An adjustable numerical control program error-proof machining method, device, system and storage medium

Abstract

The application provides a kind of adjustable numerical control program error-proof machining method, equipment, system and storage medium, it is related to numerical control technical field, processing method includes: by simulation unit, the machining three-dimensional model of workpiece to be handled is established.Based on the machining three-dimensional model, determine initial tool path parameter.According to initial tool path parameter, simulation is carried out, and simulation result is obtained, which includes virtual allowance distribution.According to virtual allowance distribution, adjust initial tool path parameter to determine actual machining path parameter.According to actual machining path parameter, rough machining is carried out on workpiece to be handled, and the processed workpiece is obtained.The measured allowance of the processed workpiece is obtained, and the machining parameters of the numerical control system are determined based on the measured allowance according to the preset optimization algorithm, and the finishing machining is carried out according to the machining parameters.The application can identify potential problems in advance by establishing a three-dimensional model and simulation, reduce errors in the actual machining process, and improve the machining accuracy of the final product.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of numerical control, in particular to an adjustable numerical control program error-proof machining method, device, system and storage medium. BACKGROUND

[0002] With the continuous development of numerical control machining technology, the role of numerical control system in modern manufacturing industry is becoming more and more important. However, in the process of workpiece machining, the traditional numerical control system often relies on static preset path and parameters. When machining according to the preset path and parameters, although the online measurement unit can obtain the measured residual amount of the machined workpiece, when the required machining parameters are adjusted, it is usually necessary to stop, which reduces the efficiency of tool compensation and cutting parameter updating.

[0003] In addition, although the existing tool compensation mechanism can realize global adjustment, the adjustment accuracy for local positions is low, and the numerical control program needs to be modified many times to achieve the desired accuracy. However, frequent modification of the numerical control program not only increases the complexity of processing settings, but also reduces the overall processing efficiency. Moreover, this situation increases the need for manual intervention in batch processing, which may lead to errors and affect the consistency of processing and the quality of the final product. SUMMARY

[0004] The present application solves one or more of the above related technical problems.

[0005] To solve the above problems, the present application provides an adjustable numerical control program error-proof machining method, device, system and storage medium.

[0006] In a first aspect, the present application provides an adjustable numerical control program error-proof machining method applied to a numerical control system, wherein the numerical control system comprises a simulation unit, a control unit and an online measurement unit; the adjustable numerical control program error-proof machining method comprises:

[0007] establishing a machining three-dimensional model of a to-be-processed workpiece through the simulation unit; determining initial tool path parameters based on the machining three-dimensional model, and performing simulation simulation according to the initial tool path parameters to obtain a simulation result, wherein the simulation result comprises a virtual residual amount distribution;

[0008] adjusting the initial tool path parameters according to the virtual residual amount distribution to determine actual machining path parameters;

[0009] performing rough machining on the to-be-processed workpiece according to the actual machining path parameters through the control unit to obtain a processed workpiece;

[0010] obtaining a measured residual amount of the processed workpiece through the online measurement unit;

[0011] According to a preset optimization algorithm, the machining parameters of the numerical control system are determined based on the measured residual amount, and the control unit is used to finish machining the processed workpiece according to the machining parameters.

[0012] Optionally, the machining parameters include tool compensation data and cutting parameters; and the determination of the machining parameters of the numerical control system based on the measured residual amount according to the preset optimization algorithm includes:

[0013] Based on the simulation results and a preset threshold, a potential out-of-tolerance area is determined, and the processed workpiece is divided according to the potential out-of-tolerance area to obtain different machining areas.

[0014] Based on a preset genetic algorithm, tool compensation data and cutting parameters of the numerical control system are determined according to the measured residual amount of each machining area.

[0015] Optionally, the preset genetic algorithm processing process includes:

[0016] Initialize population parameters, including multiple individuals and corresponding individual parameter data;

[0017] Calculate the fitness data of the corresponding individual according to the measured residual amount of each machining area and the corresponding individual parameter data, and determine multiple parent individuals according to all the fitness data based on a preset roulette selection method;

[0018] Generate new individuals by crossing all the parent individuals, select a preset proportion of the new individuals for Gaussian mutation to obtain mutated individuals, and update the initial population parameters based on the mutated individuals;

[0019] Repeat the above process until a preset iteration number is reached or a stop condition is met to obtain the final target mutated individual and corresponding individual parameter data, which are the tool compensation data and the cutting parameters.

[0020] Optionally, the numerical control system further includes a feedback mechanism unit connected to the online measurement unit, and the adjustable numerical control program error-proof machining method further includes:

[0021] During the machining of the workpiece to be processed, the online measurement unit acquires tool wear parameters at a preset frequency, and the wear parameters include the machining parameters, material properties and environmental data;

[0022] The feedback mechanism unit inputs the wear parameters into a preset tool wear model to obtain tool predicted wear results;

[0023] Compare the tool predicted wear results with a set threshold;

[0024] adjust the machining parameters of the numerical control system when the tool wear prediction result is greater than the set threshold value.

[0025] Optionally, the construction process of the preset tool wear model comprises:

[0026] obtaining simulation machining parameters, simulation material characteristics, simulation environment data and corresponding label data of a simulation workpiece through simulation simulation experiments;

[0027] training and optimizing an original wear model through the simulation machining parameters, the simulation material characteristics, the simulation environment data and the corresponding label data, and taking the optimized original wear model as the tool wear model.

[0028] Optionally, the training and optimization of the original wear model through the simulation machining parameters, the simulation material characteristics, the simulation environment data and the corresponding label data, and taking the optimized original wear model as the tool wear model, comprises:

[0029] training the original wear model according to the simulation machining parameters, the simulation material characteristics and the simulation environment data to obtain a temporary tool wear result;

[0030] calculating the loss of the original wear model through a preset loss function according to the temporary tool wear result and the corresponding label data to obtain a loss function output;

[0031] adjusting the model parameters of the original wear model according to the loss function output, iteratively training until the loss function output meets a preset condition, and taking the original wear model after parameter adjustment as the tool wear model.

[0032] Optionally, the error-proof machining method of the adjustable numerical control program further comprises:

[0033] obtaining tool life data, machining time data and workpiece quality data, and determining corresponding temporary scores according to the tool life data, the machining time data and the workpiece quality data;

[0034] obtaining comprehensive score data based on each temporary score and corresponding preset weight data;

[0035] judging according to the comprehensive score and a preset score threshold to obtain a judgment result, and generating a recommended adjustment strategy of the machining parameters based on the judgment result.

[0036] Secondly, the present invention provides an adjustable CNC program error-proofing machining device, applied to a CNC system, wherein the CNC system includes a simulation unit, a control unit, and an online measurement unit, and the adjustable CNC program error-proofing machining device includes:

[0037] The processing module is used to establish a three-dimensional machining model of the workpiece to be processed through a simulation unit; determine the initial toolpath parameters based on the machining three-dimensional model; perform simulation based on the initial toolpath parameters to obtain simulation results, the simulation results including virtual allowance distribution; and adjust the initial toolpath parameters based on the virtual allowance distribution to determine the actual machining path parameters.

[0038] The processing module is used to perform rough processing on the workpiece to be processed according to the actual processing path parameters through the control unit to obtain the processed workpiece;

[0039] The processing module is also used to obtain the measured allowance of the processed workpiece through the online measurement unit;

[0040] The machining module is also used to determine the machining parameters of the CNC system based on the measured allowance according to a preset optimization algorithm, and to perform finishing machining on the processed workpiece according to the machining parameters through the control unit.

