Adjustable numerical control program mistake-proofing processing method, equipment, system and storage medium

Abstract

The invention provides an adjustable numerical control program mistake-proof machining method, device and system and a storage medium, and relates to the technical field of numerical control, and the machining method comprises the steps that a machining three-dimensional model of a to-be-processed workpiece is established through a simulation unit; based on the machining three-dimensional model, initial tool path parameters are determined; simulation is carried out according to the initial tool path parameters, a simulation result is obtained, and the result comprises virtual margin distribution. And adjusting the initial tool path path parameters according to the virtual margin distribution so as to determine actual machining path parameters. And rough machining is conducted on the to-be-treated workpiece according to the actual machining path parameters, and the treated workpiece is obtained. And the actually-measured allowance of the processed workpiece is obtained, machining parameters of the numerical control system are determined based on the actually-measured allowance according to a preset optimization algorithm, and finish machining is conducted according to the machining parameters. By establishing the three-dimensional model and analogue simulation, potential problems can be recognized in advance, errors occurring in the actual machining process are reduced, and therefore the machining precision of a final product is improved.

Description

Technical Field

[0001] The present invention relates to the field of numerical control technology, and in particular to an adjustable numerical control program error-proofing processing method, equipment, system and storage medium. Background Art

[0002] With the continuous advancement of CNC machining technology, CNC systems are playing an increasingly important role in modern manufacturing. However, traditional CNC systems often rely on static, preset paths and parameters during workpiece machining. While online measurement units can capture the actual workpiece allowances after machining based on these preset paths and parameters, adjustments to these parameters often require machine downtime, reducing the efficiency of tool compensation and cutting parameter updates.

[0003] Furthermore, while existing tool compensation mechanisms can achieve global adjustments, they offer low precision for local positions, requiring multiple NC program modifications to achieve the desired accuracy. Frequent NC program modifications not only increase the complexity of machining setup but also reduce overall machining efficiency. Furthermore, this situation increases the need for manual intervention in batch machining, which can lead to errors and compromise machining consistency and final product quality. Summary of the Invention

[0004] The problem solved by the present invention is one or more of the above-mentioned related technical problems.

[0005] To solve the above problems, the present invention provides an adjustable CNC program error-proofing processing method, equipment, system and storage medium.

[0006] In a first aspect, the present invention provides an adjustable CNC program error-proofing processing method, which is applied to a CNC system, wherein the CNC system includes a simulation unit, a control unit, and an online measurement unit; the adjustable CNC program error-proofing processing method includes: Establishing a three-dimensional machining model of a workpiece to be processed by a simulation unit; determining initial tool path parameters based on the three-dimensional machining model; performing simulation according to the initial tool path parameters to obtain simulation results, wherein the simulation results include a virtual margin distribution; Adjusting the initial tool path parameters according to the virtual margin distribution to determine actual machining path parameters; Performing rough machining on the workpiece to be processed according to the actual machining path parameters by the control unit to obtain a processed workpiece; Obtaining the measured margin of the processed workpiece by the online measuring unit; According to a preset optimization algorithm, the processing parameters of the numerical control system are determined based on the measured allowance, and the processed workpiece is fine-processed according to the processing parameters through the control unit.

[0007] Optionally, the machining parameters include tool compensation data and cutting parameters; and determining the machining parameters of the numerical control system based on the measured allowance according to a preset optimization algorithm includes: Determining a potential out-of-tolerance area based on the simulation result and a preset threshold, and dividing the processed workpiece according to the potential out-of-tolerance area to obtain different processing areas; Based on a preset genetic algorithm, tool compensation data and cutting parameters of the numerical control system are determined according to the actually measured allowances of each processing area.

[0008] Optionally, the preset genetic algorithm processing process includes: Initializing population parameters, wherein the population parameters include a plurality of individuals and corresponding individual parameter data; Calculating the fitness data of the corresponding individual according to the measured margins of each processing area and the corresponding individual parameter data, and determining a plurality of parent individuals according to all the fitness data based on a preset roulette wheel selection method; Performing crossover on all the parent individuals to generate new individuals, selecting a preset proportion of the new individuals to perform Gaussian mutation to obtain mutant individuals, and updating the initialized population parameters based on the mutant individuals; Repeat the above process until the preset number of iterations is reached or the stopping condition is met, and the final target variant individual and the corresponding individual parameter data, the individual parameter data, the tool compensation data and the cutting parameters are obtained.

[0009] Optionally, the numerical control system further includes a feedback mechanism unit, wherein the feedback mechanism unit is connected to the online measurement unit, and the adjustable numerical control program error-proofing processing method further includes: During the processing of the workpiece to be processed, the wear parameters of the tool are obtained by the online measurement unit at a preset frequency, wherein the wear parameters include the processing parameters, material properties and environmental data; The wear parameters are input into a preset tool wear model through the feedback mechanism unit to obtain a tool wear prediction result; comparing the tool wear prediction result with a set threshold; When the predicted tool wear result is greater than the set threshold, the machining parameters of the numerical control system are adjusted.

[0010] Optionally, the process of constructing the preset tool wear model includes: Acquire the simulation processing parameters, simulation material properties, simulation environment data and corresponding label data of the simulation workpiece through simulation experiments; The original wear model is trained and optimized using the simulation processing parameters, the simulation material properties, the simulation environment data and the corresponding label data, and the optimized original wear model is used as the tool wear model.

[0011] Optionally, the training and tuning of the original wear model using the simulated processing parameters, the simulated material properties, the simulated environment data, and the corresponding label data, and using the tuned original wear model as the tool wear model, includes: The original wear model is trained according to the simulation processing parameters, the simulation material properties, and the simulation environment data to obtain a temporary tool wear result; According to the temporary tool wear result and the corresponding label data, the loss calculation of the original wear model is performed using a preset loss function to obtain a loss function output; The model parameters of the original wear model are adjusted according to the output of the loss function, and the training is iterated until the output of the loss function meets the preset conditions. The original wear model after parameter adjustment is used as the tool wear model.

[0012] Optionally, the adjustable CNC program error-proofing processing method further includes: Acquiring tool life data, machining time data, and workpiece quality data, and determining a corresponding temporary score based on the tool life data, the machining time data, and the workpiece quality data; Obtaining comprehensive score data based on each of the temporary scores and corresponding preset weight data; A judgment is made according to the comprehensive score and the preset score threshold to obtain a judgment result, and a recommended adjustment strategy for the corresponding processing parameters is generated based on the judgment result.

[0013] In a second aspect, the present invention provides an adjustable CNC program error-proofing processing device, which is applied to a CNC system, wherein the CNC system includes a simulation unit, a control unit, and an online measurement unit. The adjustable CNC program error-proofing processing device includes: a processing module configured to establish a three-dimensional machining model of a workpiece to be processed through a simulation unit; determine initial tool path parameters based on the three-dimensional machining model; perform simulation according to the initial tool path parameters to obtain simulation results, the simulation results including a virtual margin distribution; and adjust the initial tool path parameters according to the virtual margin distribution to determine actual machining path parameters; a processing module, configured to perform rough processing on the workpiece to be processed according to the actual processing path parameters through the control unit to obtain a processed workpiece; The processing module is further configured to obtain the measured margin of the processed workpiece through the online measurement unit; The processing module is also used to determine the processing parameters of the CNC system based on the measured allowance according to a preset optimization algorithm, and to perform fine processing on the processed workpiece according to the processing parameters through the control unit.

