Mechanical mold numerical control automatic machining process for rapid recombination and manufacturing

Abstract

The invention discloses a mechanical mold numerical control automatic machining process for rapid recombination manufacturing, and relates to the technical field of mechanical mold machining. The method comprises the following steps: S1, on the basis of functional characteristics and machining precision requirements of a mechanical mold, disassembling the mold into standardized modules; according to the method, a standardized module feature library and a parameter template library are constructed through modular disassembly of a mold structure and parametric modeling of a machining task, rapid remodeling of machining of molds of different specifications and different types is achieved in combination with modular recombination configuration of a numerical control system, the response speed in a multi-variety and small-batch production scene is greatly increased, and the production efficiency is improved. The full-process automation from mold module clamping, tool replacement, machining execution to material conveying and quality detection is achieved, the manual intervention link is reduced, the labor cost is reduced, meanwhile, errors caused by manual operation are avoided, and the stability of machining precision is improved.

Description

Technical Field

[0001] This invention belongs to the field of mechanical mold processing technology, and in particular relates to a CNC automated processing technology for mechanical molds oriented towards rapid reconfiguration manufacturing. Background Technology

[0002] As the manufacturing industry undergoes a profound transformation towards multi-variety, small-batch, and customized production, the market's demands for rapid product iteration continue to rise. Consequently, the processing needs of mechanical molds exhibit significant characteristics of high-frequency model changes and rapid response. Traditional batch-based and fixed mold processing models are no longer sufficient to meet the flexible production needs of the current manufacturing industry, necessitating processing technologies with rapid reconfiguration capabilities to support industry development.

[0003] The current industry standard for mechanical mold processing technology requires separate processing flow planning for different specifications and types of molds, along with dedicated tooling fixtures and processing equipment. Key steps such as equipment debugging, tool changing, processing parameter setting, and mold clamping and positioning are completed manually, forming a traditional model of one processing solution for one mold.

[0004] When faced with mold changeover requirements, the entire process planning and equipment debugging need to be carried out again, resulting in excessively long changeover cycles and extremely poor reconfigurability. The reliance on manual operation for many key links not only increases labor costs but also makes it easy for human error to affect the stability of processing accuracy. The degree of automation is low, and there is a lack of real-time perception and adaptive adjustment mechanisms for abnormal situations such as tool wear, material deviation, and equipment failure during the processing. Once a problem occurs, it can easily lead to the generation of batches of scrap, further increasing production costs.

[0005] To address these issues, we provide a CNC automated machining process for mechanical molds designed for rapid reconfiguration manufacturing. Summary of the Invention

[0006] The purpose of this invention is to provide a CNC automated machining process for mechanical molds for rapid reconfiguration manufacturing, which solves the problems of poor reconfiguration adaptability, low changeover efficiency, and insufficient automation in the traditional mechanical mold machining process of the prior art.

[0007] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution.

[0008] This invention relates to a CNC automated machining process for mechanical molds for rapid reconfiguration manufacturing, comprising the following steps: S1: Based on the functional characteristics and processing accuracy requirements of mechanical molds, the mold is decomposed into standardized modules, and a mold module feature library including module size, material, and processing features is constructed. S2: Based on the feature information of the mold module to be processed, match the basic processing parameters from the preset processing parameter template library, optimize the parameters in combination with the specific size and accuracy requirements of the module, and generate a standardized processing task parameter package; S3: Based on an open CNC system architecture, it calls the logical instructions in the machining task parameter package to complete the dynamic reorganization of machining axis linkage, tool path planning rules, and auxiliary equipment control logic, and realizes the adaptive adaptation of the hardware interface between the CNC system and machining equipment, testing equipment, and material transfer equipment. S4: Based on the reconfigured information, the CNC system drives the machining equipment to complete automatic tool changing, automatic workpiece clamping, and automatic execution of the machining path, and controls the material conveying equipment to realize the delivery of processed materials; S5: The machine vision device collects images of the mold module's processed surface, and the force sensor collects cutting force data during the processing. The machine vision device then collects images of the cutting force during the processing, and the machine dimensional accuracy, surface roughness, and processing stability are detected in real time. If an abnormality is detected, the CNC system is triggered to perform adaptive parameter adjustments.