[0041] Thirdly, the present invention provides an adjustable CNC program error-proofing machining system, comprising a memory and a processor; the memory is used to store a computer program; the processor is used to implement the adjustable CNC program error-proofing machining method as described in the first aspect when the computer program is executed.

[0042] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the adjustable CNC program error-proofing machining method as described in the first aspect.

[0043] The beneficial effects of the adjustable CNC program error-proofing machining method, equipment, system, and storage medium of the present invention are:

[0044] The establishment of a three-dimensional model of the workpiece to be processed using the simulation unit in the numerical control system can accurately reflect the geometric characteristics and processing requirements of the workpiece. Based on the above-mentioned three-dimensional model, the initial tool path parameters are determined using algorithms or experience, which will guide the movement trajectory of the tool during processing. Then, simulation is performed according to the initial tool path parameters, and simulation results are obtained. These results include virtual allowance distribution information, which can help to understand the surface allowance state of the workpiece after processing, i.e., the thickness and distribution of the remaining material on the workpiece after the tool moves according to the set path. Based on the virtual allowance distribution in the simulation results, it is analyzed which areas have excessive allowance, and the initial tool path parameters are adjusted. This step aims to optimize the actual machining path to remove the allowance more efficiently and avoid redundant cutting.

[0045] Subsequently, the control unit performs rough machining on the workpiece to be processed according to the actual machining path parameters. This step aims to quickly remove a large amount of material to lay the foundation for subsequent finishing. At the same time, the online measurement unit is used to obtain the measured allowance of the processed workpiece. This measurement result can reflect the quality of the workpiece after actual processing.

[0046] Finally, based on the measured allowance, the corresponding machining parameters are calculated through a pre-set optimization algorithm. These parameters can be used to adjust the movement strategy of the tool to improve the machining precision. Therefore, the control unit performs finishing according to the determined machining parameters to ensure that the workpiece meets higher machining precision and quality requirements in the final stage.

[0047] In summary, the present application can identify potential problems in advance, reduce errors in the actual machining process, and improve the machining precision of the final product by establishing a three-dimensional model and performing simulation. At the same time, by combining real-time online measurement with parameter optimization, the numerical control system can more efficiently utilize materials, reduce waste, and reduce production costs during the machining process. The effective feedback mechanism can automatically adjust the tool parameters based on real-time data, flexibly respond to different processing requirements and unexpected situations. In addition, the entire process is highly automated, reducing the dependence on human intervention and reducing the likelihood of human error, improving production consistency and efficiency. By reasonably combining rough machining and finishing, the machining cycle is shortened, the overall production efficiency is improved, and the competitiveness of the enterprise is enhanced.

[0048] Therefore, the adjustable numerical control program error-proof machining method of the present application not only improves the machining quality and efficiency through advanced simulation and measurement technology, but also effectively reduces the invalid consumption of production, enhancing the overall flexibility and intelligence level of the system. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 Fig. 1 is a flowchart of an adjustable numerical control program error-proof machining method according to an embodiment of the present application;

[0050] Figure 2 A structure schematic diagram of a programmable NC program error-proof machining device according to an embodiment of the present application;

[0051] Figure 3 A structure schematic diagram of a programmable NC program error-proof machining system according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the above objectives, features and advantages of the present application more apparent, specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present application are shown in the drawings, it should be understood that the present application can be implemented in various forms, and should not be interpreted as being limited to the embodiments set forth herein, rather these embodiments are provided so as to more thoroughly and completely understand the present application. It should be understood that the drawings and embodiments of the present application are merely for illustrative purposes, and are not intended to limit the scope of protection of the present application.

[0053] It should be understood that each step recited in the method embodiments of the present application can be executed in different order, and / or in parallel. In addition, the method embodiments can include additional steps and / or omit the execution of the steps shown. The scope of the present application is not limited in this respect.

[0054] The term “comprising” and variations thereof as used herein are open-ended, that is, “comprising but not limited to”; the term “based on” is “based at least in part on”; the term “one embodiment” means “at least one embodiment”; the term “another embodiment” means “at least one additional embodiment”; the term “some embodiments” means “at least some embodiments”; the term “optional” means “optional in at least some embodiments”. Related definitions are given throughout the description. It should be noted that the concepts “first”, “second”, etc. mentioned in the present application are merely used to distinguish different devices, modules or units, and are not intended to limit the order or interdependence of the functions performed by these devices, modules or units.

[0055] It should be noted that the modification of “one” “multiple” mentioned in the present application is illustrative rather than limiting, and those skilled in the art should understand that, unless otherwise explicitly indicated in the context, it should be understood as “one or more”.

[0056] The names of the messages or information exchanged between the plurality of devices in the embodiments of the present application are merely for illustrative purposes, and are not intended to limit the scope of the messages or information.

[0057] In the related art, when formulating a tool path, traditional CNC systems often rely on experience and simple geometric models, with low precision. These systems have difficulty accurately predicting the distribution of virtual allowances during machining when facing tool wear or changes in workpiece material properties, which can lead to the formation of potential out-of-tolerance areas, seriously affecting the quality of the final workpiece. Especially in the field of finish machining, the total allowance (e.g. 0.6mm) reserved for two-tool cutting processes often cannot be evenly distributed due to factors such as differences in tool wear, material hardness fluctuations, etc., causing deviations in the allowance after single-tool cutting.

[0058] Secondly, the current related technology mainly relies on experience for adjustment, lacking systematic optimization and automatic feedback mechanism. This makes the machining process lack flexibility and adaptability, especially when facing complex workpieces and changing process requirements, it cannot dynamically adapt to machining deviations.

[0059] To solve the problems in the above related technology, the embodiment provides a kind of adjustable numerical control program error-proof machining method, equipment, system and storage medium.

[0060] As Figure 1 The adjustable numerical control program error-proof machining method provided by the embodiment of the application is applied to a numerical control system, and the numerical control system includes a simulation unit, a control unit and an online measurement unit. The adjustable numerical control program error-proof machining method includes the following steps.

[0061] Step S100, a machining three-dimensional model of a workpiece to be processed is established by a simulation unit; initial tool path parameters are determined based on the machining three-dimensional model, simulation is performed according to the initial tool path parameters, and a simulation result is obtained, the simulation result including a virtual allowance distribution.

[0062] Specifically, the embodiment can be applied to a numerical control system that uses tools for cutting and other machining processes. The simulation unit in the numerical control system is used to convert the geometric characteristics and machining requirements of the workpiece to be processed into a three-dimensional digital model. This process usually involves CAD (Computer Aided Design) software, which inputs the physical characteristics of the workpiece (such as shape, size, material properties, etc.) into the system.

[0063] Based on the established three-dimensional model, the shape and characteristics of the workpiece are analyzed by algorithm or empirical rule to determine the initial tool path parameters. These parameters will indicate the specific trajectory that the tool should follow during machining. According to the initial tool path parameters, the system performs simulation. This simulation process takes into account factors such as tool cutting action, machining speed, cutting depth, etc. to realistically reproduce the machining process.

[0064] The output of the simulation results includes virtual stock distribution information. This information shows the material removal status of each machining area during the preliminary simulation, indicating the remaining material amount after the tool reaches each area. The virtual stock distribution is presented in graphical or data form, usually representing the stock thickness of different areas, which can be visualized using color coding, heat maps, etc. This distribution information can intuitively show which part of the workpiece needs further processing, reflecting the potential over-difference areas in the machining process.

[0065] By analyzing the virtual stock distribution, the tool path can be effectively optimized, and the machining parameters such as tool path introduction and feed rate can be adjusted to ensure efficient removal of stock in actual machining, avoiding excessive tool wear or workpiece quality problems.

