault labels are used as output of the artificial intelligent model to complete training, testing and checking of the artificial intelligent model, and the trained artificial intelligent model is marked as a fault prediction model.
4. The machining control system according to claim 2, wherein 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.
5. The machining control system according to claim 1, wherein the numerical control machine is adjusted according to the preset machining parameters when the numerical control machine is abnormally operated and the corresponding failure flag is 0.
6. The machining control system according to claim 1, wherein the setting program is an operation program set by a worker for a numerical control machine tool.
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| CN114460901B (en) * | 2022-01-04 | 2022-10-04 | 广州佳盟子机床有限公司 | Data acquisition system of numerical control machine tool |
| CN114442555B (en) * | 2022-01-04 | 2022-10-11 | 广东海思智能装备有限公司 | Electrical control system of numerical control machine tool |
| CN114995291B (en) * | 2022-07-18 | 2023-05-05 | 深圳宏友金钻石工具有限公司 | Control system of numerical control machine tool |
| CN116224930B (en) * | 2023-01-17 | 2023-08-22 | 扬州市职业大学(扬州开放大学) | Processing control method and system for numerically controlled grinder product |
| CN116184930B (en) * | 2023-03-22 | 2023-07-21 | 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 |
| CN117555287B (en) * | 2024-01-12 | 2024-04-09 | 中国机械总院集团云南分院有限公司 | CAE-based numerical control machine tool machining dynamic performance monitoring method and system |
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