Production control method suitable for flexible electromachining of precision parts and medium

Abstract

The invention discloses a production management and control method suitable for flexible electromachining of precision parts and a medium, and relates to the technical field of electromachining, the method comprises the following steps: after a production task of a product is received, establishing a correlation model among a process route, a process, an operation unit and a task execution item of the product; determining a plurality of electromachining devices based on the correlation model, and collecting state data of each electromachining device in real time; determining optimal electromachining equipment for executing the current production task; judging whether the current production task needs a cutter or not based on the association model, if so, intelligently selecting the cutter, automatically controlling to take and feed the cutter, and executing the production task; if not, directly executing the production task; and judging whether continuous production is needed or not, and if yes, continuing production. According to the invention, the rigid binding of the traditional process and equipment is broken, the adaptability of the production line to multi-variety and small-batch orders is improved, the production process is optimized, the manual intervention is reduced, and the production efficiency and the resource utilization rate are improved.

Description

Technical Field

[0001] This invention relates to the field of electrical discharge machining (EDM) technology, and in particular to a production control method and medium suitable for flexible EDM of precision parts. Background Technology

[0002] The aerospace industry demands extremely high precision, consistency, and reliability in the machining of precision components. With the rapid development of new aerospace equipment, the demand for precision components in this field continues to grow. However, existing electrical discharge machining (EDM) production lines generally face the following challenges: insufficient flexibility, making it difficult to adapt to the demand for multi-variety, small-batch orders; reliance on extensive manual labor, resulting in high labor costs and frequent tool breakage; and chaotic process management, leading to low production efficiency and resource utilization. These problems severely restrict the improvement of precision component manufacturing capabilities in the aerospace industry.

[0003] Current electrical discharge machining (EDM) production control methods primarily rely on fixed process routes and manual scheduling, organizing the production process through pre-set machining programs and fixed equipment allocation. In terms of tool management, existing technologies employ manual recording and tool selection, relying on operator experience for tool changes and scheduling. Production status monitoring is typically achieved through manual inspections and periodic data recording, while equipment coordination depends on a unified central control system.

[0004] The current production line model suffers from numerous manual interventions and a lack of intelligent dynamic scheduling mechanisms, resulting in the following technical deficiencies: insufficient production line collaboration and flexible scheduling capabilities, hindering rapid response to multi-variety, small-batch order demands; rigid binding of process resources, restricting production efficiency improvements; low levels of automation and intelligence in key auxiliary processes, leading to frequent tool malfunctions and damage; and a lack of transparency and refined control over the production process, impacting production efficiency and resource utilization. These deficiencies directly result in low equipment utilization, insufficient on-time order delivery, and limited production line reconfiguration capabilities.

[0005] Based on the aforementioned shortcomings of existing technologies, there is an urgent need to develop a new production control method that can achieve intelligent dynamic scheduling, flexible process configuration, and full-process automated control, in order to improve the production efficiency and quality stability of precision component EDM. Summary of the Invention

[0006] The technical problem to be solved by this invention is to address the shortcomings of existing technologies, specifically by providing a production control method and medium suitable for flexible electrical discharge machining of precision parts, as detailed below: 1) In a first aspect, the present invention provides a production control method applicable to flexible electrical discharge machining of precision parts, the specific technical solution of which is as follows: Once a production task for a product is received, a model is established to link the product's process route, procedures, work units, and task execution items. Multiple electrical discharge machining (EDM) devices are identified based on an association model, and the status data of each EDM device is collected in real time. Based on the correlation model and the status data of each electrical discharge machining (EDM) device, and combined with the idle time of each EDM device, the optimal EDM device for performing the current production task is determined. Based on the correlation model, it is determined whether the current production task requires tools. If so, tools are intelligently selected and tool feeding is automatically controlled to execute the production task; otherwise, the production task is executed directly. Determine whether production needs to continue. If so, return to the step of collecting real-time status data for each electrical discharge machining (EDM) device and continue production.

[0007] The beneficial effects of the production control method for flexible electrical discharge machining of precision parts provided by this invention are as follows: By establishing a correlation model between the product's process route, operations, work units, and task execution items, flexible allocation of process resources is achieved, effectively solving the problem of rigid binding between traditional process routes and equipment, and improving the production line's adaptability to multi-variety, small-batch orders. Based on the correlation model, multiple EDM (Electrical Discharge Machining) devices are identified in real time, and their status data is collected. Intelligent scheduling decisions are made based on equipment idle time, enabling dynamic selection of the optimal EDM device, thereby improving equipment utilization and on-time order delivery. The correlation model intelligently judges tool requirements and automatically executes tool selection and loading / unloading processes, achieving intelligent management of the entire tool lifecycle, reducing manual intervention, lowering tool damage rates, and ensuring stable machining quality. The entire production control process forms a continuously optimizing cycle, achieving transparent and refined control of the production process, ultimately achieving the beneficial effects of improving production efficiency, reducing labor costs, and increasing resource utilization.

[0008] Based on the above solution, the production control method of the present invention applicable to flexible electrical discharge machining of precision parts can be further improved as follows.

[0009] Furthermore, establish a relationship model between the product's process route, procedures, work units, and task execution items, including: Define the product's technological route and process sequence; Each process is bound to multiple executable work units; Configure appropriate electrical discharge machining equipment for each work unit; Define the auxiliary tasks that need to be performed before and after the task execution item of each work unit. The auxiliary tasks include at least one of workpiece clamping, positioning, cleaning or inspection.

[0010] The beneficial effects of adopting the above-mentioned further solution are as follows: Current EDM production lines face problems such as chaotic process route management and rigid binding of process resources, resulting in insufficient flexibility and low production efficiency. By establishing a correlation model between the product's process route, operations, work units, and task execution items, the product's process route and operation sequence are defined. Multiple executable work units are bound to each operation, corresponding EDM equipment is configured for each work unit, and auxiliary tasks to be performed before and after each work unit's task execution item are defined, including workpiece clamping, positioning, cleaning, or inspection. This achieves flexible reconfiguration of the process route and dynamic allocation of production resources. This correlation model breaks the rigid binding of traditional processes and equipment, improves the production line's adaptability to multi-variety, small-batch orders, optimizes the production process, reduces manual intervention, and improves production efficiency and resource utilization.

[0011] Furthermore, based on the correlation model and the status data of each electrical discharge machining (EDM) device, and combined with the idle time of each EDM device, the optimal EDM device for performing the current production task is determined, including: Calculate the idle time period for each electrical discharge machining (EDM) machine based on its planned production schedule, and determine the idle time index for each EDM machine based on its idle time period. Based on the correlation model, the location correlation between production tasks and electrical discharge machining equipment is analyzed; The status data, idle time index, and location correlation of each electrical discharge machining (EDM) device are evaluated from multiple dimensions to determine the optimal EDM device.

[0012] The beneficial effects of adopting the above-mentioned further solution are as follows: Current EDM production lines suffer from a lack of intelligent dynamic scheduling mechanisms, resulting in insufficient production line coordination and flexible scheduling capabilities. This leads to low equipment utilization, inadequate order delivery timeliness, and uneven distribution of production resources. By using a correlation model and the status data of each EDM device, combined with the idle time of each device, the optimal EDM device for executing the current production task is determined. This includes calculating idle time periods and determining the idle time index based on the planned production schedule, analyzing the positional correlation between production tasks and EDM devices based on the correlation model, and conducting multi-dimensional evaluation of status data, idle time index, and positional correlation. This achieves intelligent allocation and optimized scheduling of production tasks. This method effectively improves the flexibility of the production line, ensures efficient equipment operation, reduces production interruptions, and improves order delivery efficiency. Simultaneously, by optimizing material flow through positional correlation analysis, transportation time is reduced, further improving overall production efficiency.