[0014] In a third aspect, the present invention provides an adjustable CNC program error-proofing processing 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 processing method as described in the first aspect when executing the computer program. In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the adjustable CNC program error-proofing processing method as described in the first aspect is implemented.

[0015] The beneficial effects of the adjustable CNC program error-proofing processing method, equipment, system and storage medium of the present invention are: By using the simulation unit in the CNC system to create a three-dimensional model of the workpiece being processed, it can accurately reflect the workpiece's geometric characteristics and processing requirements. Based on the above three-dimensional model, the initial tool path parameters are determined using algorithms or experience. These parameters will guide the tool's movement trajectory during the processing process. Next, simulation is performed based on the initial tool path parameters to obtain simulation results. These results include virtual allowance distribution information, which can provide a preliminary understanding of the surface allowance state of the workpiece after processing. That is, it indicates the thickness and distribution of the material remaining 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 allowances, and the initial tool path parameters are adjusted accordingly. This step aims to optimize the actual processing path to more efficiently remove allowances and avoid redundant cutting.

[0016] The control unit then performs rough machining on the workpiece based on the actual machining path parameters. This step aims to quickly remove a large amount of material, laying the foundation for subsequent finishing. Simultaneously, an online measurement unit obtains the actual allowance of the processed workpiece. This measurement result can reflect the quality of the actual finished workpiece.

[0017] Finally, based on the measured stock, a pre-set optimization algorithm calculates the corresponding machining parameters. These parameters are used to adjust the tool's motion strategy to improve machining accuracy. The control unit then performs finishing according to the determined machining parameters, ensuring that the workpiece achieves the highest precision and quality requirements in the final stage.

[0018] In summary, the present invention can identify potential problems in advance and reduce errors that occur during the actual processing process by establishing a three-dimensional model and simulation, thereby improving the processing accuracy of the final product. At the same time, by combining real-time online measurement with parameter optimization, the CNC system can use materials more efficiently during the processing, reduce waste, and lower production costs. An effective feedback mechanism can automatically adjust tool parameters based on real-time data, and flexibly respond to different processing requirements and unexpected situations. In addition, the entire process is highly automated, which reduces dependence on manual intervention, reduces operational errors caused by human factors, and improves production consistency and efficiency. By combining reasonable rough processing with fine processing, the processing cycle is shortened, the overall production efficiency is improved, and the competitiveness of the enterprise is enhanced.

[0019] Therefore, the adjustable CNC program error-proofing processing method of the present invention not only improves processing quality and efficiency through advanced simulation and measurement technology, but also effectively reduces ineffective production losses and enhances the overall flexibility and intelligence level of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic flow chart of an adjustable CNC program error-proofing machining method according to an embodiment of the present invention; Figure 2 This is a schematic structural diagram of an adjustable CNC program error-proofing processing device according to an embodiment of the present invention; Figure 3 The figure is a schematic structural diagram of an adjustable CNC program error-proofing machining system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein. Instead, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0022] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0023] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0024] It should be noted that the modifications of "one" and "multiple" mentioned in the present invention are illustrative rather than restrictive. Those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0025] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0026] In related technologies, traditional CNC systems often rely on experience and simple geometric models when developing toolpaths, resulting in low accuracy. These systems struggle to accurately predict the distribution of virtual stock during machining when faced with tool wear or changes in workpiece material properties. This can lead to the formation of potential out-of-tolerance areas, seriously impacting the quality of the final workpiece. Especially in the field of fine turning, when using a two-cut process, the total reserved stock (e.g., 0.6mm) is often not evenly distributed due to factors such as tool wear variations and material hardness fluctuations, resulting in deviations in the stock after single-cut cutting.

[0027] Secondly, current technologies rely heavily on experience-based adjustments, lacking systematic optimization and automated feedback mechanisms. This results in a lack of flexibility and adaptability in the machining process, especially when faced with complex workpieces and changing process requirements, and an inability to dynamically adapt to machining deviations.

[0028] In response to the problems existing in the above-mentioned related technologies, this embodiment provides an adjustable CNC program error-proofing processing method, equipment, system and storage medium.

[0029] like Figure 1 As shown, an embodiment of the present invention provides an adjustable CNC program error-proofing processing method, which is applied to a CNC system. The CNC system includes a simulation unit, a control unit, and an online measurement unit. The adjustable CNC program error-proofing processing method includes: In step S100, a three-dimensional processing model of the workpiece to be processed is established through a simulation unit; initial tool path parameters are determined based on the three-dimensional processing model, and simulation is performed according to the initial tool path parameters to obtain simulation results, which include virtual margin distribution.

[0030] Specifically, this embodiment can be applied to CNC systems that use cutting tools for machining processes such as cutting. Using a simulation unit within the CNC system, the geometric features and machining requirements of the workpiece being processed are converted into a three-dimensional digital model. This process typically involves CAD (Computer-Aided Design) software, which inputs the workpiece's physical characteristics (such as shape, dimensions, and material properties) into the system.

[0031] Based on the established 3D model, algorithms or empirical rules are used to analyze the workpiece's shape and characteristics and determine initial toolpath parameters. These parameters dictate the specific trajectory the tool should follow during machining. Based on these initial toolpath parameters, the system performs a simulation. This simulation takes into account factors such as the tool's cutting motion, machining speed, and cutting depth to realistically reproduce the machining process.

[0032] The simulation output includes virtual stock distribution information. This information shows the material removal in each machining area during the initial simulation, indicating the amount of material remaining after the tool reaches each area. The virtual stock distribution is presented in the form of a graph or data, usually expressed as the stock thickness of different areas, and can be visualized using color coding, heat maps, etc. This distribution information can intuitively show which parts of the workpiece require further processing and reflect the possible out-of-tolerance areas during the machining process.

[0033] By analyzing the virtual stock distribution, the tool path can be effectively optimized, thereby adjusting the machining parameters, such as the introduction of the tool path and the feed rate, to ensure that the stock can be efficiently removed in actual machining, avoiding increased tool wear or workpiece quality problems.

[0034] In short, virtual allowance distribution is a prediction and analysis of the residual material status of the workpiece under simulation and during tool machining, which provides an important basis for optimizing subsequent machining paths and parameters, thereby improving machining accuracy and efficiency.

[0035] By simulating a 3D model before actual machining, the tool's trajectory and the workpiece's cutting area can be more accurately predicted and verified, significantly reducing potential errors during actual machining. Furthermore, by analyzing simulation results, potential problems, such as excessive virtual stock distribution or areas where the tool might collide, can be identified in advance. This allows for design optimization before machining begins, preventing deviations and defects later in the process.