[0066] In summary, virtual stock distribution is a prediction and analysis of the residual material condition of the workpiece during the simulation state and tool machining process, providing an important basis for optimizing subsequent machining path and parameters, thereby improving machining precision and efficiency.

[0067] By simulating the three-dimensional model before actual machining, the tool motion trajectory and cutting area of the workpiece can be more accurately predicted and verified, significantly reducing potential errors in actual machining. In addition, by analyzing the simulation results, potential problems such as excessive virtual stock distribution or tool collision areas can be identified in advance. This allows for optimization of the design before machining begins, thereby avoiding over-differences and defects in subsequent machining.

[0068] Virtual stock distribution provides the material removal status of each machining area, allowing the system to better plan the machining process, ensuring reasonable material utilization, reducing waste, and reducing production costs.

[0069] Real-time simulation can quickly adjust tool path parameters during the design phase without the need to actually change programs and equipment, greatly improving process flexibility and adaptability. Efficient simulation processes can accelerate development cycles, shorten the time from design to production, and enable enterprises to respond more quickly to market demand, thereby enhancing competitiveness.

[0070] In summary, the introduction of step S100 in numerical control machining not only enhances the controllability and accuracy of the machining process through simulation technology, but also effectively reduces risks and costs, improving overall production efficiency and flexibility.

[0071] Step S200, adjusting the initial tool path parameters based on the virtual stock distribution to determine the actual machining path parameters.

[0072] Specifically, first, a detailed analysis of the virtual stock distribution obtained in step S100 is required. This distribution reflects the material removal situation of each region during machining and shows which regions may have more residual material.

[0073] According to the virtual stock distribution, key regions and potential out-of-tolerance regions are identified. These regions need to be focused on during finishing to ensure that the machining quality meets the expected standards.

[0074] Based on the analysis results of the virtual stock, the initial tool path parameters are adjusted. This may include changing the cutting order, speed, depth, or cutting angle of the tool to more effectively remove material and reduce residual material. For example, for regions with larger virtual stock, the tool path can be adjusted so that the tool performs more cutting operations in these regions while reducing the tool's dwell time in other regions with smaller stock.

[0075] After completing the above adjustments, the new guide path parameters are converted into actual machining path parameters, preparing for the actual machining stage.

[0076] By adjusting the guide path parameters specifically, the virtual stock of each machining region can be effectively reduced, allowing the tool to contribute more cutting while ensuring that the surface quality of the final product meets the expected standards. In addition, by identifying potential out-of-tolerance regions and making corresponding adjustments, the likelihood of out-of-tolerance in actual machining can be reduced, which helps to improve product consistency and qualification rate. The adjusted machining path also promotes more uniform material removal, ensuring more reasonable material utilization and reducing ineffective material waste, thereby reducing production costs. Through accurate adjustment of the virtual stock, the tool can quickly and efficiently complete the machining task, thereby shortening the machining cycle and improving overall production efficiency.

[0077] The flexibility of this process enables the CNC system to adapt to different workpieces and machining requirements, improving the system's adaptability and intelligence level. Therefore, adjusting the initial tool path parameters based on the virtual stock distribution not only enhances the accuracy and efficiency of the machining process, but also effectively reduces the quality risk in production, optimizes the utilization of resources, and improves the overall production capacity and competitiveness.

[0078] Step S300, through the control unit, rough machining is performed on the workpiece to be processed according to the actual machining path parameters, obtaining a processed workpiece.

[0079] Specifically, first, the control unit receives the actual machining path parameters adjusted in step S200. These parameters have been optimized based on the virtual stock distribution to ensure that the tool can more effectively remove material.

[0080] The control unit sets the rough machining conditions such as the corresponding cutting tool, cutting speed, feed rate, and cutting depth during the preparation stage. These parameters need to be reasonably configured according to the material properties of the workpiece to be processed and design requirements to improve machining quality.

[0081] The control unit starts the machining equipment (such as a numerical control machine tool) and performs rough machining on the workpiece to be processed according to the set actual machining path parameters. In this step, the tool moves along the predetermined path to remove excess material on the surface of the workpiece and form a preliminary shape.

[0082] During rough machining, the system monitors the machining state in real time, such as the cutting force of the tool, the temperature and vibration of the workpiece, etc., to ensure the stability of the machining process. The control unit makes necessary dynamic adjustments based on feedback information to address potential machining problems.

[0083] After rough machining, a preliminary shaped workpiece is obtained, which lays the foundation for subsequent finishing.

[0084] Through reasonable design of actual machining path parameters, rough machining can quickly remove a large amount of material, thereby shortening the machining time and improving the efficiency of the overall production process. In addition, optimized path parameters help the tool to contact the workpiece surface in the best way, reducing unnecessary wear and tear, improving the service life of the tool, and reducing production costs. At the same time, through real-time monitoring of the machining process, the control unit can respond and adjust the machining parameters in a timely manner to ensure that the machining quality of the workpiece meets the design requirements and reduces the risk of defects.

[0085] Rough machining not only removes excess material but also lays a good foundation for subsequent finishing. This helps to improve the efficiency and effectiveness of finishing and ensures that the final product meets higher quality standards.

[0086] In summary, the introduction of step S300 through optimized actual machining path parameters for rough machining significantly improves machining efficiency and quality while reducing costs, enhancing the stability and reliability of the production process.

[0087] Step S400: Obtain the measured allowance of the processed workpiece through the online measurement unit.

[0088] Specifically, the online measurement unit is integrated into the numerical control machining system and is designed to monitor and evaluate the machining state of the workpiece in real time. This unit is equipped with high-precision sensors and measurement equipment that can quickly and accurately obtain the geometric information of the workpiece.

[0089] After rough machining is completed, the control unit starts the online measurement unit to ensure that it is in normal working condition and the sensors have been calibrated and are ready for workpiece measurement.

[0090] The online measurement unit scans the surface of the processed workpiece in real time using laser, tactile probe or other measurement techniques, and obtains the geometric data of the workpiece. The system compares the measurement results with the design specifications and calculates the actual material remaining amount, i.e. the actual measurement allowance.

[0091] The measured measurement allowance data obtained by measurement is transmitted to the control unit in real time for data processing and analysis. The system identifies potential out-of-tolerance areas or unprocessed areas based on the measured measurement allowance and the preset standard, and generates corresponding feedback information.

[0092] The control unit can adjust the subsequent processing parameters according to the real-time measurement data to ensure that the final processing quality of the workpiece meets the design requirements.

[0093] By obtaining the actual measurement allowance in real time, engineers can timely understand the processing state of the workpiece, so as to adjust the processing process and improve the overall accuracy of the part. At the same time, the online measurement technology provides continuous monitoring of the processing process, making the processing process more controllable and reducing potential processing errors, thereby improving the yield. In addition, the implementation of real-time measurement shortens the feedback time from measurement to adjustment, which can optimize the processing process faster and improve the response speed compared with the traditional offline measurement method.

[0094] By accurately grasping the actual measurement allowance, unprocessed areas can be identified in time to avoid subsequent processing waste caused by insufficient processing, and the utilization of materials can be optimized. Real-time data of actual measurement allowance makes the adjustment of tools and processing strategies more scientific and reasonable, thereby improving production efficiency and shortening the overall production cycle.

[0095] In summary, by obtaining the actual measurement allowance of the processed workpiece through the online measurement unit, not only the processing accuracy and production efficiency can be improved, but also the controllability of the processing process can be enhanced, and the material waste can be reduced. This process has important application value in modern numerical control machining.

[0096] Step S500, according to the preset optimization algorithm, based on the actual measurement allowance, the processing parameters of the numerical control system are determined, and the control unit is used to perform finish machining on the processed workpiece according to the processing parameters.