[0013] Furthermore, intelligent tool selection and automatic control of tool feeding and handling are implemented to execute production tasks, including: The process requirements of the current production task are obtained based on the association model; Queries all tools in the tool library that meet the process requirements; compares the remaining life of each tool that meets the requirements; Select the tool with the longest remaining life as the optimal tool; Perform production tasks using the optimal cutting tools.

[0014] The beneficial effects of adopting the above-mentioned further solution are as follows: Currently, EDM production lines rely on manual experience in tool management, leading to problems such as improper tool selection and frequent abnormal tool damage, directly affecting machining quality and causing production interruptions. By obtaining process requirements based on an association model, querying the tool library for tools that meet the requirements, comparing their remaining lifespan, and selecting the tool with the longest remaining lifespan as the optimal tool, and automatically controlling tool handling to execute production tasks, precise tool selection and automated tool handling processes are achieved. This method ensures that the tool in optimal condition is always used during the machining process, effectively avoiding quality defects caused by tool wear or improper selection, reducing abnormal tool damage, and simultaneously reducing the need for manual intervention through automated operation, improving tool utilization efficiency and production continuity, and ensuring the stability and consistency of machining quality.

[0015] 2) In a second aspect, the present invention also provides a production control system suitable for flexible electrical discharge machining of precision parts, the specific technical solution of which is as follows: It includes a correlation model establishment module, a state data acquisition module, an optimal electrical discharge machining equipment determination module, and a generation module; The association model building module is used to: after receiving the production task of a product, establish an association model between the product's process route, operation, work unit and task execution item; The status data acquisition module is used to: identify multiple electrical discharge machining (EDM) devices based on an association model, and collect the status data of each EDM device in real time; The optimal electrical discharge machining (EDM) equipment determination module is used to: determine the optimal EDM equipment for performing the current production task based on the correlation model and the status data of each EDM equipment, and in combination with the idle time of each EDM equipment; The generation module is used to: determine whether the current production task requires tools based on the association model; if so, intelligently select tools and automatically control tool loading and unloading to execute the production task; if not, directly execute the production task. The generation module is also used to: determine whether production needs to continue; if so, return to the steps of collecting real-time status data of each electrical discharge machining device and continue production.

[0016] Based on the above solution, the production control system of the present invention, which is suitable for flexible electrical discharge machining of precision parts, can be further improved as follows.

[0017] Furthermore, the association model building module is specifically used for: Define the product's technological route and process sequence; Each process is bound to multiple executable work units; Configure appropriate electrical discharge machining equipment for each work unit; Define the auxiliary tasks that need to be performed before and after the task execution item of each work unit. The auxiliary tasks include at least one of workpiece clamping, positioning, cleaning or inspection.

[0018] Furthermore, the optimal electrical discharge machining (EDM) equipment determination module is specifically used for: Calculate the idle time period for each electrical discharge machining (EDM) machine based on its planned production schedule, and determine the idle time index for each EDM machine based on its idle time period. Based on the correlation model, the location correlation between production tasks and electrical discharge machining equipment is analyzed; The status data, idle time index, and location correlation of each electrical discharge machining (EDM) device are evaluated from multiple dimensions to determine the optimal EDM device.

[0019] Furthermore, the generation module is also specifically used for: The process requirements of the current production task are obtained based on the association model; Queries all tools in the tool library that meet the process requirements; compares the remaining life of each tool that meets the requirements; Select the tool with the longest remaining life as the optimal tool; Perform production tasks using the optimal cutting tools.

[0020] 3) In a third aspect, the present invention also provides an electronic device, the electronic device including a processor coupled to a memory, the memory storing at least one computer program, the at least one computer program being loaded and executed by the processor, so that the electronic device implements any of the above-mentioned production control methods applicable to flexible electrical discharge machining of precision parts.

[0021] 4) In a fourth aspect, the present invention also provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements any of the above-mentioned production control methods applicable to flexible electrical discharge machining of precision parts.

[0022] It should be noted that the beneficial effects of the technical solutions of the second to fourth aspects of the present invention and their corresponding possible implementations can be found in the above description of the technical effects of the first aspect and its corresponding possible implementations, and will not be repeated here. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below: Figure 1 This is a schematic flowchart of a production control method for flexible electrical discharge machining of precision parts according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a production control system for flexible electrical discharge machining of precision parts according to an embodiment of the present invention. Detailed Implementation

[0024] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0025] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0026] like Figure 1 As shown, an embodiment of the present invention provides a production control method for flexible electrical discharge machining of precision parts, comprising the following steps: S1. Upon receiving the production task for the product, establish a correlation model between the product's process route, procedures, work units, and task execution items. This includes establishing a relationship model between the product's process route, procedures, work units, and task execution items, including: S10. Define the product's technological route and process sequence, specifically: S100. Input basic product information, including product number, product name, and product specifications, through the user interface. This data is stored in the system's product database as the basis for defining process routes. Verify the completeness and format of the input data to ensure there are no omissions or errors.

[0027] S101. Based on product type and processing requirements, retrieve relevant processes from the predefined process library. The process library stores standard process templates, including process number, process name, process description, and required resources. An initial process list is automatically generated for user reference and selection. Users can quickly locate the required process using the search or filtering functions.

[0028] S102. Users can arrange the order of processes by dragging and dropping process icons through a graphical interface. A directed graph data structure is used to represent the process sequence, where each node represents a process and edges represent the sequential relationships between processes. For example, for product P, its process sequence S can be represented as: in, Indicates the product's process sequence. Indicates the first process. This indicates the second process. This represents the nth process step. The system verifies the sequence's validity in real time, ensuring there are no circular dependencies or conflicts, and uses algorithms to detect deadlocks or redundant paths.

[0029] S103. For each process, the user needs to input or select specific parameters, such as processing time, accuracy requirements, and equipment type. This data is collected through a form interface and linked to the corresponding processes in the process sequence. For example, for process... Its parameters include processing time and accuracy requirements ,in Indicate process Standard execution time Indicate process The machining accuracy level is determined. Based on these parameters, resource requirements and estimated completion time are automatically calculated.

[0030] S104. Save the defined product process routes and operation sequences to a relational database. The database table structure includes a product table, an operation table, and a process route association table. The product table stores product information, the operation table stores operation details, and the process route association table stores the mapping relationship between products and operation sequences. SQL transactions ensure data consistency and integrity, and support concurrent access and version control.

[0031] S105. Automatically checks the completeness and feasibility of process sequences, such as detecting potential bottlenecks through simulated execution. Users can preview the process route flowchart and make adjustments. All change history is recorded, version management is supported, and report generation functionality is provided to analyze process route efficiency and resource utilization.

[0032] The product's process route refers to the complete path of all processes a product undergoes during production, including the specific operations, execution sequence, and related resource requirements for each process. The product's process route defines the entire processing flow from raw materials to finished product, ensuring the standardization and consistency of the production process. For example, for a precision component, the product's process route might include rough machining, precision machining, inspection, and packaging, each with specific equipment and manpower requirements.