[0036] Virtual stock distribution provides the material removal status of each machining area, enabling the system to better plan the machining process, ensure reasonable material utilization, reduce waste and lower production costs.

[0037] Real-time simulation enables rapid adjustment of toolpath parameters during the design phase without requiring actual program or equipment changes, significantly improving process flexibility and adaptability. Furthermore, efficient simulation processes can accelerate development cycles, shortening the time from design to production, enabling companies to respond more quickly to market demands and thus enhance their competitiveness.

[0038] In summary, the introduction of step S100 in CNC machining not only enhances the controllability and accuracy of the machining process through simulation technology, but also effectively reduces risks and costs, and improves overall production efficiency and flexibility.

[0039] Step S200 : adjusting the initial tool path parameters according to the virtual margin distribution to determine actual machining path parameters.

[0040] Specifically, first, the virtual allowance distribution obtained in step S100 needs to be analyzed in detail. This distribution reflects the material removal status of each area of ​​the tool during the machining process and shows which areas may have more residual material.

[0041] Based on the virtual stock distribution, critical areas and potential out-of-tolerance areas are identified. These areas need to be paid special attention to during finishing to ensure that the processing quality meets the expected standards.

[0042] Based on the analysis results of the virtual allowance, the initial tool path parameters are adjusted. This may include changing the tool's cutting sequence, speed, depth, or cutting angle to more effectively remove material and reduce the remaining material. For example, for areas with large virtual allowances, the tool path can be adjusted to make the tool perform more cutting operations in these areas, while shortening the tool's dwell time in other areas with smaller allowances.

[0043] After completing the above adjustments, the new guide path parameters are converted into actual processing path parameters, and the actual processing stage is ready.

[0044] By adjusting the guide path parameters in a targeted manner, the virtual allowance of each processing area 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 areas and making corresponding adjustments, the possibility of out-of-tolerance in actual processing can be reduced, which helps to improve product consistency and qualification rate. The adjusted processing path also promotes more uniform material removal, thereby ensuring more reasonable material utilization, reducing invalid material waste, and thus reducing production costs. Through accurate adjustment of the virtual allowance, the tool can complete the processing task quickly and efficiently, thereby shortening the processing cycle and improving overall production efficiency.

[0045] The flexibility of this process enables the CNC system to adapt to different workpieces and processing requirements, improving the system's adaptability and intelligence. Therefore, adjusting the initial toolpath parameters based on the virtual stock distribution not only enhances the accuracy and efficiency of the machining process, but also effectively reduces quality risks in production, optimizes resource utilization, and improves overall production capacity and competitiveness.

[0046] Step S300 : performing rough processing on the workpiece to be processed according to the actual processing path parameters by the control unit to obtain a processed workpiece.

[0047] Specifically, first, the control unit receives the actual machining path parameters adjusted in step S200. These parameters have been optimized based on the virtual allowance distribution to ensure that the tool can remove material more effectively.

[0048] During the preparation phase, the control unit sets the appropriate roughing conditions, including the cutting tool, cutting speed, feed rate, and depth of cut. These parameters must be properly configured based on the material properties and design requirements of the workpiece to be processed to achieve optimal machining quality.

[0049] The control unit activates the processing equipment (such as a CNC machine tool) and performs rough machining on the workpiece according to the actual machining path parameters set. In this step, the tool moves along a predetermined path to remove excess material from the workpiece surface and form a preliminary shape.

[0050] During roughing, the system monitors the machining status in real time, such as the tool's cutting force, workpiece temperature, and vibration, to ensure machining stability. The control unit then makes necessary dynamic adjustments based on this feedback to address potential machining issues.

[0051] After rough machining is completed, a preliminarily formed workpiece is obtained, and its surface features lay the foundation for subsequent fine machining.

[0052] Through properly designed actual machining path parameters, roughing can quickly remove large amounts of material, thereby shortening machining time and improving overall production efficiency. Furthermore, optimized path parameters help the tool optimally contact the workpiece surface, reducing unnecessary wear, extending tool life, and lowering production costs. Furthermore, by monitoring the machining process in real time, the control unit can respond and adjust machining parameters promptly, ensuring that the workpiece quality meets design requirements and reducing the risk of defects.

[0053] The rough machining process not only removes excess material but also lays a good foundation for subsequent finishing. This helps improve the efficiency and effect of finishing and ensures that the final product meets higher quality standards.

[0054] In summary, the introduction of step S300 significantly improves the processing efficiency and quality by performing rough processing through optimized actual processing path parameters, while reducing costs and enhancing the stability and reliability of the production process.

[0055] Step S400: obtaining the actual measurement margin of the processed workpiece through the online measurement unit.

[0056] Specifically, the online measurement unit is integrated into the CNC machining system to monitor and evaluate the machining status of the workpiece in real time. The unit is equipped with high-precision sensors and measuring devices to quickly and accurately obtain the geometric information of the workpiece.

[0057] After rough machining is completed, the control unit starts the online measuring unit, ensures that it is in normal working condition, the sensor is calibrated and ready for workpiece measurement.

[0058] The online measurement unit scans the processed workpiece surface using a laser, tactile probe, or other measurement technology, acquiring the workpiece's geometric data in real time. The system compares the measurement results with the design specifications and calculates the measured allowance, i.e., the actual amount of material remaining on the workpiece.

[0059] The measured allowance data is transmitted in real time to the control unit for data processing and analysis. Based on the measured allowance and preset standards, the system identifies potential out-of-tolerance areas or unprocessed areas and generates corresponding feedback information.

[0060] The control unit can make necessary adjustments to subsequent processing parameters based on real-time measurement data to ensure that the final processing quality of the workpiece meets the design requirements.

[0061] By obtaining real-time measured allowances, engineers can promptly understand the workpiece's machining status, enabling targeted adjustments to the machining process and improving overall part accuracy. Furthermore, online measurement technology provides continuous monitoring of the machining process, making it more controllable, reducing potential machining errors, and ultimately increasing the yield rate. Furthermore, the implementation of real-time measurement shortens the feedback time from measurement to adjustment, enabling faster optimization of the machining process and improving response speed compared to traditional offline measurement methods.

[0062] By accurately understanding the measured allowance, unprocessed areas can be identified promptly, avoiding subsequent processing waste due to insufficient processing and fully optimizing material utilization. Real-time data on measured allowance makes the adjustment of tooling and processing strategies more scientific and reasonable, thereby improving production efficiency and shortening the overall production cycle.

[0063] In summary, obtaining the actual measured allowance of the processed workpiece through an online measurement unit can not only improve machining accuracy and production efficiency, but also enhance the controllability of the machining process and reduce material waste. This process has important application value in modern CNC machining.

[0064] Step S500 , determining the machining parameters of the numerical control system based on the measured allowance according to a preset optimization algorithm, and performing fine machining on the processed workpiece according to the machining parameters through the control unit.

[0065] Specifically, a preset optimization algorithm is introduced, which uses the measured allowance as input data, analyzes the actual machining status of the workpiece, and makes appropriate machining parameter adjustment suggestions.