[0097] Specifically, a preset optimization algorithm is introduced, which uses the actual measurement allowance as input data to analyze the actual processing state of the workpiece and make appropriate adjustment suggestions for the processing parameters.

[0098] The previously acquired measured stock data is input into the optimization algorithm. This algorithm evaluates the machining requirements of each region by comparing the actual stock with the design standard, such as determining which areas require more material removal and adjusting parameters such as cutting speed, feed rate, cutting depth, etc. The optimization algorithm automatically generates optimal machining parameters based on the analysis results. These parameters aim to maximize material removal rate while ensuring that the workpiece quality is within an acceptable range. After the control unit receives the optimized machining parameters, it prepares the cutting tool and machining equipment to ensure that it meets the specified finishing requirements.

[0099] During the finishing process, the control unit activates the machining equipment and performs finishing on the processed workpiece based on the calculated optimal machining parameters. During this process, the tool efficiently removes material according to the set path to achieve higher surface quality and precision. At the same time, the system continuously monitors the machining state during finishing and adjusts the machining parameters in real time based on real-time feedback to ensure smooth machining and achieve the expected results.

[0100] Using measured stock-based data input optimization algorithm, customized parameters are provided for each machining area, thereby improving the overall accuracy and surface quality of the workpiece. By accurately analyzing the measured stock, the optimization algorithm can effectively reduce material waste, ensure that each machining process fully utilizes materials, and reduce production costs. In addition, through the implementation of optimized parameters, the tool can more efficiently perform cutting tasks, reduce redundant cutting, shorten machining time, and thus improve overall production efficiency. At the same time, real-time monitoring and adjustment functions allow the control unit to flexibly respond to changes during the machining process, ensuring that the machining process is more controllable and reducing the incidence of faults and errors.

[0101] In summary, by determining the machining parameters of the numerical control system according to the preset optimization algorithm and the measured stock for finishing, the machining precision, production efficiency and material utilization rate are significantly improved, and the controllability of the machining process is enhanced, providing a more intelligent solution for modern numerical control machining.

[0102] In this embodiment, the simulation unit in the numerical control system is used to establish a three-dimensional model of the workpiece to be processed, which can accurately reflect the geometric characteristics and processing requirements of the workpiece. Based on the above-mentioned three-dimensional model, the initial tool path parameters are determined by using algorithms or experience, which will guide the movement trajectory of the tool during processing. Then, simulation is carried out according to the initial tool path parameters, and simulation results are obtained. These results include virtual allowance distribution information, which can help to understand the surface allowance state of the workpiece after processing, that is, the thickness and distribution of the remaining material on the workpiece after the tool moves according to the set path. According to the virtual allowance distribution in the simulation results, it is analyzed which areas have excessive allowance, and the initial tool path parameters are adjusted. This step aims to optimize the actual machining path to remove the allowance more efficiently and avoid redundant cutting.

[0103] Subsequently, the control unit performs rough machining on the workpiece to be processed according to the actual machining path parameters, which aims to quickly remove a large amount of material and lay the foundation for subsequent finishing. At the same time, the online measurement unit is used to obtain the measured allowance of the processed workpiece, which can reflect the quality of the workpiece after actual processing.

[0104] Finally, based on the measured allowance, the corresponding machining parameters are calculated by using the preset optimization algorithm. These parameters can be used to adjust the movement strategy of the tool to improve the machining precision. Therefore, the control unit performs finishing according to the determined machining parameters to ensure that the workpiece meets higher machining precision and quality requirements in the final stage.

[0105] In summary, by establishing a three-dimensional model and simulation, potential problems can be identified in advance, and errors in the actual machining process can be reduced, thereby improving the machining precision of the final product. At the same time, by combining real-time online measurement with parameter optimization, the numerical control system can more efficiently utilize materials during processing, reduce waste, and reduce production costs. The effective feedback mechanism can automatically adjust the tool parameters based on real-time data, flexibly respond to different processing requirements and unexpected situations. In addition, the entire process is highly automated, reducing the dependence on human intervention and reducing the likelihood of human error, improving production consistency and efficiency. By reasonably combining rough machining and finishing, the machining cycle is shortened, the overall production efficiency is improved, and the competitiveness of the enterprise is enhanced.

[0106] Therefore, the adjustable numerical control program error-proof machining method not only improves the machining quality and efficiency through advanced simulation and measurement technology, but also effectively reduces the invalid consumption of production, enhances the overall flexibility and intelligent level of the system.

[0107] Optionally, the machining parameters include tool compensation data and cutting parameters; and the determining the machining parameters of the numerical control system based on the measured allowance according to the preset optimization algorithm comprises:

[0108] Based on the simulation results and a preset threshold, a potential tolerance deviation area is determined, and the processed workpiece is divided according to the potential tolerance deviation area, to obtain different machining areas.

[0109] Based on a preset genetic algorithm, tool compensation data and cutting parameters of the numerical control system are determined according to the measured residual amount of each machining area.

[0110] Optionally, the preset genetic algorithm processing process includes:

[0111] Initializing population parameters, the population parameters including multiple individuals and corresponding individual parameter data;

[0112] Calculating the fitness data of the corresponding individual according to the measured residual amount of each machining area and the corresponding individual parameter data, and determining multiple parent individuals according to all the fitness data based on a preset roulette selection method;

[0113] Generating new individuals by crossing all the parent individuals, selecting a preset proportion of the new individuals for Gaussian mutation to obtain mutated individuals, and updating the initial population parameters based on the mutated individuals;

[0114] Repeating the above process until a preset iteration number is reached or a stop condition is met, to obtain final target mutated individuals and corresponding individual parameter data, the individual parameter data including the tool compensation data and the cutting parameters.

[0115] Specifically, the machining parameters include tool compensation data (mainly including X / Z axis offset, used to correct the size deviation caused by tool wear or machining error) and cutting parameters (such as cutting speed, feed rate and cutting depth, etc.).

[0116] Based on the simulation results and a preset threshold, the actual machining state of the workpiece is analyzed, and the area that may exceed the size tolerance is identified. This step is the key to ensure the quality of the finished product, and can find possible problems in the machining process in advance.

[0117] According to the identified potential tolerance deviation area, the processed workpiece is divided into multiple machining areas. These areas will adopt different machining strategies according to their different residual materials (measured residual amount), which can realize more efficient and accurate subsequent machining.

[0118] Based on the measured residual amount of each machining area, a preset genetic algorithm is applied to determine the tool compensation data and the cutting parameters, to optimize the machining process. Genetic algorithm simulates the process of natural selection, and finds the optimal solution through iteration.

[0119] The preset genetic algorithm processing process includes: initializing population parameters: at the beginning of the genetic algorithm, initializing population parameters. These parameters include multiple "individuals" and corresponding individual parameter data, which represent possible combinations of tool compensation and cutting parameters.

[0120] Based on the measured tolerances of each machining area and the corresponding individual parameter data, the fitness data of each individual is calculated. Individuals with high fitness represent that their parameter configurations can better meet the machining requirements.

[0121] Using the preset roulette selection method, multiple parent individuals are determined based on all fitness data. The higher the fitness of an individual, the greater the probability of being selected as a parent, to ensure that good features are inherited.

[0122] All parent individuals are subjected to crossover operation to generate new individuals of the next generation. This step simulates gene recombination in biology and may combine the advantages of the parents. In the generated new individuals, a portion of the individuals are selected for Gaussian mutation according to a preset proportion. This process introduces randomness, aiming to explore the parameter space and avoid the algorithm falling into a local optimal solution.