[0033] In this context, the process sequence refers to the order in which the various processes within a product's manufacturing process are arranged, i.e., the order in which the processes are executed. The process sequence clearly defines the dependencies between each process, ensuring the continuity and efficiency of production tasks. For example, the process sequence might stipulate that the electrical discharge machining (EDM) process must be completed before subsequent cleaning and inspection processes can proceed, in order to avoid cross-contamination or quality risks.

[0034] S11. Bind multiple executable work units to each process. The specific implementation process is as follows: S110. Retrieve the defined process list and executable job cell library from the database. The executable job cell library stores all available executable job cells, each including a cell number, cell name, cell description, required equipment type, and execution parameters. The process list and executable job cell library are displayed through the user interface for user selection and management.

[0035] S111. Select multiple executable work units for each process. Specifically, the user selects multiple executable work units from the executable work unit library for each process through a graphical interface. For example, for the i-th process... Users can select a set of executable job units. , Indicates binding to the process. The j-th executable job unit, where m represents the process bound to the job. The number of executable job units. Allows users to add or remove executable job units by dragging and dropping or by checking boxes, and displays the binding status in real time.

[0036] S112. For each executable job unit bound to each process, the user needs to set priority weights and execution conditions. Priority weights are used to select the optimal job unit from multiple executable job units, and execution conditions include equipment availability, resource constraints, or time requirements. For example, for executable job units... , The priority weight is expressed as A higher value indicates a higher priority selection, and the execution condition is expressed as follows: , Specifically, it refers to executable work units. Triggering conditions, such as device status or task type. These parameters are entered via a form and stored in the associated table.

[0037] S113. Use a binding table in a relational database to store the many-to-many relationship between operations and executable job units. The binding table includes fields such as operation number, executable job unit number, priority weight, and execution conditions. For example, the binding relationship... It can be represented as a series of tuples Each tuple records the binding details of a process and an executable job unit. Database transactions ensure data consistency and integrity, and support batch updates and rollback operations.

[0038] S114. Automatically check whether the executable job units bound to each process meet resource compatibility and logical consistency requirements. For example, verify the dependencies between executable job units through simulated execution and detect any resource conflicts or circular bindings. Users can preview the binding results and make adjustments. Generate a report listing all binding relationships and potential problems for user reference and optimization.

[0039] S115. Once the binding relationship is established, the optimal executable job unit is dynamically selected for task allocation based on the bound executable job unit and real-time production status. This ensures that the binding data is synchronized with production resources in real time, supporting dynamic adjustments and updates.

[0040] An executable work unit (AMU) refers to the smallest unit of work that can be independently executed in the production process. It includes specific operational instructions, resource requirements, and execution conditions. Each AMU corresponds to a specific production activity, such as machining, inspection, or handling, and can be associated with specific production equipment or resources. Executable work units ensure the modularity and flexibility of production tasks, allowing a single process to be implemented through multiple selectable AMUs, thus adapting to different production scenarios and resource availability. For example, in a precision parts EDM production environment, suppose there is a "surface finishing" process. This process can be implemented by binding multiple different AMUs, each representing a specific machining method: the first AMU is numbered U-EDM-01, corresponding to the "three-axis EDM finishing" activity. The first executable work unit contains a complete sequence of operation instructions, including electrode installation, parameter setting, and machining path planning. Resource requirements explicitly specify the need for a three-axis EDM (Electrical Discharge Machining) machine (model EDM-300), dedicated copper electrode tools, and industrial coolant. Execution conditions are limited to production scenarios where the workpiece hardness is greater than HRC50 and the machining accuracy requirement is ±0.01mm. The second executable work unit, numbered U-EDM-02, corresponds to the "Five-Axis EDM Finishing" activity. This executable work unit contains five-axis simultaneous machining instructions, supporting complex surface machining. Resource requirements specify a five-axis EDM machine (model EDM-500) and special graphite electrode tools. Execution conditions are suitable for production scenarios where the workpiece has complex geometric features such as deep grooves and narrow slits, and the machining accuracy requirement is ±0.005mm. The third executable work unit, numbered U-WCM-01, corresponds to the "Slow Wire EDM Finishing" activity. This executable job unit contains a complete set of instructions for wire EDM machining; the resource requirements specify a precision wire EDM machine model WCM-200 and brass electrode wire material; the execution conditions are particularly suitable for machining thin-walled parts, requiring a surface roughness Ra < 0.4μm in production scenarios. By binding these three executable job units to the "surface finishing" process, the production system can intelligently select the most suitable executable job unit to perform the task based on specific conditions such as current equipment availability, machining accuracy requirements, and workpiece characteristics. When the three-axis EDM machine is under maintenance, it can automatically select an executable job unit for five-axis EDM or wire EDM; when encountering thin-walled parts, the executable job unit for wire EDM is prioritized to ensure machining quality. This multi-executable job unit binding mechanism effectively improves the adaptability and resource utilization of the production system.

[0041] S12. Configure corresponding electrical discharge machining equipment for each work unit. The specific implementation process is as follows: S120. Retrieve information on all available electrical discharge machining (EDM) equipment from the equipment management system, including equipment number, equipment type, technical parameters, current status, and physical location. This EDM equipment information is stored in an equipment resource database, which is organized using a relational database table structure and includes a basic equipment attribute table, an equipment capability table, and an equipment status table. The equipment resource database provides data support for subsequent configuration operations.

[0042] S121. Based on the process requirements and technical specifications of the work unit, select suitable electrical discharge machining (EDM) equipment types from the equipment resource library. For example, for the work unit... According to its machining accuracy requirements Processing range and equipment capability matrix ( This represents the degree of matching between the capabilities of the electrical discharge machining (EDM) equipment and the requirements of the work unit, and a matching score is calculated. The formula for calculating the match score is: in, This represents the accuracy capability of the k-th electrical discharge machining (EDM) equipment. This represents the processing range of the k-th electrical discharge machining (EDM) equipment. This represents the other capability parameters of the k-th electrical discharge machining (EDM) equipment. , , These represent the weighting coefficients for each capability. , , This represents the similarity calculation function.

[0043] S122. The user selects one or more specific electrical discharge machining (EDM) devices from the matched list of EDM devices for each job unit through a graphical interface. The configuration relationship between the job unit and the EDM device is recorded, forming a configuration mapping table. For example, job unit... It can be configured into electrical discharge machining equipment. This configuration relationship is represented as a binary tuple. It supports configuring multiple alternative EDM devices for a single work unit to provide flexibility in production scheduling.

[0044] S123. For each established configuration relationship, the correspondence between the work unit parameters and the EDM equipment parameters needs to be defined in detail. A parameter mapping interface should be provided, where users need to set specific information such as machining program code, process parameters, and equipment setting parameters. For example, work unit... Processing parameters Need to be mapped to EDM equipment Equipment parameters This mapping relationship is achieved through the parameter mapping function. accomplish: in, Indicates work unit The set of processing parameters, Electrical discharge machining equipment The set of device parameters, This represents a parameter conversion function.

[0045] S124. Verify the rationality of the configuration of each work unit and electrical discharge machining (EDM) equipment through simulation execution, check whether the equipment capacity meets the operational requirements, whether the equipment load is balanced, and whether there are any resource configuration conflicts. The verification process includes three parts: equipment capacity verification, resource conflict detection, and process feasibility analysis. A verification report is generated, listing all discovered problems and suggested solutions. Users adjust the configuration relationships based on the report.