[0009] The present invention is further configured such that the mold module feature library also includes the processing priority, assembly relationship and compatible processing equipment model information of each module, which are stored in a standardized format to support the rapid retrieval and updating of module information.

[0010] The present invention is further configured such that the machining parameter template library covers standard machining parameters for different materials and machining types, and the parameter optimization process is achieved by combining genetic algorithm with finite element simulation analysis, with the optimization variables being a machining parameter vector composed of cutting speed, feed rate, and depth of cut. A dual-objective fitness function is constructed with the goals of maximizing processing efficiency and minimizing processing error. ,in Let be the processing efficiency function. For machining accuracy function, The weighting coefficients are and satisfy the following conditions: The parameters are optimized through selection, crossover, and mutation iteration operations using a genetic algorithm. Combined with cutting force and cutting temperature data obtained from finite element simulation as constraints, the optimal combination of machining parameters is finally output.

[0011] The present invention is further configured such that S3 adopts a modular programming concept, decomposing the machining control logic into a tool control submodule, an axis linkage control submodule, and an auxiliary equipment control submodule. The machining logic is rapidly recombined through the dynamic combination of submodules, and the hardware interface is adapted using a standardized communication protocol.

[0012] The present invention is further configured such that, in S5, the machine vision device collects images of the surface of the mold module to obtain processing dimensional accuracy and surface roughness data, and the force sensing device collects cutting force fluctuation data during the processing. The two types of data are compared with preset accuracy thresholds and force fluctuation thresholds to realize anomaly judgment. When an anomaly is detected, the parameter adaptive adjustment range is set according to the severity of the anomaly.

[0013] The present invention is further configured to include S6: processing data traceability and optimization, which unifies the parameter configuration information, equipment operation data and quality inspection data during the processing, and continuously updates the processing parameter template library and CNC system reconfiguration strategy by analyzing the correlation between processing parameters and processing quality.

[0014] The present invention is further configured such that, in S1, a functional structure mapping method is used to establish a mapping relationship between function and structure by combining the functional characteristics of the mold in forming, positioning and guiding.

[0015] The present invention is further configured such that, in S3, the hardware interface adaptive adaptation automatically completes the interface parameter configuration by collecting the communication protocol characteristics and pin definition information of the device interface and matching them with a preset interface feature library.

[0016] The present invention is further configured such that when the processing path is automatically executed in S4, the movement speed and acceleration of the path nodes are adjusted according to the processing characteristics of the mold module and the motion characteristics of the equipment.

[0017] The present invention is further configured such that, in S5, the abnormal classification processing selects dimensional deviation, surface defect degree, and cutting force fluctuation amplitude as evaluation indicators, and classifies abnormal levels according to preset standards, with different levels corresponding to different processing strategies.

[0018] The present invention has the following beneficial effects.

[0019] This invention constructs a standardized module feature library and parameter template library by modularly disassembling the mold structure and parametrically modeling the processing tasks. Combined with the modular reconfiguration of the CNC system, it enables rapid changeover for processing molds of different specifications and types, significantly improving the response speed in multi-variety, small-batch production scenarios. It achieves full-process automation from mold module clamping, tool changing, processing execution to material transfer and quality inspection, reducing manual intervention and labor costs. At the same time, it avoids errors caused by manual operation and improves the stability of processing accuracy. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0021] Figure 1This is the main flowchart of a CNC automated machining process for mechanical molds designed for rapid reconfiguration manufacturing.

[0022] Figure 2 This is a flowchart of the modular disassembly and feature library construction process for CNC automated machining of mechanical molds for rapid reconfiguration manufacturing.