[0123] Based on the mutated individuals, the initialized population parameters are updated to prepare for the next iteration. Through continuous iteration and updating, the overall fitness of the population is improved.

[0124] The above process is repeated until a preset number of iterations is reached or a stop condition is met, and the final target mutated individual and its corresponding individual parameter data will be the best tool compensation and cutting parameters. The stop condition can be set according to actual conditions and may include, for example, changes in the fitness of the population, solutions that meet certain targets, time limits, etc. For example: if the fitness of the population changes by less than a preset threshold (such as 0.01) in a certain number of consecutive iterations, the algorithm stops. This indicates that the algorithm has approached a stable state and further iterations may not significantly improve the quality of the solution. For example, if the fitness of the found individual reaches a preset target value (e.g., meets a certain machining precision), the algorithm can stop early. For example, a maximum running time (e.g., 10 minutes) can be set. When the time limit is reached, even if the preset number of iterations has not been reached, the algorithm will stop, etc.

[0125] By identifying and processing potential tolerance areas, tool compensation data and cutting parameters can be better adjusted to improve the overall machining accuracy of the workpiece. At the same time, by dividing the machining area and applying different machining strategies according to the measured tolerances, the material is more reasonably utilized, reducing material waste and production costs. In addition, the preset genetic algorithm can find parameter settings close to the global optimal solution in a larger parameter space through continuous selection, crossover and mutation, thereby improving machining efficiency and quality.

[0126] The real-time feedback and data-driven optimization strategy can flexibly respond to different workpieces and machining conditions, and performs well in the flexibility of production lines. By adjusting the tool compensation data and cutting parameters in a timely manner, the machining failure rate caused by improper parameter settings is reduced, and the qualified product rate is improved. The optimized machining parameters enable the tool to complete the cutting task more effectively, thereby shortening the overall machining cycle, improving production efficiency, and enhancing the competitiveness of enterprises.

[0127] In summary, through the machining parameter setting process based on real-time measured allowances and preset optimization algorithms, not only the machining precision and efficiency are improved, but also the resource utilization is optimized, the cost is reduced, thereby enhancing the competitiveness of enterprises and promoting the development of intelligent manufacturing. The implementation of the above-mentioned optimization machining parameter process makes the entire numerical control machining process more standardized and efficient. Specifically, using the measured allowance as the feedback basis makes the decision-making process more scientific and intuitive, and improves the pertinence and applicability of the parameters.

[0128] Due to the flexibility and adaptability of the machining strategy, enterprises can quickly respond to market changes and meet the diverse and personalized needs of customers. The introduction of advanced optimization techniques such as genetic algorithms demonstrates the trend of modern numerical control machining towards intelligent manufacturing, making the production process more intelligent and automated. At the same time, optimizing the utilization of materials and reducing production costs enables enterprises to maintain economic benefits while also developing towards resource conservation and environmental friendliness. This optimization strategy combines advanced technologies and methods, not only bringing significant economic benefits to enterprises, but also laying a solid foundation for the future development of the manufacturing industry. Through continuous iteration and improvement, it is expected to achieve higher levels of machining quality and production efficiency in actual operation, and promote the entire industry to higher standards.

[0129] In some embodiments, after processing, the workpiece is divided into different machining areas. Assuming that it is divided into 3 machining areas, it can be considered that there are 3 key measurement points, and the current allowance distribution is as shown in Table 1:

[0130] Table 1 Current Allowance Distribution Table

[0131]

[0132] Algorithm parameter setting: population size: 50 individuals, ΔX and ΔZ are continuous variables, and are the X-axis compensation value ΔX and Z-axis compensation value ΔZ of the tool respectively. The allowance of multiple measurement points of the workpiece tends to the target value 0.3mm. ΔX ∈ [-0.1mm, +0.1mm] (compensation range limit), ΔZ ∈ [-0.1mm, +0.1mm];

[0133] ;

[0134] wherein, the measured residual for the i-th measuring point; and the influence coefficient of the compensation value on the i-th measuring point. (It can be obtained by simulation of the machining model, and in the embodiment, it is assumed that = 0.8, = 0.2, = 0.5, = 0.5, = 0.3, = 0.7.)

[0135] The objective is to maximize the fitness (minimize the sum of absolute values of residuals). Selection strategy: roulette wheel selection (the higher the fitness, the greater the probability of being selected); crossover method: arithmetic crossover (a = 0.5); mutation method: Gaussian mutation (standard deviation σ = 0.02 mm); termination condition: 100 iterations or fitness change rate < 1%.

[0136] The specific implementation process is as follows:

[0137] 1. Initial individuals, individual 1: ΔX = +0.05 mm, ΔZ = -0.03 mm; individual 2: ΔX = -0.08 mm, ΔZ = +0.07 mm;... (similar for other individuals).

[0138] 2. Fitness calculation: take individual 1 as an example (ΔX = +0.05 mm, ΔZ = -0.03 mm):

[0139] Calculate the residual after compensation: P1 residual = 0.25 + (0.05 x 0.8) + (-0.03 x 0.2) = 0.25 + 0.04 - 0.006 = 0.284 mm (the actual machining path parameter);

[0140] P2 residual = 0.35 + (0.05 x 0.5) + (-0.03 x 0.5) = 0.35 + 0.025 - 0.015 = 0.360 mm (the actual machining path parameter);

[0141] P3 residual = 0.28 + (0.05 x 0.3) + (-0.03 x 0.7) = 0.28 + 0.015 - 0.021 = 0.274 mm (the actual machining path parameter);

[0142] Calculate the fitness: Fitness = -(|0.284 - 0.3| + |0.360 - 0.3| + |0.274 - 0.3|) = -(0.016 + 0.060 + 0.026) = -0.102;

[0143] Similarly, calculate the fitness values of all individuals.

[0144] 3. Selection and Crossover:

[0145] Wheel Selection: The higher the fitness, the higher the probability of being selected. Assume the highest fitness in the current population is -0.05 (individual A) and the lowest is -0.20 (individual B). Therefore, the probability of individual A being selected is significantly higher than individual B.

[0146] Algorithm Crossover, select two parent individuals (e.g., individual A: ΔX = +0.08, ΔZ = -0.02; individual B: ΔX = -0.05, ΔZ = +0.04), generate offspring:

[0147] Offspring ΔX = 0.5 × 0.08 + 0.5 × (-0.05) = +0.015 mm;

[0148] Offspring ΔZ = 0.5 × (-0.02) + 0.5 × 0.04 = +0.01 mm;

[0149] 4. Mutation: Mutate the offspring individual with a 5% probability. For example, offspring ΔX = +0.015 mm is mutated according to Gaussian distribution: ΔX_new = 0.015 + N(0, 0.02) = 0.015 + 0.005 = +0.020 mm; (Assume a random perturbation value is sampled from the normal distribution N(0, 0.02), here assume the random perturbation is +0.005 mm).

[0150] 5. Iteration and Termination, repeat steps 2-4, after multiple generations of evolution, the population gradually converges; the final optimal solution: ΔX = +0.03 mm, ΔZ = -0.01 mm.

[0151] Final optimal solution: ΔX = +0.03 mm, ΔZ = -0.01 mm;

[0152] Residual after compensation:

[0153] P1: 0.25 + (0.03 × 0.8) + (-0.01 × 0.2) = 0.25 + 0.024 - 0.002 = 0.272 mm (needs further adjustment, may trigger constraint handling);

[0154] P2: 0.35 + (0.03 × 0.5) + (-0.01 × 0.5) = 0.35 + 0.015 - 0.005 = 0.360 mm;

[0155] P3: 0.28 + (0.03 × 0.3) + (-0.01 × 0.7) = 0.28 + 0.009 - 0.007 = 0.282 mm;

[0156] Constraint Handling: If the residual of the measurement point is still out of tolerance, a penalty term needs to be added to the fitness function:

[0157] ;

[0158] A 10 times penalty weight is applied to the over-tolerance part, forcing the algorithm to search for a solution that satisfies the constraints.