[0066] The previously measured stock allowance data is input into the optimization algorithm. By comparing the actual stock allowance with the design standard, the algorithm evaluates the machining requirements of each area, determining, for example, which areas require more material removal and the necessary adjustments to parameters such as cutting speed, feed rate, and depth of cut. Based on this analysis, the optimization algorithm automatically generates the optimal machining parameters. These parameters are designed to maximize the material removal rate while ensuring acceptable workpiece quality. Upon receiving the optimized machining parameters, the control unit prepares the cutting tools and machining equipment to ensure they meet the specified finishing requirements.

[0067] During finishing, the control unit activates the machining equipment and performs finishing on the processed workpiece according to the calculated optimal machining parameters. During this process, the tool efficiently removes material along the programmed path, achieving the highest surface quality and precision. Furthermore, during finishing, the system continuously monitors the machining status and adjusts machining parameters based on real-time feedback to ensure a smooth process and achieve the desired results.

[0068] An optimization algorithm, based on data input from measured allowances, provides tailored parameters for each machining area, thereby improving the overall accuracy and surface quality of the workpiece. By accurately analyzing the measured allowances, the optimization algorithm can effectively reduce material waste, ensure that the material is fully utilized during each machining operation, and reduce production costs. Furthermore, by implementing optimized parameters, the tool can perform cutting tasks more efficiently, 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 in the machining process, ensuring a more controllable process and reducing the incidence of failures and errors.

[0069] In summary, by determining the machining parameters of the CNC system according to the preset optimization algorithm and the measured allowance for fine machining, the machining accuracy, production efficiency and material utilization rate are significantly improved, while the controllability of the machining process is enhanced, providing a more intelligent solution for modern CNC machining.

[0070] In this embodiment, the simulation unit in the numerical control system is used to establish a three-dimensional model for processing the workpiece to be processed, which can accurately reflect the geometric characteristics and processing requirements of the workpiece. On the basis of the above-mentioned three-dimensional model, the initial tool path parameters are determined by algorithm or experience, and the parameters will guide the movement trajectory of the tool during the processing. Then, simulation is performed based on the initial tool path parameters to obtain simulation results. These results include virtual allowance distribution information, which can pre-understand the surface allowance state of the workpiece after processing, that is, the thickness and distribution of the material remaining 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 allowances, so as to adjust the initial tool path parameters. This step is intended to optimize the actual processing path to more efficiently remove the allowance and avoid redundant cutting.

[0071] The control unit then performs rough machining on the workpiece based on the actual machining path parameters. This step aims to quickly remove a large amount of material, laying the foundation for subsequent finishing. Simultaneously, an online measurement unit obtains the actual allowance of the processed workpiece. This measurement result can reflect the quality of the actual finished workpiece.

[0072] Finally, based on the measured stock, a pre-set optimization algorithm calculates the corresponding machining parameters. These parameters are used to adjust the tool's motion strategy to improve machining accuracy. The control unit then performs finishing according to the determined machining parameters, ensuring that the workpiece achieves the highest precision and quality requirements in the final stage.

[0073] In summary, by establishing 3D models and conducting simulations, potential problems can be identified in advance, errors occurring during the actual machining process can be reduced, and the final product's machining accuracy can be improved. Furthermore, by combining real-time online measurement with parameter optimization, the CNC system can more efficiently utilize materials during the machining process, reduce waste, and lower production costs. An effective feedback mechanism automatically adjusts tool parameters based on real-time data, flexibly responding to varying machining requirements and unexpected situations. Furthermore, the entire process is highly automated, reducing reliance on human intervention, minimizing operational errors caused by human factors, and improving production consistency and efficiency. By rationally combining roughing and finishing, the machining cycle is shortened, overall production efficiency is improved, and the company's competitiveness is enhanced.

[0074] Therefore, the adjustable CNC program error-proofing processing method not only improves the processing quality and efficiency through advanced simulation and measurement technology, but also effectively reduces the ineffective loss of production and enhances the overall flexibility and intelligence level of the system.

[0075] Optionally, the machining parameters include tool compensation data and cutting parameters; and determining the machining parameters of the numerical control system based on the measured allowance according to a preset optimization algorithm includes: Determining a potential out-of-tolerance area based on the simulation result and a preset threshold, and dividing the processed workpiece according to the potential out-of-tolerance area to obtain different processing areas; Based on a preset genetic algorithm, tool compensation data and cutting parameters of the numerical control system are determined according to the actually measured allowances of each processing area.

[0076] Optionally, the preset genetic algorithm processing process includes: Initializing population parameters, wherein the population parameters include a plurality of individuals and corresponding individual parameter data; Calculating the fitness data of the corresponding individual according to the measured margins of each processing area and the corresponding individual parameter data, and determining a plurality of parent individuals according to all the fitness data based on a preset roulette wheel selection method; Performing crossover on all the parent individuals to generate new individuals, selecting a preset proportion of the new individuals to perform Gaussian mutation to obtain mutant individuals, and updating the initialized population parameters based on the mutant individuals; Repeat the above process until the preset number of iterations is reached or the stopping condition is met, and the final target variant individual and the corresponding individual parameter data, the individual parameter data, the tool compensation data and the cutting parameters are obtained.

[0077] Specifically, the machining parameters include tool compensation data (mainly including X / Z axis offset, used to correct dimensional deviations caused by tool wear or machining errors) and cutting parameters (such as cutting speed, feed rate and cutting depth, etc.).

[0078] Based on the simulation results and preset thresholds, the actual machining status of the workpiece is analyzed to identify areas that may exceed dimensional tolerances. This step is critical to ensuring the quality of the finished product and can detect potential problems in the machining process in advance.

[0079] Based on the identified potential out-of-tolerance areas, the processed workpiece is divided into multiple processing areas. These areas will adopt different processing strategies based on their remaining material (measured allowance), enabling more efficient and accurate subsequent processing.

[0080] Based on the measured stock allowances in each machining area, a pre-set genetic algorithm is applied to determine tool compensation data and cutting parameters to optimize the machining process. The genetic algorithm mimics the process of natural selection and iterates to find the optimal solution.

[0081] The preset genetic algorithm processing process includes: Initializing population parameters: At the beginning of the genetic algorithm, the population parameters are initialized. These parameters include multiple "individuals" and corresponding individual parameter data. These individuals represent possible combinations of tool compensation and cutting parameters.

[0082] Based on the measured margin of each processing area and the corresponding individual parameter data, the fitness data of each individual is calculated. Individuals with high fitness indicate that their parameter configuration can better meet the processing requirements.

[0083] Using the preset roulette wheel selection method, multiple parent individuals are determined based on all fitness data. Individuals with higher fitness have a greater probability of being selected as parents, ensuring that good traits are passed on.

[0084] A crossover operation is performed on all parent individuals to generate new individuals for the next generation. This step simulates genetic recombination in organisms and may combine the advantages of both parents. Among the generated new individuals, a predetermined proportion of individuals are selected for Gaussian mutation. This process introduces randomness to explore the parameter space and prevent the algorithm from falling into a local optimum.