[0023] Figure 3 This is a flowchart for optimizing machining parameter matching in a CNC automated machining process for mechanical molds designed for rapid reconfiguration manufacturing.

[0024] Figure 4 This is a flowchart illustrating the dynamic reconfiguration of a CNC system in a CNC automated machining process for mechanical molds designed for rapid reconfiguration manufacturing.

[0025] Figure 5 This is a flowchart of online detection and anomaly classification processing in a CNC automated machining process for mechanical molds designed for rapid reconfiguration manufacturing. Detailed Implementation

[0026] The technical solutions of the present invention will be described below with reference to the accompanying drawings. The described embodiments are only some embodiments of the present invention, and not all embodiments.

[0027] Please see Figure 1-5 This invention relates to a CNC automated machining process for mechanical molds for rapid reconfiguration manufacturing, comprising the following steps: S1: First, based on the functional characteristics of mechanical molds in forming, positioning, guiding, and venting, as well as the machining accuracy requirements of dimensional tolerances, surface roughness, and geometric tolerances, a modular design concept is adopted to decompose the complete mechanical mold into several standardized modules, such as cavity modules, core modules, guiding modules, positioning modules, and ejection modules. Then, feature information is collected for each standardized module, including the module's dimensional parameters, material information, machining features, machining priority, assembly relationships, and compatible machining equipment models. Finally, the collected module feature information is stored in a standardized format to build a mold module feature library, supporting the rapid retrieval, calling, and updating of module information.

[0028] S2: Based on the feature information of the mold module to be processed, a quick search is performed through the mold module feature library to match the corresponding basic machining parameter template. The machining parameter template library presets standard machining parameters for different materials and machining types, including cutting speed, feed rate, depth of cut, tool type, and machining allowance. Subsequently, combined with the specific size and accuracy requirements of the module to be processed, a genetic algorithm combined with finite element simulation analysis is used to optimize the parameters. The specific process is as follows: with cutting speed, feed rate, and depth of cut as optimization variables, and with the goal of maximizing machining efficiency and minimizing machining error, a fitness function is constructed. Iterative optimization is performed through selection, crossover, and mutation operations of the genetic algorithm, and cutting force and cutting temperature data obtained by finite element simulation are used as constraints. Finally, the optimal combination of machining parameters is obtained, and a standardized machining task parameter package containing machining logic instructions, parameter configuration information, and a draft toolpath planning is generated.

[0029] S3: Based on an open CNC system architecture, it calls the logical instructions in the machining task parameter package generated by S2. It adopts a modular programming concept to decompose the machining control logic into several functional sub-modules such as tool control sub-module, axis linkage control sub-module, and auxiliary equipment control sub-module. Through the dynamic combination of sub-modules, the machining logic can be quickly reorganized. At the same time, the CNC system is based on a standardized communication protocol to automatically identify the hardware interface types of machining equipment, inspection equipment, and material transfer equipment, complete the adaptive configuration of interface parameters, and realize the collaborative linkage between various devices.

[0030] S4: Based on the reconfigured information, the CNC system issues control commands to control the robotic arm to automatically grasp and clamp the mold module to be processed, control the tool magazine to automatically change the corresponding tools, and simultaneously start the cooling and lubrication system. According to the planned tool path, it drives the machining axes of the machining equipment to move in coordination to complete the automated machining of the mold module. During this process, the material conveying equipment automatically delivers the module to be processed to the machining station according to the processing progress, and transfers the processed module to the inspection station, realizing the fully automated flow of the processing process.

[0031] Step S5: During the machining process, the machine vision equipment collects image information of the machined surface of the mold module in real time to detect defects such as roughness, scratches, and burrs on the machined surface. At the same time, the machining dimensional accuracy is measured. The cutting force data during the machining process is collected by the force sensing equipment to judge tool wear and abnormal machining load. The detected quality data is compared with the preset accuracy threshold and force fluctuation threshold. If an abnormality is detected, the CNC system handles it according to the severity of the abnormality. In case of minor abnormality, the cutting speed, feed rate and other machining parameters are automatically adjusted. In case of severe abnormality, the machining is immediately suspended and an alarm signal is issued to remind the staff to troubleshoot the fault.