[0159] For example: if the algorithm output is verified as ΔX = +0.06mm, ΔZ = -0.04mm;

[0160] After compensation, the remaining amount is:

[0161] P1: 0.25 + (0.06 x 0.8) + (-0.04 x 0.2) = 0.25 + 0.048 - 0.008 = 0.290mm;

[0162] P2: 0.35 + (0.06 x 0.5) + (-0.04 x 0.5) = 0.35 + 0.03 - 0.02 = 0.360mm (still over-tolerance, local compensation needs to be started);

[0163] P3: 0.28 + (0.06 x 0.3) + (-0.04 x 0.7) = 0.28 + 0.018 - 0.028 = 0.270mm;

[0164] Adjustment strategy: For the problem of P2 remaining amount being too large, call the local compensation module to separately increase the Z-axis compensation value of this area (for example, increase an additional -0.03mm Z compensation). The updated remaining amount is: P2 = 0.360 - 0.03 x 0.5 = 0.345mm (still over-tolerance, further iteration optimization or adjustment of cutting parameters is needed).

[0165] Optionally, the numerical control system further comprises a feedback mechanism unit connected with the online measurement unit, and the adjustable numerical control program error-proof machining method further comprises:

[0166] During the machining of the workpiece to be processed, the online measurement unit acquires the tool wear parameters according to a preset frequency, and the tool wear parameters include the machining parameters, material characteristics and environmental data;

[0167] The feedback mechanism unit inputs the tool wear parameters into a preset tool wear model to obtain a tool predicted wear result;

[0168] The tool predicted wear result is compared with a set threshold value;

[0169] When the tool predicted wear result is greater than the set threshold value, the machining parameters of the numerical control system are adjusted.

[0170] Specifically, during the machining process of the workpiece to be processed, the online measurement unit periodically acquires a series of wear parameters at a preset frequency. These wear parameters include machining parameters of the tool (such as cutting speed, feed rate, etc.), material properties (such as material hardness, toughness, etc.), and environmental data (such as temperature, humidity, etc.). These data are crucial for determining the wear state of the tool.

[0171] The acquired wear parameters are input into the preset tool wear model through the feedback mechanism unit. This model uses historical data and mathematical algorithms to predict the wear condition of the tool under the current machining conditions. Through analysis of these parameters, the model can generate a prediction result about the wear of the tool.

[0172] The predicted tool wear result is compared with the set threshold value. If the predicted wear result exceeds the set threshold value, it indicates that the tool is in an excessive wear state, which may affect the machining quality and efficiency.

[0173] When it is found that the predicted wear result of the tool is greater than the set threshold value, the feedback mechanism unit is started to automatically adjust the machining parameters of the numerical control system. These adjustments can include increasing the cutting speed, reducing the feed rate, or increasing the tool compensation data, etc., to reduce the load of the tool and thus prolong the service life of the tool.

[0174] By monitoring and predicting the wear condition of the tool in real time, the system can discover potential problems early and ensure that the tool state during the machining process always remains within a good range, thereby improving the quality of the final product. Timely adjustment of machining parameters can effectively reduce tool wear, prolong the service life of the tool, reduce the frequency of tool replacement, and save maintenance costs. In addition, real-time feedback and adjustment of the wear condition help to better maintain the optimal state of the machining parameters during the machining process, reduce downtime, and improve overall production efficiency.

[0175] Accurate wear prediction and corresponding parameter adjustment can make the use of materials and energy more reasonable, reduce resource waste, and promote sustainable development. At the same time, the data-driven feedback mechanism makes the decision-making process more scientific, avoiding reliance on experience or blind adjustment, and enhancing the intelligent level of the numerical control system. In addition, by adjusting the tool state early, reducing machining failures caused by tool failure, and reducing subsequent repair and rework costs, the yield of qualified products is improved.

[0176] By implementing this feedback mechanism unit, the real-time monitoring of tool wear and the dynamic adjustment of machining parameters are combined, not only optimizing the machining process, but also improving the intelligent level of the numerical control system, promoting the efficiency and stability of modern production.

[0177] Optionally, the construction process of the preset tool wear model includes:

[0178] The simulation machining parameters, simulation material characteristics, and simulation environment data of the simulation workpiece and the corresponding label data are obtained through simulation simulation experiments;

[0179] The original wear model is trained and optimized through the simulation machining parameters, simulation material characteristics, simulation environment data, and corresponding label data, and the optimized original wear model is used as the tool wear model.

[0180] Optionally, the original wear model is trained and optimized through the simulation machining parameters, simulation material characteristics, simulation environment data, and corresponding label data, and the optimized original wear model is used as the tool wear model, comprising:

[0181] The original wear model is trained according to the simulation machining parameters, simulation material characteristics, and simulation environment data to obtain a temporary tool wear result.

[0182] According to the temporary tool wear result and the corresponding label data, the original wear model is loss calculated through a preset loss function to obtain a loss function output.

[0183] The model parameters of the original wear model are adjusted according to the loss function output, and iterative training is performed until the loss function output meets a preset condition, and the original wear model after parameter adjustment is used as the tool wear model.

[0184] Specifically, the construction process of the tool wear model is as follows: first, a series of data are obtained through simulation simulation experiments, including the machining parameters, material characteristics, and environment data of the simulation workpiece, and the corresponding label data. The simulation workpiece simulates various conditions in actual machining, thereby providing a true reflection for subsequent model training. The machining parameters (such as cutting speed, feed rate, depth of cut, etc.), material characteristics (which can include workpieces and tools), environment data (such as temperature, humidity, etc.), and label data obtained from simulation are used to train and optimize the original wear model. This process aims to learn the relationship between these data so that the model can accurately predict the wear condition of the tool.

[0185] During the training process, the simulation machining parameters, simulation material characteristics, and simulation environment data are input into the original wear model to obtain a temporary tool wear result. This result represents the expected wear level of the tool under these conditions.

[0186] The temporary tool wear results are compared with the corresponding label data, and the loss value of the model is calculated through a preset loss function. The loss function is a quantitative tool for evaluating the gap between the model output results and the true results, reflecting the prediction accuracy of the model.

[0187] According to the output results of the loss function, the model parameters of the original wear model are continuously adjusted. This process is iterative until the output of the loss function meets the preset conditions, indicating that the prediction ability of the model has reached a satisfactory level.

[0188] After parameter adjustment and training, the obtained optimized model is the final tool wear model. This model can be applied in actual processing to provide a basis for tool wear prediction.

[0189] Training the wear model through multi-dimensional features in simulation data can significantly improve the prediction accuracy of the model, making the prediction of tool wear more reliable. At the same time, by combining data from different simulation conditions, the model can better adapt to various situations that may be encountered in actual processing, enhancing its versatility and adaptability.

[0190] Using virtual simulation to obtain data can greatly reduce experiments and trial-and-error in actual processing, thereby reducing production costs and time. Accurate wear prediction can help enterprises more effectively manage tool usage and replacement, reduce unnecessary waste, and improve tool utilization. In addition, accurate prediction results can help operators adjust processing parameters in a timely manner to avoid processing failures due to unmonitored tool wear, improving overall production efficiency.