[0085] Based on the mutated individuals, the initialized population parameters are updated to prepare for the next round of iteration. Through continuous iterative updates, the overall fitness of the population is improved.

[0086] The above process is repeated until the preset number of iterations is reached or a stopping condition is met. The resulting target variant individuals and their corresponding individual parameter data will be the optimal tool compensation and cutting parameters. The stopping condition can be set based on actual conditions and may include, for example, the change in the population's fitness, a solution that meets a specific goal, or a time limit. For example, if the change in the population's fitness is less than a preset threshold (such as 0.01) over a certain number of consecutive iterations, the algorithm will stop. This indicates that the algorithm has reached a stable state and further iterations may not significantly improve the solution quality. For example, if the fitness of the individual found during the algorithm reaches a preset target value (for example, meeting a specific machining accuracy), the algorithm can be stopped early. For example, a maximum run time (such as 10 minutes) can be set. When this time limit is reached, the algorithm will stop even if the preset number of iterations has not been reached.

[0087] By identifying and addressing potential out-of-tolerance areas, tool compensation data and cutting parameters can be better adjusted, thereby improving the overall machining accuracy of the workpiece. Furthermore, by dividing the machining area and applying different machining strategies based on the measured allowances, material utilization is more rational, reducing material waste and lowering production costs. Furthermore, a pre-defined genetic algorithm, through continuous selection, crossover, and mutation, can find parameter settings close to the global optimal solution across a large parameter space, thereby improving machining efficiency and quality.

[0088] Utilizing real-time feedback and data-driven optimization strategies, the system flexibly responds to diverse workpieces and machining conditions, demonstrating exceptional production line flexibility. By timely adjusting tool compensation data and cutting parameters, it reduces machining failures caused by improper parameter settings while simultaneously increasing the yield rate. Optimized machining parameters enable tools to more effectively complete cutting tasks, shortening overall machining cycles, improving production efficiency, and enhancing enterprise competitiveness.

[0089] In summary, the machining parameter setting process based on real-time measured allowances and pre-set optimization algorithms not only improves machining accuracy and efficiency, but also helps optimize resource utilization and reduce costs, thereby enhancing enterprise competitiveness and promoting the development of intelligent manufacturing. Implementing this process for optimizing machining parameters makes the entire CNC machining process more standardized and efficient. Specifically, using measured allowances as feedback makes the decision-making process more scientific and intuitive, improving the pertinence and applicability of parameters.

[0090] Thanks to the flexibility and adaptability of machining strategies, companies can quickly respond to market changes and meet the diverse and personalized needs of their customers. The introduction of advanced optimization techniques, such as genetic algorithms, demonstrates the trend of modern CNC machining transitioning towards intelligent manufacturing, making production processes more intelligent and automated. At the same time, optimizing material utilization and reducing production costs enable companies to maintain economic benefits while also moving towards resource conservation and environmental friendliness. This optimization strategy, combining advanced technologies and methods, has not only brought significant economic benefits to companies but also laid 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 operations, pushing the entire industry towards higher standards.

[0091] In some embodiments, if the processed workpiece is divided into different processing areas, assuming that it is divided into three processing areas, it can be considered that there are three key measurement points, and their current margin distribution is shown in Table 1: Table 1 Current margin distribution table

[0092] Algorithm parameter settings: Population size: 50 individuals, ΔX and ΔZ are continuous variables, representing the tool's X-axis compensation value ΔX and Z-axis compensation value ΔZ, respectively. The workpiece margin at multiple measurement points is made to approach the target value of 0.3 mm. ΔX∈[-0.1mm, +0.1mm] (compensation range limit), ΔZ∈[-0.1mm, +0.1mm]; ; in, is the measured margin at the i-th measurement point; and is the influence coefficient of the compensation value on the i-th measurement point. (It can be obtained through processing model simulation. In this embodiment, it is assumed that =0.8, =0.2, =0.5, =0.5, =0.3, =0.7.) The goal is to maximize fitness (minimize the sum of the absolute values ​​of the residuals). Selection strategy: Roulette wheel selection (the higher the fitness, the greater the probability of selection); Crossover method: Arithmetic crossover (α = 0.5); Mutation method: Gaussian mutation (standard deviation σ = 0.02 mm); Termination condition: 100 iterations or fitness change rate < 1%.

[0093] The specific implementation process is: 1. Initial individuals: Individual 1: ΔX = +0.05mm, ΔZ = -0.03mm; Individual 2: ΔX = -0.08mm, ΔZ = +0.07mm; ... (other individuals are similar).

[0094] 2. Fitness calculation: Take individual 1 as an example (ΔX=+0.05mm, ΔZ=-0.03mm): Calculate the allowance after compensation: P1 allowance = 0.25 + (0.05 × 0.8) + (-0.03 × 0.2) = 0.25 + 0.04 - 0.006 = the actual machining path parameter 0.284 mm; P2 margin = 0.35 + (0.05 × 0.5) + (-0.03 × 0.5) = 0.35 + 0.025 - 0.015 = the actual processing path parameter 0.360 mm; P3 margin = 0.28 + (0.05 × 0.3) + (-0.03 × 0.7) = 0.28 + 0.015 - 0.021 = the actual processing path parameter 0.274 mm; Calculate fitness: Fitness = -(|0.284-0.3|+|0.360-0.3|+|0.274-0.3|) = -(0.016+0.060+0.026)=-0.102; Similarly, calculate the fitness values ​​of all individuals.

[0095] 3. Selection and crossover: Roulette wheel selection: Individuals with higher fitness have a greater probability of being selected. Assume that the highest fitness in the current population is -0.05 (individual A) and the lowest is -0.20 (individual B). Therefore, individual A is significantly more likely to be selected than individual B.

[0096] The algorithm crosses over and selects two parent individuals (e.g., individual A: ΔX=+0.08, ΔZ=-0.02; individual B: ΔX=-0.05, ΔZ=+0.04) to generate offspring: Offspring ΔX = 0.5 × 0.08 + 0.5 × (-0.05) = + 0.015 mm; Offspring ΔZ = 0.5 × (-0.02) + 0.5 × 0.04 = + 0.01 mm; 4. Mutation: Mutate the offspring individuals with a probability of 5%. For example, if the offspring ΔX = +0.015mm, mutate according to the Gaussian distribution: ΔXnew = 0.015 + N(0, 0.02) = 0.015 + 0.005 = +0.020mm; (assuming a random perturbation value sampled from the normal distribution N(0, 0.02), here we assume the random perturbation is +0.005mm).

[0097] 5. Iteration and termination: repeat steps 2 to 4. After multiple generations of evolution, the population gradually converges; the final optimal solution is: ΔX = +0.03mm, ΔZ = -0.01mm.