[0032] Step S6: Store the parameter configuration information, equipment operation data, and quality inspection data during the processing into a unified database to form a complete processing data traceability chain, which facilitates the traceability and analysis of subsequent product quality issues. Based on the accumulation and analysis results of processing data, continuously update the standard parameters in the processing parameter template library and optimize the reconfiguration strategy of the CNC system to further improve the adaptability of the process and the processing accuracy.

[0033] The mold module feature library also includes the processing priority, assembly relationship and compatible processing equipment model information of each module, which are stored in a standardized format to support the rapid retrieval and updating of module information.

[0034] The machining parameter template library covers standard machining parameters for different materials and machining types. The parameter optimization process is achieved using a genetic algorithm combined with finite element simulation analysis. The optimization variables are a machining parameter vector consisting of cutting speed, feed rate, and depth of cut. A dual-objective fitness function is constructed with the goals of maximizing processing efficiency and minimizing processing error. ,in Let be the processing efficiency function. For machining accuracy function, The weighting coefficients are and satisfy the following conditions: The parameters are optimized through selection, crossover, and mutation iteration operations using a genetic algorithm. Combined with cutting force and cutting temperature data obtained from finite element simulation as constraints, the optimal combination of machining parameters is finally output.

[0035] S3 adopts a modular programming approach, breaking down the machining control logic into tool control sub-modules, axis linkage control sub-modules, and auxiliary equipment control sub-modules. The machining logic can be quickly reassembled through the dynamic combination of sub-modules, and the hardware interface is adapted using a standardized communication protocol.

[0036] In S5, machine vision equipment is used to collect images of the surface of the mold module to obtain data on machining dimensional accuracy and surface roughness. Combined with force sensing equipment to collect cutting force fluctuation data during the machining process, the two types of data are compared with preset accuracy thresholds and force fluctuation thresholds to achieve anomaly judgment. When an anomaly is detected, the parameters are adaptively adjusted according to the severity of the anomaly.

[0037] It also includes S6: processing data traceability and optimization, which unifies the storage of parameter configuration information, equipment operation data and quality inspection data during the processing process, and continuously updates the processing parameter template library and CNC system reconfiguration strategy by analyzing the correlation between processing parameters and processing quality.

[0038] S1 employs a functional structure mapping method, combining the forming, positioning, and guiding functional characteristics of the mold to establish a mapping relationship between function and structure, ensuring that the disassembled standardized modules have the characteristics of independent processing and independent assembly.

[0039] In S3, hardware interface adaptive adaptation automatically completes interface parameter configuration by collecting the communication protocol characteristics and pin definition information of the device interface and matching them with a preset interface feature library.

[0040] When the machining path is executed automatically in S4, the movement speed and acceleration of the path nodes are adjusted according to the machining characteristics of the mold module and the motion characteristics of the equipment.

[0041] In S5, the anomaly classification process selects dimensional deviation, surface defect degree, and cutting force fluctuation as evaluation indicators. Anomalies are classified into different levels according to preset standards, and different levels correspond to different processing strategies.

[0042] Example 1 For automotive engine block forming molds, it is necessary to meet the requirements of rapid changeover processing for multiple batches and different displacement specifications. The specific implementation steps are as follows: S1: Using the functional structure mapping method, combined with the forming, positioning, and ejection functional characteristics and machining accuracy requirements of the mold, it is decomposed into four standardized modules: cavity module, core module, guide module, and ejection module. The size parameters, material information, machining features, and assembly relationship information of each module are collected and stored in a standardized format to build a mold module feature library, which supports the rapid retrieval of modules corresponding to each displacement specification.