[0046] S125. Store the configuration relationship between the work unit and the electrical discharge machining (EDM) equipment in the process route database, and establish a complete configuration relationship table. The configuration relationship table includes fields such as work unit number, EDM equipment number, parameter mapping relationship, and configuration priority. Provide configuration version management functionality, supporting configuration relationship updates, rollbacks, and historical record queries to ensure the security and traceability of configuration data.

[0047] In the field of precision parts manufacturing, electrical discharge machining (EDM) equipment can specifically include EDM forming machines, wire EDM machines, and EDM drilling machines, each with specific processing capabilities and technical parameters. EDM equipment can connect to an upper-level management system via industrial communication protocols to achieve real-time monitoring of equipment status and automatic execution of processing tasks.

[0048] S13. Define the auxiliary tasks that need to be performed before and after the task execution item of each work unit. The auxiliary tasks include at least one of workpiece clamping, positioning, cleaning or inspection. The specific implementation process is as follows: S130. All possible auxiliary tasks are categorized and managed through an auxiliary task library. The library is divided into four main categories based on task nature: clamping, positioning, cleaning, and detection. Each auxiliary task type includes a standardized task template, containing basic information such as task name, task description, executing equipment, estimated time, and safety requirements. The system administrator can maintain and update the auxiliary task library to ensure the completeness and accuracy of auxiliary task types.

[0049] S131. Through a graphical interface, the user selects and configures auxiliary tasks that need to be completed before the task execution for each work unit's task execution items. For example, for the task execution items of a work unit... ( This represents the k-th task execution item in the j-th work unit of the i-th process, and supports configuring a set of pre-process auxiliary tasks. ,in, Represents the task execution item of the work unit. The p-th pre-processing auxiliary task, where p represents the number of pre-processing auxiliary tasks. During configuration, the user needs to set the execution order number for each pre-processing auxiliary task. and execution conditions .

[0050] S132. The user selects and configures auxiliary tasks to be performed after the task execution is completed for each job unit's task execution item. For example, for the task execution item of the job unit... It supports configuring a set of post-processing auxiliary tasks. ,in, Represents the task execution item of the work unit. The q-th post-ancillary task, where q represents the number of post-ancillary tasks. Users need to set the execution order number for each post-ancillary task. and execution conditions .

[0051] S133. For each configured auxiliary task, whether it is a pre-processor or post-processor type, the resource equipment required to execute the auxiliary task needs to be specified. A suitable execution device is assigned to each auxiliary task using a resource equipment matching algorithm. For example, for a workpiece clamping auxiliary task, the appropriate execution device will be assigned based on the workpiece size. Clamping accuracy requirements and equipment capability values Calculate the matching score : in, and These are the weighting coefficients for size matching and precision matching, respectively. The function calculates the size matching degree. Function calculation accuracy matching degree.

[0052] S134. For each auxiliary task, its execution parameters and quality inspection standards need to be defined in detail. A parameter configuration interface should be provided, allowing users to set specific parameters for the auxiliary task, such as clamping force. Positioning accuracy Cleaning standards And so on. At the same time, quality inspection standards are set for each auxiliary task. This ensures that the execution of auxiliary tasks meets production requirements.

[0053] S135. The configuration verification algorithm checks whether the configuration of auxiliary tasks before and after the task execution item in each work unit is complete and reasonable. Verification content includes the logical order of auxiliary task sequences, resource and equipment capability matching, the rationality of parameter settings, and the applicability of quality inspection standards. A verification report is generated, identifying problems in the configuration, such as missing necessary auxiliary tasks, resource conflicts, or unreasonable parameter settings. Users make corresponding adjustments based on the report.

[0054] S136. Store the complete auxiliary task configuration information in the process route database and establish an auxiliary task configuration table. The auxiliary task configuration table includes fields such as task unit number, task execution item number, auxiliary task type, execution order, execution conditions, resource and equipment allocation, execution parameters, and quality standards. Version management functionality is provided to support updating, querying, and backtracking of configuration data, ensuring the consistency and traceability of auxiliary task configuration data.

[0055] In this context, a workpiece refers to a semi-finished product or component that is processed and manufactured during the production process; it is the direct object of the processing activities. A workpiece originates from raw materials that have undergone preliminary processing and are transformed into a final product through a series of processing steps. The workpiece occupies an intermediate level in the product structure; unlike the final product, which possesses complete function and an independent form, the workpiece requires subsequent processing, assembly, and testing to become a qualified product. In the electrical discharge machining (EDM) process for precision parts, a workpiece typically refers to a metal or non-metal material part undergoing EDM, wire EDM, or other electrical discharge machining treatments.

[0056] In this context, a task execution item within a work unit refers to a specific processing or operational activity defined within that work unit. It represents an indivisible basic operational step in the production process. Each task execution item within a work unit contains a complete set of operational instructions, process parameter requirements, and quality inspection standards, serving as the smallest unit for production task scheduling and execution. Task execution items within a work unit are closely associated with specific electrical discharge machining (EDM) equipment and require clearly defined start conditions, execution processes, and end states to ensure the standardization and normalization of production activities. For example, in a precision parts EDM production environment, suppose there exists a "cavity EDM" work unit. This work unit contains a specific task execution item, numbered T-EDM-CAVITY-01. This task execution item has a complete set of operational instructions, including a series of sequentially executed action commands: first, electrode installation and correction; then, workpiece coordinate system setting; next, discharge parameter adjustment; and finally, cavity machining trajectory control. Each action command includes detailed operating steps and equipment control instructions. Regarding process parameter requirements, this task execution item clearly specifies the following processing parameters: discharge current set to 8 amps, pulse width to 50 microseconds, pause time to 20 microseconds, processing depth to 15 mm, and surface roughness requirement Ra ≤ 1.6 micrometers. These parameters constitute the core technical requirements of the task execution item. The quality inspection standard section defines the acceptance requirements for the processing results: cavity size tolerance controlled within ±0.02 mm, sidewall perpendicularity error not exceeding 0.01 mm, and bottom surface flatness error less than 0.005 mm. These standards serve as the basis for judging the completion quality of the task execution item. This task execution item is closely linked to a three-axis EDM-450 EDM machine. The equipment's starting conditions include the working fluid level reaching the standard position, the spindle pressure set within the normal range, and the safety protection device being in the closed state. The execution process strictly follows the preset processing program, and the ending state is confirmed by both equipment feedback signals and online detection data. With such detailed task execution items, the production system can accurately schedule and execute processing operations, ensuring that each production link meets the predetermined technical specifications and quality requirements, and achieving precise control and standardized management of the production process.

[0057] S2. Based on the association model, multiple electrical discharge machining (EDM) devices are identified, and the status data of each EDM device is collected in real time. The specific implementation process is as follows: S20. Access the established association model and extract all EDM equipment information related to the current production task. The association model stores the complete mapping relationship between product process routes, processes, work units, and EDM equipment. Based on the product process route identifier... Iterate through all associated electrical discharge machining (EDM) devices in the association model to form an EDM device set. ,in, This represents the i-th electrical discharge machining (EDM) device, and n represents the total number of associated EDM devices. Simultaneously, basic attribute information for each EDM device is obtained, including device number, device type, communication address, and capability parameters.

[0058] S21. Initialize the status data acquisition interface for each EDM device in the EDM equipment set. Establish the corresponding data acquisition channel according to the type of EDM device and communication protocol requirements. For EDM devices that support the OPC protocol, create an OPC client instance. Configure server address Collection cycle and list of data items Establish an independent data acquisition session for each electrical discharge machining (EDM) device to ensure the isolation and stability of data acquisition.