[0098] Final optimal solution: ΔX=+0.03mm, ΔZ=-0.01mm; Residue after compensation: 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 processing); P2: 0.35+(0.03×0.5)+(-0.01×0.5)=0.35+0.015-0.005=0.360mm; P3: 0.28+(0.03×0.3)+(-0.01×0.7)=0.28+0.009-0.007=0.282mm; Constraint processing: If the margin of the measurement point is still out of tolerance, a penalty term needs to be added to the fitness function: ; A 10-fold penalty weight is imposed on the out-of-tolerance part to force the algorithm to search for a solution that satisfies the constraints.

[0099] For example: If the algorithm output and verification are ΔX=+0.06mm, ΔZ=-0.04mm; Residue after compensation: P1: 0.25+(0.06×0.8)+(-0.04×0.2)=0.25+0.048-0.008=0.290mm; P2: 0.35+(0.06×0.5)+(-0.04×0.5)=0.35+0.03-0.02=0.360mm (still out of tolerance, local compensation needs to be activated); P3: 0.28+(0.06×0.3)+(-0.04×0.7)=0.28+0.018-0.028=0.270mm; Adjustment strategy: To address the excessive stock allowance in P2, invoke the local compensation module and increase the Z-axis compensation value in that area (for example, adding an additional -0.03mm Z compensation). Updated stock allowance: P2 = 0.360 - 0.03 × 0.5 = 0.345mm (still out of tolerance, requiring further iterative optimization or adjustment of cutting parameters).

[0100] Optionally, the numerical control system further includes a feedback mechanism unit, wherein the feedback mechanism unit is connected to the online measurement unit, and the adjustable numerical control program error-proofing processing method further includes: During the processing of the workpiece to be processed, the wear parameters of the tool are obtained by the online measurement unit at a preset frequency, wherein the wear parameters include the processing parameters, material properties and environmental data; The wear parameters are input into a preset tool wear model through the feedback mechanism unit to obtain a tool wear prediction result; comparing the tool wear prediction result with a set threshold; When the predicted tool wear result is greater than the set threshold, the machining parameters of the numerical control system are adjusted.

[0101] Specifically, during the machining of a workpiece, an online measurement unit regularly acquires a series of wear parameters at a preset frequency. These wear parameters include tool parameters (such as cutting speed and feed rate), material properties (such as hardness and toughness), and environmental data (such as temperature and humidity). This data is crucial for determining the wear status of the tool.

[0102] The acquired wear parameters are fed into a pre-defined tool wear model through a feedback mechanism. This model uses historical data and mathematical algorithms to predict tool wear under current machining conditions. By analyzing these parameters, the model generates a prediction of tool wear.

[0103] The predicted tool wear results are compared with the set threshold. If the predicted wear results exceed the set threshold, it indicates that the tool is in a state of excessive wear, which may affect the machining quality and efficiency.

[0104] When the predicted tool wear exceeds a set threshold, the feedback mechanism activates and automatically adjusts the CNC system's machining parameters. These adjustments can include increasing cutting speed, reducing feed rate, or increasing tool compensation data to reduce tool load and thus extend tool life.

[0105] By monitoring and predicting tool wear in real time, the system can identify potential problems early, ensuring that tool condition remains within optimal limits throughout the machining process, thereby improving final product quality. Timely adjustments to machining parameters can effectively reduce tool wear, extend tool life, reduce tool replacement frequency, and save maintenance costs. Furthermore, real-time feedback and adjustments to wear conditions help maintain optimal machining parameters during machining, reducing downtime and improving overall production efficiency.

[0106] Accurate wear prediction and corresponding parameter adjustments can more rationally utilize materials and energy, reduce resource waste, and promote sustainable development. Furthermore, a data-driven feedback mechanism makes the decision-making process more scientific, avoiding reliance on experience or blind adjustments, and enhancing the intelligence level of the CNC system. Furthermore, by adjusting tool status early, machining failures caused by tool failure can be reduced, subsequent repair and rework costs can be lowered, and the yield rate of qualified products can be improved.

[0107] By implementing this feedback mechanism unit, real-time monitoring of tool wear is combined with dynamic adjustment of machining parameters, which not only optimizes the machining process but also improves the intelligence level of the CNC system, promoting the efficiency and stability of modern production.

[0108] Optionally, the process of constructing the preset tool wear model includes: Acquire the simulation processing parameters, simulation material properties, simulation environment data and corresponding label data of the simulation workpiece through simulation experiments; The original wear model is trained and optimized using the simulation processing parameters, the simulation material properties, the simulation environment data and the corresponding label data, and the optimized original wear model is used as the tool wear model.

[0109] Optionally, the training and tuning of the original wear model using the simulated processing parameters, the simulated material properties, the simulated environment data, and the corresponding label data, and using the tuned original wear model as the tool wear model, includes: The original wear model is trained according to the simulation processing parameters, the simulation material properties, and the simulation environment data to obtain a temporary tool wear result; According to the temporary tool wear result and the corresponding label data, the loss calculation of the original wear model is performed using a preset loss function to obtain a loss function output; The model parameters of the original wear model are adjusted according to the output of the loss function, and the training is iterated until the output of the loss function meets the preset conditions. The original wear model after parameter adjustment is used as the tool wear model.

[0110] Specifically, the tool wear model is constructed as follows: First, a series of data is acquired through simulation experiments. This data includes the simulated workpiece's machining parameters, material properties, environmental data, and corresponding labeled data. The simulated workpiece simulates various conditions in actual machining, providing a realistic reflection for subsequent model training. The original wear model is trained and optimized using the machining parameters (such as cutting speed, feed rate, and depth of cut), material properties (which may include the workpiece and tool), environmental data (such as temperature and humidity), and labeled data obtained from the simulation. This process aims to enable the model to accurately predict tool wear by learning the relationships between these data.

[0111] During the training process, the original wear model is fed with simulated machining parameters, simulated material properties, and simulated environmental data to produce a provisional tool wear result that represents the expected level of tool wear under these conditions.

[0112] The temporary tool wear results are compared with the corresponding labeled data, and the model's loss value is calculated using a preset loss function. The loss function is a quantitative tool used to evaluate the gap between the model output and the actual results, reflecting the model's prediction accuracy.

[0113] Based on the output 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 model's predictive ability has reached a satisfactory level.

[0114] After parameter adjustment and training, the optimized model is the final tool wear model. This model can be applied in actual machining and provides a basis for tool wear prediction.

[0115] Training the wear model using the multidimensional features of simulation data significantly improves the model's prediction accuracy, making tool wear predictions more reliable. Furthermore, by combining data from different simulation conditions, the model can better adapt to various situations encountered during actual machining, enhancing its versatility and adaptability.

[0116] Capturing data through virtual simulation can significantly reduce the trial and error involved in actual machining, thereby reducing production costs and time. Accurate wear predictions can help companies more effectively manage tool usage and replacement, reducing unnecessary waste and improving tool utilization. Furthermore, accurate predictions can help operators adjust machining parameters in a timely manner, avoiding machining failures caused by undetected tool wear and improving overall production efficiency.