[0043] S2: Retrieve information about each module from the mold module feature library, match the basic parameters of the corresponding material and processing type in the processing parameter template library, and combine the specific accuracy requirements of each module. Use genetic algorithm combined with finite element simulation analysis to optimize parameters. Use cutting speed, feed rate and depth of cut as optimization variables to construct a dual-objective fitness function. Through iterative operation of genetic algorithm and constraint screening of finite element simulation, obtain the optimal combination of processing parameters and generate standardized processing task parameter packages for each module.

[0044] S3: It adopts an open CNC system, calls the logical instructions in the machining task parameter package of each module, and decomposes the machining control logic into tool control, axis linkage control and auxiliary equipment control function sub-modules. The sub-modules are dynamically combined according to the machining requirements of each module of the cylinder mold. Through standardized communication protocols, the hardware interface between the CNC system and the machining center, machine vision equipment, force sensing equipment and robotic arm is adaptively adapted to achieve collaborative linkage of each device.

[0045] S4: The CNC system issues control commands, the robotic arm grabs the module to be processed from the material rack and clamps it onto the worktable of the machining center. The tool magazine automatically changes the corresponding tool according to the parameter package information. The cooling and lubrication system starts simultaneously. The machining center drives each machining axis to move in coordination according to the planned tool path to complete the module processing. During the processing, the material conveying equipment automatically delivers the module to be processed and transfers the processed module according to the processing progress, realizing the fully automated flow.

[0046] S5: The machine vision equipment acquires images of the machined surface in real time, detects defects such as surface roughness and scratches, and measures dimensional accuracy. The force sensing equipment collects cutting force data during the cutting process, judges problems such as tool wear and abnormal machining load. When a slight abnormality is detected, the CNC system automatically adjusts parameters such as cutting speed and feed rate. When a serious abnormality is detected, the machining is immediately paused and an alarm signal is issued.

[0047] S6: Store the parameter configuration, equipment operation, quality inspection and other data of this processing in the database to form a complete traceability chain. By analyzing the correlation between processing parameters and processing quality, update the standard parameters of the corresponding modules in the processing parameter template library, optimize the CNC system reconfiguration strategy, and provide better support for the processing of molds with different displacement specifications in the future.

[0048] Example 2 The mold for the housing of a miniature electronic connector is characterized by its small size, fine machining features, and extremely high changeover frequency. The specific implementation steps are as follows: S1: Based on the precision forming and positioning characteristics of the mold, the functional structure mapping method is used to decompose it into three standardized modules: cavity module, core module, and positioning module. Information such as material, fine machining features, assembly relationship and compatible processing equipment model of each module is collected and stored in the mold module feature library in a standardized format. The fine machining parts of each module are marked with emphasis.

[0049] S2: Retrieve information from each module in the feature library, match the basic parameters suitable for micro mold processing in the processing parameter template library, and optimize the processing parameters by combining the fine processing requirements of the module with genetic algorithm and finite element simulation analysis. Focus on adjusting the constraint range of optimization variables for fine processing parts, and generate a standardized task parameter package suitable for micro mold processing.

[0050] S3: Call the parameter package logic instruction to decompose the machining control logic into functional sub-modules such as precision tool control, high-precision axis linkage control, and micro material transfer control, and dynamically combine them. Through standardized communication protocols, complete the hardware interface adaptation between the CNC system and high-precision machining centers, high-resolution machine vision equipment, and micro robotic arms to ensure that the linkage accuracy between devices meets the requirements.

[0051] S4: The micro robotic arm grabs and clamps the micro modules to be processed according to instructions. During the clamping process, visual-assisted positioning ensures clamping accuracy. The tool magazine is automatically replaced with precision machining tools. The cooling and lubrication system starts in micro-lubrication mode. The machining center drives the machining axes to move in high-precision coordination according to the planned path to complete the fine feature processing of the module. The material transfer equipment delivers and transfers the micro modules according to the schedule to avoid module damage.