[0191] In the construction of the tool wear model, the introduction of simulation and data-driven methods demonstrates the trend of intelligent manufacturing and promotes the digitalization and automation process of manufacturing. Through this series of construction and optimization process, the tool wear model not only achieves higher prediction accuracy and reliability, but also provides strong support for enterprises in production management and resource optimization, promoting the development of manufacturing to a higher level.

[0192] It should be noted that the preset tool wear model can be selected according to specific circumstances, such as statistical methods (e.g., regression analysis), machine learning algorithms, neural network models (ANN, Artificial Neural Network), etc.

[0193] Optionally, the error-proof machining method of the adjustable numerical control program further comprises:

[0194] Obtain tool life data, processing time data, and workpiece quality data, and determine the corresponding temporary score based on the tool life data, the processing time data, and the workpiece quality data;

[0195] Based on each of the temporary scores and corresponding preset weight data, comprehensive score data is obtained;

[0196] According to the comprehensive score and the preset score threshold, a judgment result is obtained, and a recommended adjustment strategy of the corresponding processing parameter is generated based on the judgment result.

[0197] Specifically, on the basis of the above-mentioned adjustable numerical control program error-proof machining method, the following steps can be selectively introduced to further optimize the machining process:

[0198] Data acquisition process includes: tool life data: regularly monitor the use of tools, which can be calculated based on tool predicted wear results, record the remaining life and wear state of the tool.

[0199] Machining time data: track the actual time of each machining step and compare it with the standard time to evaluate the machining efficiency.

[0200] Workpiece quality data: record and evaluate the quality indicators of each workpiece, such as dimensional accuracy, surface roughness and defect rate, etc.

[0201] According to the acquired tool life data, machining time data and workpiece quality data, the corresponding temporary scores are calculated (for example, upper and lower limit score regions can be set for each evaluation standard). Temporary scores reflect the current evaluation of tool state, machining efficiency and product quality.

[0202] Based on each of the temporary scores and preset weight data (importance weight of each standard), comprehensive score data is calculated. The setting of weight can be adjusted according to experience data or actual machining requirements.

[0203] Compare the calculated comprehensive score with the preset score threshold. According to the score result under different conditions, it can be judged whether the machining parameter needs to be adjusted.

[0204] Compare the obtained comprehensive score with the preset score threshold. Through comparison, it can be judged whether the current machining condition meets the production requirements. If the comprehensive score is higher than the preset threshold, it means that the machining state is good, otherwise there may be problems.

[0205] According to the judgment result, a corresponding machining parameter recommended adjustment strategy is generated. For example: if the comprehensive score is higher than the threshold, it may indicate that higher machining quality is needed, and enhanced cutting parameters, optimized tool path, etc. can be considered; if it is lower than the threshold, the tool life can be adjusted to prolong the use time.

[0206] By comprehensively considering tool life, machining time, and workpiece quality, a holistic evaluation perspective is formed, leading to more scientific and rational decision-making. Furthermore, this process allows the CNC system to adjust machining parameters in real time based on actual machining conditions, thereby improving overall machining efficiency, reducing unnecessary downtime, and saving on maintenance and material costs. This method supports flexible adaptation to changing production demands in dynamic machining environments, enabling adjustments to machining strategies based on actual conditions, resulting in a more efficient production process.

[0207] During the processing, real-time feedback generates recommended adjustment strategies, allowing operators to respond more flexibly to unexpected situations and ensure continuous and efficient production.

[0208] Data-driven scoring systems make decision-making more scientific, reducing reliance on experience and minimizing the possibility of human error, thereby enhancing the intelligence level of equipment. This process can also provide precise parameter adjustment suggestions based on real-time data, thus improving automation and production continuity. Furthermore, through real-time monitoring and adjustment, overall production efficiency is improved, ensuring efficient operation of the processing process and ultimately achieving production targets on time.

[0209] In conclusion, by introducing temporary and comprehensive scoring mechanisms, the adjustable CNC program error-proofing machining method can not only enhance the intelligence and flexibility of the machining process, but also significantly improve production efficiency and product quality, meeting the modern manufacturing industry's pursuit of high efficiency and precision.

[0210] In some embodiments, it is assumed that a CNC machine tool is machining a certain metal part. Real-time monitoring includes: tool life data: the remaining service life of a certain tool is predicted to be 40 hours, indicating that based on previous wear monitoring, the tool wear coefficient is 0.02, which is evaluated in conjunction with the cutting time; machining time data: the estimated machining time for the current part is 10 hours. According to historical data, the standard machining time for this part should be 8 hours, so the score can be calculated proportionally, for example, if the standard time is set to 100%, the actual time is 80% (8 / 10); workpiece quality data: the quality of the produced workpiece is evaluated by an online inspection system. The dimensional accuracy and surface roughness of the workpiece meet the requirements, and the score is 95% (where 100% is completely qualified).

[0211] The priority criteria weights are set as follows: tool life weight: 0.5; machining time weight: 0.3; workpiece quality weight: 0.2.

[0212] The corresponding temporary score is determined based on the different priority criteria and the corresponding preset weight data;

[0213] Assume the provisional ratings are: S1 = 80% (reasonableness of tool life); S2 = 80% (based on expected machining time); S3 = 95% (workpiece quality).

[0214] The comprehensive score = (0.5 x 0.8) + (0.3 x 0.8) + (0.2 x 0.95) = 0.4 + 0.24 + 0.19 = 0.83;

[0215] Assuming the preset score threshold is 0.8, the comprehensive score 0.83 is higher than the threshold, and it is judged that the processing state is good, and no parameter adjustment is needed.

[0216] If the preset score threshold is 0.85, the comprehensive score 0.83 is lower than the threshold, a recommended adjustment strategy can be generated, for example: increase the compensation data of the tool to improve the overall adaptability of the tool. Or prevent premature wear, evaluate reducing the feed rate or adjusting the cutting depth to prevent excessive wear. Or adjust the processing time, consider adding additional rest time according to the actual running data to reduce the wear rate of the tool, etc.

[0217] After parameter adjustment, the tool wear condition and workpiece quality are continuously monitored in real time. This monitoring includes evaluating the new comprehensive score to ensure that the newly set processing parameters can effectively improve the processing process and improve the efficiency of the tool and the quality of the product.

[0218] As shown in Figure 2 The embodiment of the present application provides a kind of adjustable NC program error-proof machining equipment, it is applied to numerical control system, the numerical control system includes simulation unit, control unit and online measurement unit, the adjustable NC program error-proof machining equipment includes:

[0219] Processing module, for establishing the processing three-dimensional model of the workpiece to be processed by simulation unit;And based on the processing three-dimensional model, determine initial tool path parameter, according to the initial tool path parameter, simulation is carried out, and simulation result is obtained, the simulation result includes virtual allowance distribution;According to the virtual allowance distribution, the initial tool path parameter is adjusted to determine actual machining path parameter;

[0220] Processing module, for according to the actual machining path parameter, the workpiece to be processed is coarsely processed by the control unit, and the workpiece after processing is obtained;

[0221] The processing module is further used to obtain the measured allowance of the workpiece after processing by the online measurement unit;

[0222] The processing module is further used to determine the machining parameter of the numerical control system based on the measured allowance according to preset optimization algorithm, and the workpiece after processing is finely processed by the control unit according to the machining parameter.

[0223] As shown in Figure 3As shown, the embodiment of the present application provides a kind of adjustable numerical control program error-proof processing system, including memory and processor;The memory is used to store computer program;The processor is used to when executing the computer program, realize the adjustable numerical control program error-proof processing method as described above.

[0224] The embodiment of the present application provides a kind of computer readable storage medium, the storage medium is stored with computer program, when the computer program is executed by processor, realize the adjustable numerical control program error-proof processing method as described above.