[0059] S22. Establish a real-time data acquisition connection with each electrical discharge machining (EDM) device through the initialized data acquisition interface. For OPC protocol devices, the connection process includes three sub-processes: establishing a communication link, subscribing to data items, and starting data listening. Maintain a connection state matrix. ( This represents the set of connection statuses for all electrical discharge machining (EDM) devices, and monitors the connection status of each device in real time. When a connection error occurs, an automatic reconnection mechanism is triggered to ensure continuous data acquisition. Once a connection is established, it begins receiving real-time status data pushed by the EDM devices.

[0060] S23. Continuously receive status data from each electrical discharge machining (EDM) device through the established data acquisition connection. The acquired EDM device status data includes operating status. ( (Indicates the equipment's operating status, such as running, idle, faulty, etc.) and processing progress. ( (Indicates the percentage of the current processing task completed) and fault codes. ( (Code indicating equipment malfunction), spindle load Working fluid temperature Key parameters, etc. The collected raw data undergoes preprocessing, including data validation, format conversion, and outlier filtering, to ensure the accuracy and consistency of the status data.

[0061] S24. Monitor the communication status during the data acquisition process in real time. When a communication interruption or data anomaly is detected in the electrical discharge machining equipment, automatically execute the anomaly handling procedure. Record communication anomaly events. This includes the abnormal device number, abnormality type, occurrence time, and abnormality description. A data completion algorithm is employed to make reasonable estimations based on historical data patterns when data is missing, ensuring the integrity of the status data. Simultaneously, an abnormality alert is sent to operators, prompting manual intervention.

[0062] S25. Update the processed electrical discharge machining (EDM) equipment status data to the status database in real time. The status database adopts a time-series database structure to optimize the storage and query performance of time-series data. Establish an independent status record table for each EDM device, recording the timestamps of the status data. Equipment number and state values It provides a status data query interface, allowing other modules to obtain the latest electrical discharge machining equipment status information in real time.

[0063] The status data of electrical discharge machining (EDM) equipment refers to a set of data indicators reflecting the real-time operating status and working parameters of the equipment. This data includes real-time changes in equipment operating status, processing progress, fault alarm information, actual values ​​of process parameters, and equipment utilization rate. The status data of EDM equipment is acquired through equipment sensors, control systems, and data acquisition interfaces, providing accurate equipment status information support for production scheduling decisions and serving as the fundamental data source for achieving intelligent production management.

[0064] S3. Based on the correlation model and the status data of each electrical discharge machining (EDM) device, and combined with the idle time of each EDM device, determine the optimal EDM device for executing the current production task. The specific implementation process is as follows: S30. Calculate the idle time period for each electrical discharge machining (EDM) machine based on its planned production schedule. Then, determine the idle time index for each EDM machine based on its idle time period. The specific implementation process is as follows: S300: By accessing the scheduling database of the Manufacturing Execution System (MES), extract the production task schedule for each EDM (Electrical Discharge Machining) device within a future time period. Obtain the EDM device... Planned production data ,in, Electrical discharge machining equipment Let m represent the j-th scheduled task and m be the total number of scheduled tasks. Each scheduled task includes a start time. End time and task priority .

[0065] S301. Analyze the planned production timeline of each electrical discharge machining (EDM) device to identify the time intervals between scheduled tasks. These time intervals constitute the idle time periods of the EDM device. For electrical discharge machining (EDM) equipment... Its idle time period set ,in, Electrical discharge machining equipment The l-th idle time period, k represents the number of idle time periods, which is determined as follows: after sorting the scheduled tasks by start time, the time intervals between adjacent tasks, as well as the time range before the first task and after the last task, are identified as potential idle time periods. Each idle time period Has start time End time and duration The calculation formula is: ).

[0066] S302. Perform multi-dimensional attribute analysis on each identified idle time period, including duration, time window, and urgency attributes. The duration attribute directly uses the length of the idle time period. Time window properties ( Indicates idle time period The type of time window (e.g., normal working hours, overtime hours, or holiday periods) is determined based on the company's work calendar; urgency attribute. ( Indicates idle time period The urgency level is calculated based on the distance between the start time of the idle period and the current time, using a time difference function.

[0067] S303. Based on the attribute characteristics of idle time periods, establish an idle time index calculation model. For electrical discharge machining equipment... idle time index The formula for calculating using a weighted comprehensive evaluation method is as follows: in, This represents the maximum duration of idle time for all electrical discharge machining equipment, used for standardization. , , These are the weighting coefficients for time length, time window, and urgency, respectively, satisfying... ; Electrical discharge machining equipment The number of idle time slots. The weighting coefficient is dynamically adjusted according to the production strategy; for example, it is increased when emphasizing equipment utilization. Value, increase when focusing on emergency task response. value.

[0068] S304. Based on the established calculation model, calculate the original idle time index for each electrical discharge machining (EDM) device, and then perform standardization to ensure that the idle time index of all EDM devices falls within a uniform range of 0 to 1. The standardization process uses the min-max normalization method, and the formula is: in, Electrical discharge machining equipment Standardized idle time index This represents the minimum initial idle time index among all electrical discharge machining (EDM) equipment. This represents the maximum raw idle time index among all electrical discharge machining (EDM) equipment. The standardized idle time index facilitates cross-equipment comparisons and scheduling decisions.

[0069] S305. Verify the accuracy and rationality of the idle time index calculation through simulation testing and historical data backtesting. The verification process includes checking the consistency between the index value and actual equipment availability, evaluating the index's support for production scheduling decisions, and analyzing the stability of the index calculation. Adjust the parameter settings in the calculation model based on the verification results, such as optimizing the weighting coefficients. , , The value of can be adjusted, or the idle time period identification algorithm can be improved to ensure that the idle time index can accurately reflect the actual availability of the electrical discharge machining equipment.

[0070] The idle time index is a comprehensive evaluation indicator used to quantify the availability of electrical discharge machining (EDM) equipment within a specific future time period. This index uses mathematical methods to comprehensively consider multiple factors, including the length of idle time, time window distribution characteristics, and task urgency, to form a standardized numerical evaluation. The calculation of the idle time index is based on the planned production schedule and real-time production status of the EDM equipment, accurately reflecting its potential production capacity and scheduling flexibility, and providing crucial decision-making basis for intelligent allocation of production tasks.

[0071] S31. Based on the correlation model, analyze the positional correlation between production tasks and electrical discharge machining equipment. The specific implementation process is as follows: S310. Obtain the layout data of the electrical discharge machining (EDM) equipment and the material transport path information. By accessing the factory layout database, extract the spatial coordinates of all EDM equipment and the topology of the material transport system. plane coordinates ,in, Electrical discharge machining equipment The horizontal coordinate in the factory coordinate system Electrical discharge machining equipment The vertical coordinate in the factory coordinate system. Simultaneously, obtain the material transport path network. ,in, Represents a set of path nodes. It represents the set of path edges, including complete topological information of automated guided vehicle paths, conveyor belt lines, and manual transport channels.

[0072] S311. Calculate the physical distance between the production task and the electrical discharge machining (EDM) equipment. Based on the process sequence corresponding to the production task in the association model, determine the material flow that needs to be transferred between different EDM equipment during the task execution. For the production task... and electrical discharge machining equipment physical distance Calculated using the Euclidean distance formula: in, Indicates production task The coordinates of the current location Electrical discharge machining equipment The coordinates. When a production task involves multiple processes, the distance from the equipment in the previous process to the candidate equipment is calculated as the basis for evaluation.