[0052] S5: The high-resolution machine vision equipment focuses on detecting the dimensional accuracy and surface defects of finely machined parts. The force sensing equipment collects cutting force data during the fine cutting process, judges the wear of micro tools, and classifies and handles them according to the severity of the abnormality to ensure the processing quality of micro molds.

[0053] S6: Store the entire processing data, analyze the correlation between fine machining parameters and machining quality, update the standard parameters for micro mold machining in the parameter template library, optimize the control logic for fine machining in the CNC system, and improve the efficiency of subsequent changeover machining of similar electronic component molds.

[0054] Example 3 For the mold of the outer shell of household refrigerator door panel, which has a complex structure and includes large-area curved surface processing features, it needs to meet the requirements of rapid model changeover for different door panel models. The specific implementation steps are as follows: S1: Combining the surface forming, edge positioning, and overall ejection functions of the mold, the functional structure mapping method is used to decompose it into three standardized modules: surface cavity module, edge positioning module, and overall ejection module. Information such as surface processing features, material information, and assembly relationships of each module are collected and stored in a standardized format to build a mold module feature library, with a focus on recording surface feature parameters.

[0055] S2: Retrieve information from each module in the feature library, match the basic parameters corresponding to surface machining in the machining parameter template library, combine the machining requirements of the door panel mold surface, optimize the parameters through genetic algorithm combined with finite element simulation analysis, focus on adjusting and optimizing variables for the adaptability of the cutting path for surface machining, and generate a standardized task parameter package containing the initial draft of the surface machining path planning.

[0056] S3: Call the parameter package logic instruction to decompose the machining control logic into functional sub-modules such as surface tool control, multi-axis linkage control, and large material transfer control, and dynamically combine them. Through standardized communication protocols, complete the hardware interface adaptation between the CNC system and large machining centers, surface inspection vision equipment, and heavy-duty robotic arms to ensure the equipment linkage stability of large module processing.

[0057] S4: The heavy-duty robotic arm grabs the large door panel mold module and clamps it onto the machining center's worktable. The tool magazine is automatically replaced with a special tool for surface machining. The cooling and lubrication system is activated and sprays oil on the surface machining area. The machining center drives multiple machining axes to work together to complete the machining of surfaces and other features according to the planned surface tool path. The material transfer equipment transfers the large machining module according to the schedule to ensure a smooth machining process.

[0058] S5: The vision device acquires images of the machined curved surface in real time, and detects the surface roughness and contour accuracy; the force sensing device acquires cutting force data during the surface cutting process, judges the tool load and wear, and in case of abnormalities in surface machining, the CNC system adaptively adjusts the machining parameters or pauses machining to ensure the quality of surface machining.

[0059] S6: Stores the entire processing data for this operation, focuses on analyzing the correlation between surface processing parameters and surface processing quality, updates the standard parameters for surface processing in the parameter template library, optimizes the multi-axis linkage surface processing control logic in the CNC system, and provides support for the rapid changeover processing of molds for different models of home appliances.

[0060] The preferred embodiments of the present invention disclosed above are only for the purpose of illustrating the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation described herein. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can better understand and utilize the present invention.

Claims

1. A CNC automated machining process for mechanical molds for rapid reconfiguration manufacturing, characterized in that: Includes the following steps: S1: Based on the functional characteristics and processing accuracy requirements of mechanical molds, the mold is decomposed into standardized modules, and a mold module feature library including module size, material, and processing features is constructed. S2: Based on the feature information of the mold module to be processed, match the basic processing parameters from the preset processing parameter template library, optimize the parameters in combination with the specific size and accuracy requirements of the module, and generate a standardized processing task parameter package; S3: Based on an open CNC system architecture, it calls the logical instructions in the machining task parameter package to complete the dynamic reorganization of machining axis linkage, tool path planning rules, and auxiliary equipment control logic, and realizes the adaptive adaptation of the hardware interface between the CNC system and machining equipment, testing equipment, and material transfer equipment. S4: Based on the reconfigured information, the CNC system drives the machining equipment to complete automatic tool changing, automatic workpiece clamping, and automatic execution of the machining path, and controls the material conveying equipment to realize the delivery of processed materials; S5: The machine vision device collects images of the mold module's processed surface, and the force sensor collects cutting force data during the processing. The machine vision device then collects images of the cutting force during the processing, and the machine dimensional accuracy, surface roughness, and processing stability are detected in real time. If an abnormality is detected, the CNC system is triggered to perform adaptive parameter adjustments.