[0225] Now will be described as the server or client of the present application adjustable numerical control program error-proof processing system, it is the example of hardware equipment that can be applied to each aspect of the present application.Adjustable numerical control program error-proof processing system is intended to represent various forms of digital electronic computer equipment, such as, laptop computer, desktop computer, workbench, personal digital assistant, server, blade server, mainframe computer, and other suitable computer.Adjustable numerical control program error-proof processing system can also represent various forms of mobile device, such as, personal digital processing, cellular phone, smart phone, wearable device and other similar computing device.The components shown herein, their connections and relationships, and their functions are merely as examples, and are not intended to limit the implementation of the present application described and / or claimed herein.

[0226] Adjustable numerical control program error-proof processing system includes computing unit, it can be according to the computer program stored in read-only memory (RM) or the computer program loaded into random access memory (RAM) from storage unit, to perform various appropriate actions and processing.In RAM, various programs and data required for device operation can also be stored.Computing unit, RM and RAM are connected to each other by bus.Input / output (I / ) interface is also connected to bus.

[0227] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware, and the program can be stored in a computer readable storage medium. When the program is executed, the program can include the processes of the above-mentioned embodiment methods. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the units described as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., they can be located in one place, or can be distributed on a plurality of network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present application. In addition, the functional units in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically independently, or two or more units can be integrated in one unit. The integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0228] Although the present application is disclosed as above, the protection scope of the present application is not limited to this. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present application, and these changes and modifications will fall within the protection scope of the present application.

Claims

1. A method for preventing errors in adjustable numerical control program machining, characterized by, The application is applied to a numerical control system, the numerical control system comprising a simulation unit, a control unit and an online measurement unit; the adjustable numerical control program error-proof machining method comprising: a three-dimensional machining model of a workpiece to be processed is established by the simulation unit; initial tool path parameters are determined based on the three-dimensional machining model; simulation is performed according to the initial tool path parameters to obtain a simulation result, the simulation result comprising a virtual allowance distribution; the initial tool path parameters are adjusted according to the virtual allowance distribution to determine actual machining path parameters; the workpiece to be processed is rough machined according to the actual machining path parameters by the control unit to obtain a processed workpiece; actual measurement allowances of the processed workpiece are obtained by the online measurement unit; machining parameters of the numerical control system are determined based on the actual measurement allowances according to a preset optimization algorithm, and the processed workpiece is finish machined according to the machining parameters by the control unit; the machining parameters comprising tool compensation data and cutting parameters; the machining parameters of the numerical control system being determined based on the actual measurement allowances according to the preset optimization algorithm comprising: potential tolerance deviation areas are determined based on the simulation result and a preset threshold, and the processed workpiece is divided according to the potential tolerance deviation areas to obtain different machining areas; tool compensation data and cutting parameters of the numerical control system are determined based on the actual measurement allowances of each machining area according to a preset genetic algorithm.

2. The adjustable numerical control program error proofing machining method according to claim 1, wherein, the preset genetic algorithm processing process comprising: population parameters are initialized, the population parameters comprising a plurality of individuals and corresponding individual parameter data; adaptability data of the individuals are calculated according to the actual measurement allowances of each machining area and the corresponding individual parameter data, and a plurality of parent individuals are determined according to all the adaptability data based on a preset roulette selection method; new individuals are generated by crossing all the parent individuals, a preset proportion of the new individuals are subjected to Gaussian mutation to obtain mutated individuals, and the initialized population parameters are updated based on the mutated individuals; the above process is repeated until a preset iteration number is reached or a stop condition is met, and final target mutated individuals and corresponding individual parameter data are obtained, the individual parameter data comprising the tool compensation data and the cutting parameters.

3. The adjustable NC program error proofing machining method according to claim 2, wherein, the numerical control system further comprises a feedback mechanism unit connected to the online measurement unit, and the adjustable numerical control program error-proof machining method further comprises: during machining of the workpiece to be processed, tool wear parameters are obtained by the online measurement unit at a preset frequency, the tool wear parameters comprising the machining parameters, material characteristics and environmental data; the tool wear parameters are input into a preset tool wear model by the feedback mechanism unit to obtain tool predicted wear results; the tool predicted wear results are compared with a set threshold; when the tool predicted wear results are greater than the set threshold, the machining parameters of the numerical control system are adjusted.

4. The adjustable NC program error proofing machining method according to claim 3, wherein, the construction process of the preset tool wear model comprising: simulation machining parameters, simulation material characteristics and simulation environmental data of a simulation workpiece and corresponding label data are obtained through simulation experiments; The original wear model is trained and optimized by the simulation machining parameter, the simulation material characteristic, the simulation environment data and the corresponding label data, and the original wear model after optimization is taken as the tool wear model.

5. The adjustable NC program error proofing machining method according to claim 4, wherein, The training and optimization of the original wear model by the simulation machining parameter, the simulation material characteristic, the simulation environment data and the corresponding label data, and the original wear model after optimization is taken as the tool wear model, comprises: The original wear model is trained according to the simulation machining parameter, the simulation material characteristic and the simulation environment data, to obtain a temporary tool wear result; The original wear model is loss calculated by a preset loss function according to the temporary tool wear result and the corresponding label data, to obtain a loss function output; The model parameters of the original wear model are adjusted according to the loss function output, and iterative training is performed until the loss function output meets a preset condition, and the original wear model after parameter adjustment is taken as the tool wear model.

6. The adjustable numerical control program error proofing machining method according to claim 1, wherein, The adjustable numerical control program error-proof machining method further comprises: Tool life data, machining time data and workpiece quality data are obtained, and corresponding temporary scores are determined according to the tool life data, the machining time data and the workpiece quality data; Based on each temporary score and corresponding preset weight data, comprehensive score data is obtained; According to the comprehensive score and a preset score threshold, a judgment result is obtained, and a corresponding machining parameter recommendation adjustment strategy is generated based on the judgment result.

7. An adjustable numerical control program error proofing machining apparatus, characterized by, Applied to a numerical control system, the numerical control system comprises a simulation unit, a control unit and an online measurement unit, and the adjustable numerical control program error-proof machining equipment comprises: A processing module is configured to establish a machining three-dimensional model of a to-be-processed workpiece by the simulation unit, determine initial tool path parameters based on the machining three-dimensional model, perform simulation simulation according to the initial tool path parameters, obtain a simulation result, the simulation result comprising a virtual allowance distribution, and adjust the initial tool path parameters to determine actual machining path parameters according to the virtual allowance distribution; A machining module is configured to perform rough machining on the to-be-processed workpiece according to the actual machining path parameters by the control unit, to obtain a processed workpiece; The processing module is further configured to obtain a measured allowance of the processed workpiece by the online measurement unit; The machining module is further configured to determine machining parameters of the numerical control system based on the measured allowance according to a preset optimization algorithm, and perform finish machining on the processed workpiece according to the machining parameters by the control unit; wherein the machining parameters comprise tool compensation data and cutting parameters; determining the machining parameters of the numerical control system based on the measured allowance according to the preset optimization algorithm comprises: determining a potential tolerance area based on the simulation result and a preset threshold, and dividing the processed workpiece according to the potential tolerance area to obtain different machining areas; determining the tool compensation data and the cutting parameters of the numerical control system based on the measured allowance of each machining area according to a preset genetic algorithm.

8. An adjustable numerical control program error proofing machining system, characterized in that, The application also discloses a computer readable storage medium, which comprises a computer program.

9. A computer-readable storage medium, characterized in that, The application also discloses a computer readable storage medium, which comprises a computer program.

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