[0073] S312. Evaluate the logistics path complexity between production tasks and electrical discharge machining (EDM) equipment. Based on the material transport path network, analyze the logistics path complexity from the current location of the production task to the candidate EDM equipment. Indicates production task To electrical discharge machining equipment The complexity of the logistics path. Complexity assessment considers the number of path nodes. Number of path turns Path width limit (in (Indicates the effective width of the path) and the degree of automation ( Specifically, this refers to the level of automated transportation equipment configuration along the route. The formula for calculating logistics route complexity is: in, Indicates the maximum number of path nodes. Indicates the maximum number of turns. Indicates the standard path width. , , , These are the weight coefficients of each factor, satisfying... .

[0074] S313. Based on the analysis results of physical distance and logistics path complexity, calculate the production tasks. With electrical discharge machining equipment Positional correlation between The formula for calculating location correlation is: in, This represents the maximum physical distance among all candidate electrical discharge machining (EDM) devices. This represents the minimum physical distance among all candidate electrical discharge machining (EDM) equipment. This represents the maximum logistics path complexity among all candidate electrical discharge machining (EDM) equipment. This represents the minimum logistics path complexity among all candidate electrical discharge machining (EDM) equipment. This is the weighting coefficient for distance factors, with a value ranging from 0 to 1. Location correlation. The value ranges from 0 to 1, with higher values ​​indicating stronger positional correlation.

[0075] S314. Verify the accuracy of the location correlation calculation using actual production data, and compare the calculated location correlation with the actual material transportation efficiency. Record the actual transportation time during the historical task execution process. The predicted correlation between location and position is used to verify the accuracy of the predictions. Based on the verification results, the weighting coefficients are dynamically adjusted. and arrive The value of is optimized to improve the location correlation calculation model and enhance prediction accuracy.

[0076] S315. The calculated location correlation data is pushed to the decision engine as an important reference factor for equipment selection. Location correlation, together with equipment status data and idle time index, constitutes a multi-dimensional evaluation system to support intelligent scheduling of production tasks. Location correlation data is updated in real time. When the factory layout changes or material transportation routes are adjusted, the location correlation of all equipment is recalculated in a timely manner to ensure the accuracy of scheduling decisions.

[0077] S32. Perform multi-dimensional evaluation of the status data, idle time index, and location correlation of each electrical discharge machining (EDM) device to determine the optimal EDM device. The specific implementation process is as follows: S320: Real-time acquisition of status data, idle time index, and location correlation of each electrical discharge machining (EDM) device. Extracting EDM devices from the status database. Status data ( Electrical discharge machining equipment The status data set includes parameters such as operating status, processing progress, and fault alarms. This data is obtained from the idle time calculation module for the electrical discharge machining equipment. idle time index The electrical discharge machining equipment is obtained from the location correlation analysis module. Locational correlation with production tasks Integrate these data into an evaluation dataset. ,in, Electrical discharge machining equipment A complete set of evaluation data provides input for subsequent evaluations.

[0078] S321. Convert assessment data with different dimensions and ranges into a unified standardized score to facilitate comprehensive comparison. For state data... A status score is calculated based on factors such as equipment operating status, fault level, and processing efficiency. ,in, Electrical discharge machining equipment The status data is standardized and scored, with values ​​ranging from 0 to 1. The formula for calculating the status score is: in, Electrical discharge machining equipment The actual state value, This represents the minimum state value among all electrical discharge machining (EDM) equipment. This represents the maximum state value among all electrical discharge machining (EDM) equipment. Idle Time Index Location correlation The values ​​are already standardized and require no additional processing; they can be used directly for evaluation.

[0079] S322. Based on the production strategy and optimization objectives, assign appropriate weight coefficients to the status data, idle time index, and location correlation. These weight coefficients reflect the relative importance of each dimension in the evaluation. Set the weights for the status data. ( Specifically, this represents the weighting of status data in the overall evaluation, and the weighting of the idle time index. ( Specifically, this represents the weighting of the idle time index in the overall evaluation and the weighting of location relevance. ( Specifically, this represents the weight of location correlation in the overall evaluation. The weighting coefficients satisfy the normalization condition. The weight values ​​are dynamically adjusted through expert systems or historical data learning, for example, increasing them when emphasizing equipment reliability. Improving delivery efficiency while focusing on delivery efficiency .

[0080] S323. Calculate a comprehensive evaluation score for each electrical discharge machining (EDM) device based on standardized scoring and weighting coefficients. The formula for calculating the comprehensive evaluation score is as follows: Overall assessment score The numerical range is between 0 and 1, with higher scores indicating better overall evaluation results for the electrical discharge machining (EDM) equipment. After calculating a comprehensive evaluation score for each EDM piece of equipment, an evaluation result set is formed. ,in, This indicates the total number of electrical discharge machining (EDM) equipment.

[0081] S324. Rank all electrical discharge machining (EDM) equipment according to their overall evaluation scores, and select the EDM equipment with the highest overall evaluation score as the optimal EDM equipment. Conditions met: Equipment allocation instructions are generated based on the selection results. If multiple electrical discharge machining (EDM) devices have the same highest overall evaluation score, the EDM device with the higher status data score is selected to ensure equipment reliability.

[0082] S325. Verify the accuracy and rationality of the evaluation results through simulation execution and historical data backtesting. The verification process includes checking the performance of the optimal electrical discharge machining (EDM) equipment in actual production, analyzing the correlation between the evaluation score and task completion efficiency, and the adaptability of the evaluation weight settings. Record the decision data for each evaluation. ( Specifically, this represents a complete record of the evaluation decision, including input data, weight settings, output results, and execution effects, which is used for subsequent model optimization. Based on the validation results, the weight coefficients are adjusted or the standardization method is improved to enhance the predictive accuracy and practicality of the evaluation model.

[0083] Through the above steps, a scientific, multi-dimensional evaluation of the status data, idle time index, and location correlation of each electrical discharge machining (EDM) device can be conducted to accurately determine the optimal EDM device and achieve efficient allocation of production resources. The entire process is based on data-driven decision-making and optimization algorithms, ensuring the objectivity of the evaluation process and the reliability of the results.

[0084] S4. Based on the correlation model, determine whether the current production task requires tools. If so, perform intelligent tool selection and automatic control of tool loading and unloading to execute the production task; otherwise, execute the production task directly. This includes intelligent tool selection and automatic control of tool feeding and delivery to execute production tasks, including: S40. Obtain the process requirements of the current production task based on the association model, specifically: S400: Identify the current production task and locate the associated model node. Upon receiving the production task instruction, first parse the production task identifier. Query the associated model based on the production task identifier. Locate the corresponding product process route node Verify the matching relationship between production tasks and process routes to ensure that the obtained process requirements accurately correspond to the current production tasks.

[0085] S401. Traverse the association model to obtain the complete process sequence. This starts from the located product process route nodes. Starting from this point, traverse all process nodes defined in the association model to construct a complete process sequence. ,in, Let represent the i-th process step, and n represent the total number of processes. Following the order of processes in the process route, each process node is visited sequentially, recording basic information such as process number, process name, and process type to establish a framework for extracting subsequent process requirements.