2. The CNC automated machining process for mechanical molds for rapid reconfiguration manufacturing according to claim 1, characterized in that: The mold module feature library also includes the processing priority, assembly relationship and compatible processing equipment model information of each module, which are stored in a standardized format to support rapid retrieval and updating of module information.

3. The CNC automated machining process for mechanical molds for rapid reconfiguration manufacturing according to claim 1, characterized in that: The machining parameter template library covers standard machining parameters for different materials and machining types. The parameter optimization process is achieved using a genetic algorithm combined with finite element simulation analysis. The optimization variables are a machining parameter vector consisting of cutting speed, feed rate, and depth of cut. A dual-objective fitness function is constructed with the goals of maximizing processing efficiency and minimizing processing error. ,in Let be the processing efficiency function. For machining accuracy function, The weighting coefficients are and satisfy the following conditions: The parameters are optimized through selection, crossover, and mutation iteration operations using a genetic algorithm. Combined with cutting force and cutting temperature data obtained from finite element simulation as constraints, the optimal combination of machining parameters is finally output.

4. The CNC automated machining process for mechanical molds for rapid reconfiguration manufacturing according to claim 1, characterized in that: The S3 adopts a modular programming approach, breaking down the machining control logic into a tool control submodule, an axis linkage control submodule, and an auxiliary equipment control submodule. The machining logic is rapidly recombined through the dynamic combination of submodules, and the hardware interface is adapted using a standardized communication protocol.

5. The CNC automated machining process for mechanical molds for rapid reconfiguration manufacturing according to claim 1, characterized in that: In step S5, machine vision equipment is used to collect images of the surface of the mold module to obtain data on processing dimensional accuracy and surface roughness. Combined with force sensing equipment to collect data on cutting force fluctuations during the processing, the two types of data are compared with preset accuracy thresholds and force fluctuation thresholds to achieve anomaly judgment. When an anomaly is detected, the parameter adaptive adjustment range is set according to the severity of the anomaly.

6. The CNC automated machining process for mechanical molds for rapid reconfiguration manufacturing according to claim 1, characterized in that: It also includes S6: processing data traceability and optimization, which unifies the storage of parameter configuration information, equipment operation data and quality inspection data during the processing process, and continuously updates the processing parameter template library and CNC system reconfiguration strategy by analyzing the correlation between processing parameters and processing quality.

7. The CNC automated machining process for mechanical molds for rapid reconfiguration manufacturing according to claim 1, characterized in that: The S1 method uses a functional structure mapping method, which combines the forming, positioning and guiding functional characteristics of the mold to establish a mapping relationship between function and structure.

8. The CNC automated machining process for mechanical molds for rapid reconfiguration manufacturing according to claim 1, characterized in that: In S3, the hardware interface adaptive adaptation automatically completes the interface parameter configuration by collecting the communication protocol characteristics and pin definition information of the device interface and matching them with a preset interface feature library.

9. The CNC automated machining process for mechanical molds for rapid reconfiguration manufacturing according to claim 1, characterized in that: When the processing path is executed automatically in S4, the movement speed and acceleration of the path nodes are adjusted according to the processing characteristics of the mold module and the motion characteristics of the equipment.

10. The CNC automated machining process for mechanical molds for rapid reconfiguration manufacturing according to claim 1, characterized in that: In S5, the anomaly classification process selects dimensional deviation, surface defect degree, and cutting force fluctuation amplitude as evaluation indicators, and classifies anomalies into different levels according to preset standards. Different levels correspond to different processing strategies.

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