[0117] The introduction of simulation and data-driven approaches in constructing the tool wear model demonstrates the trend toward intelligent manufacturing and promotes the digitization and automation of the manufacturing industry. Through this series of construction and optimization processes, 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, driving the manufacturing industry to a higher level of development.

[0118] It should be noted that the preset tool wear model can be selected according to specific circumstances, such as statistical methods (such as regression analysis), machine learning algorithms, neural network models (ANN, Artificial Neural Network, artificial neural network), etc.

[0119] Optionally, the adjustable CNC program error-proofing processing method further includes: Acquiring tool life data, machining time data, and workpiece quality data, and determining a corresponding temporary score based on the tool life data, the machining time data, and the workpiece quality data; Obtaining comprehensive score data based on each of the temporary scores and corresponding preset weight data; A judgment is made according to the comprehensive score and the preset score threshold to obtain a judgment result, and a recommended adjustment strategy for the corresponding processing parameters is generated based on the judgment result.

[0120] Specifically, based on the above-mentioned adjustable CNC program error-proofing processing method, the following steps can be selectively introduced to further optimize the processing process: The data acquisition process includes: Tool life data: Regularly monitor the usage of the tool, which can be calculated based on the tool wear prediction results to record the remaining life and wear status of the tool.

[0121] Processing time data: Track the actual time for each processing step and compare it with the standard time to evaluate processing efficiency.

[0122] Workpiece quality data: Record and evaluate the quality indicators of each workpiece, such as dimensional accuracy, surface roughness, and defect rate.

[0123] Based on the acquired tool life data, machining time data, and workpiece quality data, a corresponding provisional score is calculated (for example, upper and lower limit score ranges can be set for each evaluation criterion). The provisional score reflects the current assessment of tool condition, machining efficiency, and product quality.

[0124] Based on the provisional scores and preset weights (the importance of each criterion), a comprehensive score is calculated. The weights can be adjusted based on empirical data or actual processing requirements.

[0125] The calculated comprehensive score is compared with the preset score threshold. Based on the score results in different situations, it can be determined whether the processing parameters need to be adjusted.

[0126] The resulting comprehensive score is compared with a preset score threshold. This comparison determines whether the current processing conditions meet production requirements. If the comprehensive score is higher than the preset threshold, the processing is in good condition; otherwise, there may be problems.

[0127] Based on the judgment results, a corresponding machining parameter adjustment strategy is generated. For example, if the comprehensive score is above the threshold, it may indicate that higher machining quality is needed, and considerations may be given to increasing cutting parameters, optimizing tool paths, etc.; if it is below the threshold, tool life can be adjusted to extend its service life.

[0128] By comprehensively considering tool life, machining time, and workpiece quality, a comprehensive evaluation perspective is formed, making decision-making more scientific and reasonable. Furthermore, this process allows the CNC system to adjust machining parameters in real time based on the actual machining status, thereby improving overall machining efficiency, reducing unnecessary downtime, and saving maintenance and material costs. This approach supports flexible adaptation to changing production needs in a dynamic machining environment, and can adjust machining strategies based on actual conditions, making the production process more efficient.

[0129] During the machining process, real-time feedback is used to generate recommended adjustment strategies, allowing operators to respond more flexibly to emergencies during machining and ensure continuous and efficient production.

[0130] This data-driven scoring system makes the decision-making process more scientific, reduces reliance on experience, and reduces the potential for human error, thereby enhancing the intelligence of the equipment. This process also provides precise parameter adjustment recommendations based on real-time data, thereby improving automation and production continuity. Furthermore, through real-time monitoring and adjustments, overall production efficiency is improved, ensuring efficient processing and ultimately achieving production targets on time.

[0131] In summary, by introducing temporary scoring and comprehensive scoring mechanisms, the adjustable CNC program error-proofing processing method can not only enhance the intelligence and flexibility of the processing process, but also significantly improve production efficiency and product quality, meeting the modern manufacturing industry's pursuit of efficiency and precision.

[0132] In some embodiments, assume a CNC machine tool is machining a metal part. Real-time monitoring includes: tool life data: The remaining life of a tool is predicted to be 40 hours, indicating a wear coefficient of 0.02 based on previous wear monitoring, combined with an assessment of cutting time; machining time data: The estimated machining time for the current part is 10 hours. Based on historical data, the standard machining time for this part should be 8 hours. Therefore, the score can be calculated proportionally, for example, if the standard machining time is set to 100%, the actual machining time is 80% (8 / 10); and workpiece quality data: The quality of the produced workpiece is assessed by an online inspection system, and the workpiece's dimensional accuracy and surface roughness meet the requirements, with a score of 95% (where 100% is completely qualified).

[0133] The set priority standard weights are: tool life weight: 0.5; processing time weight: 0.3; workpiece quality weight: 0.2.

[0134] Determine the corresponding temporary score according to different priority standards and corresponding preset weight data; Assume that the provisional scores are: S1 = 80% (reasonableness of tool life); S2 = 80% (based on expected machining time); S3 = 95% (quality of workpiece).

[0135] Comprehensive score = (0.5 × 0.8) + (0.3 × 0.8) + (0.2 × 0.95) = 0.4 + 0.24 + 0.19 = 0.83; Assuming that the preset score threshold is 0.8, the comprehensive score of 0.83 is higher than the threshold, and it is judged that the processing status is good and no parameter adjustment is required.

[0136] If the preset score threshold is 0.85 and the overall score of 0.83 is below this threshold, a recommended adjustment strategy can be generated. For example, increasing tool compensation data to improve overall tool adaptability. Premature wear can be prevented by evaluating reducing feed rates or adjusting depth of cut to prevent excessive wear. Alternatively, machining time can be adjusted by considering adding additional rest time based on actual operating data to reduce tool wear.

[0137] After parameter adjustments are made, tool wear and workpiece quality are continuously monitored in real time. This monitoring includes evaluating the new comprehensive score to ensure that the newly set machining parameters effectively improve the machining process, tool efficiency, and product quality.

[0138] like Figure 2 As shown, an embodiment of the present invention provides an adjustable CNC program error-proofing processing device, which is applied to a CNC system. The CNC system includes a simulation unit, a control unit, and an online measurement unit. The adjustable CNC program error-proofing processing device includes: a processing module configured to establish a three-dimensional machining model of a workpiece to be processed through a simulation unit; determine initial tool path parameters based on the three-dimensional machining model; perform simulation according to the initial tool path parameters to obtain simulation results, the simulation results including a virtual margin distribution; and adjust the initial tool path parameters according to the virtual margin distribution to determine actual machining path parameters; a processing module, configured to perform rough processing on the workpiece to be processed according to the actual processing path parameters through the control unit to obtain a processed workpiece; The processing module is further configured to obtain the measured margin of the processed workpiece through the online measurement unit; The processing module is also used to determine the processing parameters of the CNC system based on the measured allowance according to a preset optimization algorithm, and to perform fine processing on the processed workpiece according to the processing parameters through the control unit.