[0086] S402. Extract the detailed process parameter requirements for each process step. This applies to each process step in the process sequence. Extract the corresponding set of process parameters from the association model. ,in, Indicate process The j-th process parameter, where m represents the number of process parameters. Process parameters include machining accuracy levels. Surface roughness Dimensional tolerances and material removal rate Key technical indicators, etc.

[0087] S403. Further iterate through the work unit configuration corresponding to each process, and extract specific process requirement information from the work unit nodes. For each process... Corresponding work unit ( Indicate process (k-th work unit), extract the specific process requirements of the work unit. ,in, Indicates work unit The Special process requirements. These include technical specifications specific to electrical discharge machining, such as electrode type, discharge parameters, and cooling methods.

[0088] S404. Extract the auxiliary tasks that need to be executed before and after the task execution item of each job unit from the association model, and analyze the process constraints brought about by these auxiliary tasks. For auxiliary tasks... Obtain its process constraint set ,in, Indicates auxiliary task The qth process constraint. These constraints include limiting factors affecting process execution, such as clamping force range, positioning accuracy requirements, and cleanliness standards.

[0089] S405. Perform integrity checks and consistency verification on all extracted process requirements. Integrity checks ensure no critical process parameters are missing, and consistency verification ensures no conflicts exist between process requirements from different sources. Use a rule engine to execute the verification logic and verify the rule set. ,in, This represents the k-th validation rule. When a missing or conflicting rule is found, an exception report is generated, prompting the user to confirm or correct it.

[0090] S406. Organize the verified process requirement data into a structured format to generate a complete process requirement document for the current production task. The process requirements document contains three main parts: basic process parameters, special process requirements, and process constraints. It uses a standardized data format to ensure that subsequent processing modules can correctly parse and use it.

[0091] S41. Query all tools in the tool library that meet the process requirements; compare the remaining life of each tool that meets the requirements.

[0092] The specific process for querying all tools in the tool library that meet the process requirements is as follows: S410. Access the association model and extract detailed process requirement data for the current production task. Obtain the set of process requirements from the association model. ,in, This represents the j-th process requirement parameter, and m represents the total number of process requirement parameters. Process requirement parameters include processing type. Material type Accuracy level Surface roughness Key parameters, etc. These process requirement parameters are standardized to form a unified query condition format.

[0093] S411. Convert the process requirement parameters into corresponding tool attribute matching conditions. For each process requirement parameter... Generate corresponding matching conditions For example, processing type Applicable machining type attribute of corresponding cutting tools (in (Indicates the type of machining the tool is suitable for) and the material type. Applicable material range properties of corresponding cutting tools ( (Indicates the range of materials the cutting tool is suitable for), precision grade. The accuracy capability attribute of the corresponding cutting tool ( This indicates the machining accuracy that the tool can achieve. Establish a mapping table between process requirements and tool attributes to ensure the accuracy of the matching conditions.

[0094] S412. Based on the constructed matching conditions, send a structured query statement to the tool library database. The query statement uses parameterized SQL format to ensure the security and efficiency of the query. The basic structure of the query statement is as follows: in, This represents a tool information data table. This represents the j-th tool attribute field. This indicates matching operators (such as =, >, <, IN, etc.). This indicates the parameter values ​​required for the process. For example, for accuracy requirements, the query condition is... For material matching, the query conditions are: After executing the query, a preliminary set of matching tools is obtained. ,in, Let i represent the i-th tool that is initially matched, and k represent the number of tools initially matched.

[0095] S413. Based on the preliminary matching results, apply tool condition filtering conditions to exclude unusable tools. Filtering conditions include tool life status. Tool availability status and tool position status These status information are obtained in real time from the tool status database, and the filtering conditions are expressed as follows: in, The minimum threshold representing the remaining tool life. This represents the set of tools after state filtering.

[0096] S414. Calculate the matching score for each filtered tool to quantify the degree of conformity between the tool and the process requirements. For tools... Its matching score Calculated using a weighted evaluation model: in, This represents the weighting coefficient of the j-th process requirement parameter. Indicates cutting tool The j-th attribute value, This represents the function for calculating the matching degree of the j-th parameter. Weight coefficients. The weighting is dynamically allocated based on the importance of process requirements, with important parameters such as accuracy requirements assigned higher weights and minor parameters assigned lower weights.

[0097] S415. Sort the tools according to the matching score to generate the final list of tools that meet the process requirements. ,in, This indicates the final number of tools that meet the requirements, sorted in descending order of matching score. Complete attribute information and matching details are recorded for each tool, including the tool number. Tool type Specifications and remaining lifespan This tool list will serve as the direct basis for subsequent tool selection decisions.

[0098] S42. Select the tool with the longest remaining life as the optimal tool. S43. Automatic control selects the optimal tool and executes the production task. The specific implementation process is as follows: S430. Obtain the optimal tool identifier from the intelligent tool selection process. This tool has the longest remaining life. Verify the current state of the optimal tool, including its availability. ( Indicates the tool's availability status (e.g., available, occupied, or under maintenance) and location information. ( This indicates the specific storage location of the tool in the tool magazine. After successful verification, a tool fetch / feed control instruction set is generated. ( This represents a complete set of instructions that includes tool retrieval, tool delivery, and installation operations. The instructions include the tool number, tool retrieval coordinates, target device address, and operation priority parameters.

[0099] S431. Control commands are transmitted to the robot system via an industrial communication network. The communication protocol uses TCP / IP or real-time Ethernet to ensure reliable command transmission. The command data structure includes the target tool number. (Right now (Indicates the optimal tool number) and the coordinates of the tool pick-up position. ( , , (Indicates the three-dimensional coordinates of the tool in the tool magazine) and the identifier of the tool delivery target device. and operation serial number Also set the command timeout period. ( The parameters indicate the maximum allowed time for instruction execution and the retry mechanism.

[0100] S432. The robot system controls a six-axis industrial robot to move to a designated tool-fetching position in the tool magazine. The robot path planning algorithm is based on the coordinates of the tool-fetching position. and current robot position (in , , (Representing the robot's current 3D coordinates), calculate the optimal motion trajectory. The motion trajectory function is expressed as: in, This represents the robot's motion path vector at time t. This represents the robot's initial position vector. This represents the target position vector for tool retrieval. This represents the time normalization function. After the robot reaches the tool-grabbing position, it performs the gripping operation, and the force sensor provides feedback to ensure the gripping force of the tool. Within safe limits.

[0101] S433, The robot carries the cutting tool to the target electrical discharge machining equipment. The tool exchange position is determined. The tool installation readiness status of the electrical discharge machining equipment is obtained through the device communication interface. The robot performs the tool installation operation, precisely inserting the tool into the spindle holder of the electrical discharge machining (EDM) equipment. During the installation process, a vision system monitors the tool installation accuracy. Ensure that the equipment requirements are met. After installation, the robot system sends a confirmation signal. .

[0102] S434. After confirming successful tool installation, the electrical discharge machining (EDM) equipment loads the machining program for the current production task. Equipment initialization processing parameters, including discharge current. Pulse width and processing depth The equipment starts the processing program and monitors the processing status data in real time. .

[0103] S434. Real-time acquisition of electrical discharge machining status data via OPC protocol. Including task completion percentage and device error codes Set the status monitoring cycle. When an anomaly is detected, the anomaly handling process is automatically triggered, such as pausing the task, changing the tool, or adjusting parameters. Simultaneously, a task execution log is recorded. This is used for subsequent analysis and optimization.