[0139] like Figure 3 As shown, an embodiment of the present invention provides an adjustable CNC program error-proofing processing system, including a memory and a processor; the memory is used to store computer programs; the processor is used to implement the adjustable CNC program error-proofing processing method as described above when executing the computer program.

[0140] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the above-mentioned adjustable CNC program error-proofing machining method is implemented.

[0141] The adjustable numerical control program error-proofing processing system that can be used as the server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The adjustable numerical control program error-proofing processing system is intended to represent various forms of digital electronic computer equipment, such as, laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The adjustable numerical control program error-proofing processing system can also represent various forms of mobile devices, such as, personal digital processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0142] The adjustable CNC program error-proofing machining system includes a computing unit that can execute various actions and processes based on a computer program stored in read-only memory (ROM) or loaded from the storage unit into random access memory (RAM). The RAM also stores various programs and data required for device operation. The computing unit, ROM, and RAM are connected to each other via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0143] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (RM), or a random access memory (RAM). In this application, the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in a single location or distributed across multiple network units. Some or all of these units can be selected based on actual needs to achieve the objectives of the embodiments of the present invention. Furthermore, the functional units in the various embodiments of the present invention can be integrated into a single processing unit, each unit can exist physically separately, or two or more units can be integrated into a single unit. These integrated units can be implemented in either hardware or software functional units.

[0144] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A method for error-proofing machining of adjustable numerical control programs, characterized in that: Applied to a numerical control system, the numerical control system includes a simulation unit, a control unit and an online measurement unit; the adjustable numerical control program error-proofing processing method includes: Establishing a three-dimensional machining model of a workpiece to be processed by a simulation unit; determining initial tool path parameters based on the three-dimensional machining model; performing simulation according to the initial tool path parameters to obtain simulation results, wherein the simulation results include a virtual margin distribution; Adjusting the initial tool path parameters according to the virtual margin distribution to determine actual machining path parameters; Performing rough machining on the workpiece to be processed according to the actual machining path parameters by the control unit to obtain a processed workpiece; Obtaining the measured margin of the processed workpiece by the online measuring unit; According to a preset optimization algorithm, the processing parameters of the numerical control system are determined based on the measured allowance, and the processed workpiece is fine-processed according to the processing parameters through the control unit.

2. The adjustable CNC program error-proofing processing method according to claim 1, characterized in that: The processing parameters include tool compensation data and cutting parameters; the processing parameters of the numerical control system are determined based on the measured allowance according to a preset optimization algorithm, including: Determining a potential out-of-tolerance area based on the simulation result and a preset threshold, and dividing the processed workpiece according to the potential out-of-tolerance area to obtain different processing areas; Based on a preset genetic algorithm, tool compensation data and cutting parameters of the numerical control system are determined according to the actually measured allowances of each processing area.

3. The adjustable CNC program error-proofing processing method according to claim 2, characterized in that: The preset genetic algorithm processing process includes: Initializing population parameters, wherein the population parameters include a plurality of individuals and corresponding individual parameter data; Calculating the fitness data of the corresponding individual according to the measured margins of each processing area and the corresponding individual parameter data, and determining a plurality of parent individuals according to all the fitness data based on a preset roulette wheel selection method; Performing crossover on all the parent individuals to generate new individuals, selecting a preset proportion of the new individuals to perform Gaussian mutation to obtain mutant individuals, and updating the initialized population parameters based on the mutant individuals; The above process is repeated until a preset number of iterations is reached or a stopping condition is satisfied, to obtain the final target variant individual and the corresponding individual parameter data, wherein the individual parameter data includes the tool compensation data and the cutting parameters.

4. The adjustable CNC program error-proofing processing method according to claim 3 is characterized in that: The numerical control system further includes a feedback mechanism unit, which is connected to the online measurement unit. The adjustable numerical control program error-proofing processing method further includes: During the processing of the workpiece to be processed, the wear parameters of the tool are obtained by the online measurement unit at a preset frequency, wherein the wear parameters include the processing parameters, material properties and environmental data; The wear parameters are input into a preset tool wear model through the feedback mechanism unit to obtain a tool wear prediction result; comparing the tool wear prediction result with a set threshold; When the tool wear prediction result is greater than the set threshold, the machining parameters of the numerical control system are adjusted.

5. The adjustable CNC program error-proofing processing method according to claim 4, characterized in that: The process of constructing the preset tool wear model includes: Acquire the simulation processing parameters, simulation material properties, simulation environment data and corresponding label data of the simulation workpiece through simulation experiments; The original wear model is trained and optimized using the simulation processing parameters, the simulation material properties, the simulation environment data and the corresponding label data, and the optimized original wear model is used as the tool wear model.

6. The adjustable CNC program error-proofing processing method according to claim 5, characterized in that: The training and tuning of the original wear model using the simulated processing parameters, the simulated material properties, the simulated environment data, and the corresponding label data, and using the tuned original wear model as the tool wear model, includes: The original wear model is trained according to the simulation processing parameters, the simulation material properties, and the simulation environment data to obtain a temporary tool wear result; According to the temporary tool wear result and the corresponding label data, the loss calculation of the original wear model is performed using a preset loss function to obtain a loss function output; The model parameters of the original wear model are adjusted according to the output of the loss function, and the training is iterated until the output of the loss function meets the preset conditions. The original wear model after parameter adjustment is used as the tool wear model.

7. The adjustable CNC program error-proofing processing method according to claim 1, characterized in that: The adjustable numerical control program error-proofing processing method further comprises: Acquiring tool life data, machining time data, and workpiece quality data, and determining a corresponding temporary score based on the tool life data, the machining time data, and the workpiece quality data; Obtaining comprehensive score data based on each of the temporary scores and corresponding preset weight data; A judgment is made according to the comprehensive score and the preset score threshold to obtain a judgment result, and a recommended adjustment strategy for the corresponding processing parameters is generated based on the judgment result.

8. An adjustable CNC program error-proofing processing equipment, characterized in that: Applied to a numerical control system, the numerical control system includes a simulation unit, a control unit and an online measurement unit, and the adjustable numerical control program error-proofing processing equipment includes: a processing module configured to establish a three-dimensional machining model of a workpiece to be processed through a simulation unit; determine initial tool path parameters based on the three-dimensional machining model; perform simulation according to the initial tool path parameters to obtain simulation results, the simulation results including a virtual margin distribution; and adjust the initial tool path parameters according to the virtual margin distribution to determine actual machining path parameters; a processing module, configured to perform rough processing on the workpiece to be processed according to the actual processing path parameters through the control unit to obtain a processed workpiece; The processing module is further configured to obtain the measured margin of the processed workpiece through the online measurement unit; The processing module is also used to determine the processing parameters of the CNC system based on the measured allowance according to a preset optimization algorithm, and to perform fine processing on the processed workpiece according to the processing parameters through the control unit.

9. An adjustable CNC program error-proofing processing system, characterized in that: It comprises 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 processing method as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that The storage medium stores a computer program, and when the computer program is executed by the processor, the adjustable CNC program error-proofing processing method according to any one of claims 1 to 7 is implemented.

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