[0104] S435. When the production task is completed, the electrical discharge machining equipment sends a task completion signal. The robot system retrieves the tool from the electrical discharge machining (EDM) equipment and returns it to its original position in the tool magazine or a designated maintenance location. It also updates the remaining lifespan data of the tool in the tool magazine. The calculation formula is: in, Indicates the remaining lifetime after the update. Indicates the remaining lifespan before use. This indicates the tool life consumption during this task. The updated tool data is then stored in the tool library database, completing the entire automated tool loading and unloading process and the execution of production tasks.

[0105] S5. Determine whether production needs to continue. If so, return to the step of collecting the status data of each electrical discharge machining device in real time and continue production.

[0106] In the above embodiments, although the steps are numbered S1, S2, etc., they are only specific embodiments given by the present invention. Those skilled in the art can adjust the execution order of S1, S2, etc. according to the actual situation. The scheme after adjusting the order is also within the protection scope of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0107] like Figure 2 As shown, an embodiment of the present invention provides a production control system 200 suitable for flexible electrical discharge machining of precision parts, which includes an association model establishment module 201, a status data acquisition module 202, an optimal electrical discharge machining equipment determination module 203, and a generation module 204. The association model building module 201 is used to: after receiving the production task of the product, establish an association model between the product's process route, operation, work unit and task execution item; The status data acquisition module 202 is used to: determine multiple electrical discharge machining (EDM) devices based on an association model, and acquire the status data of each EDM device in real time; The optimal electrical discharge machining (EDM) equipment determination module 203 is used to: determine the optimal EDM equipment for performing the current production task based on the correlation model and the status data of each EDM equipment, and in combination with the idle time of each EDM equipment; The generation module 204 is used to: determine whether the current production task requires tools based on the association model; if so, intelligently select tools and automatically control tool loading and unloading to execute the production task; if not, directly execute the production task. The generation module 204 is also used to: determine whether production needs to continue; if so, return to the step of collecting the status data of each electrical discharge machining device in real time and continue production.

[0108] Optionally, in the above technical solution, the association model establishment module 201 is specifically used for: Define the product's technological route and process sequence; Each process is bound to multiple executable work units; Configure appropriate electrical discharge machining equipment for each work unit; Define the auxiliary tasks that need to be performed before and after the task execution item of each work unit. The auxiliary tasks include at least one of workpiece clamping, positioning, cleaning or inspection.

[0109] Optionally, in the above technical solution, the optimal electrical discharge machining (EDM) equipment determination module 203 is specifically used for: Calculate the idle time period for each electrical discharge machining (EDM) machine based on its planned production schedule, and determine the idle time index for each EDM machine based on its idle time period. Based on the correlation model, the location correlation between production tasks and electrical discharge machining equipment is analyzed; The status data, idle time index, and location correlation of each electrical discharge machining (EDM) device are evaluated from multiple dimensions to determine the optimal EDM device.

[0110] Optionally, in the above technical solution, the generation module 204 is further specifically used for: The process requirements of the current production task are obtained based on the association model; Queries all tools in the tool library that meet the process requirements; compares the remaining life of each tool that meets the requirements; Select the tool with the longest remaining life as the optimal tool; Perform production tasks using the optimal cutting tools.

[0111] In another embodiment, the system includes an electrical discharge machining (EDM) device, a process route configuration module, a task scheduling module, a tool management module, a data service module, a robot system, and a tool magazine. The EDM device is an electrical discharge machining (EDM) device used for machining precision parts. The process route configuration module uses a graphical interface and database technology to establish a model linking product process routes and production resources. The task scheduling module communicates with the EDM device in real time via the OPC protocol, collects equipment status data, and executes intelligent scheduling. The tool management module implements intelligent tool management and a tool selection mechanism. The data service module provides data storage and exchange services for each module, employing a microservice architecture. The robot system is a six-axis industrial robot used for automatically picking up and feeding tools. The tool magazine is an automated tool magazine that stores various types of tools and records tool life data. The process route configuration module is connected to the data service module, the task scheduling module is connected to the data service module and the EDM device, and the tool management module is connected to the data service module, the robot system, and the tool magazine, forming a complete control system that achieves automated scheduling and intelligent management of the production process. The specific execution process is as follows: ① Once the production task for the product is received, the production task begins.

[0112] ② The process route configuration module establishes a relationship model between product process routes, processes, work units, and task execution items, specifically: I. Define the product process route and sequence of operations; II. Bind multiple executable work units to each process; III. Configure appropriate electrical discharge machining equipment for each work unit; IV. Define the auxiliary tasks that need to be performed before and after the task execution item of each work unit. The auxiliary tasks include at least one of workpiece clamping, positioning, cleaning or inspection.

[0113] ③ The task scheduling module identifies multiple electrical discharge machining (EDM) devices based on an association model and collects the status data of each EDM device in real time via the OPC protocol.

[0114] ④ The task scheduling module performs multi-dimensional evaluation based on the status data, idle time index, and location correlation of each electrical discharge machining (EDM) device to determine the optimal EDM device for executing the current production task. The intelligent scheduling decision algorithm is as follows: I. Obtain real-time status data of all available electrical discharge machining equipment; II. Calculate the idle time period of each electrical discharge machining (EDM) machine based on the planned production schedule of each EDM machine, and determine the idle time index of each EDM machine based on the idle time period of each EDM machine. III. Based on the correlation model, analyze the positional correlation between production tasks and electrical discharge machining equipment. The positional correlation is calculated by combining the physical distance between electrical discharge machining equipment and the complexity of material transportation paths. IV. Evaluate the status data, idle time index and location correlation of each electrical discharge machining (EDM) device from multiple dimensions to determine the optimal EDM device; V. Select the optimal electrical discharge machining (EDM) equipment for task allocation.

[0115] ⑤ Determine whether the current production task requires tools based on the association model. If yes, skip to step ⑦; otherwise, proceed to the next step.

[0116] ⑥ The tool management module intelligently selects the optimal tool based on the machining process requirements and schedules the robot system for automatic tool loading and unloading. Specifically, the intelligent tool selection involves: I. Obtain the process requirements of the current production task based on the association model; II. Query all tools in the tool library that meet the process requirements; compare the remaining life data of each tool that meets the requirements; III. Select the tool with the longest remaining life as the optimal tool.

[0117] ⑦ Perform processing tasks.

[0118] ⑧ Determine whether production needs to continue. If production needs to continue, return to step ③; otherwise, end the process.

[0119] This embodiment realizes intelligent and flexible management of the precision parts EDM production line, effectively improving production efficiency and quality stability, and reducing labor costs and tool wear rate.

[0120] It should be noted that the beneficial effects of the production control system for flexible electrical discharge machining of precision parts provided in the above embodiments are the same as those of the production control method for flexible electrical discharge machining of precision parts described above, and will not be repeated here. Furthermore, the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process is detailed in the method embodiments, and will not be repeated here.

[0121] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned production control methods applicable to flexible electrical discharge machining of precision parts.

[0122] An embodiment of the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements any of the above-described production control methods applicable to flexible electrical discharge machining of precision parts.

[0123] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A production control method suitable for flexible electrical discharge machining of precision parts, characterized in that, include: Once a production task for a product is received, a model is established to link the product's process route, procedures, work units, and task execution items. Based on the aforementioned correlation model, multiple electrical discharge machining (EDM) devices are identified, and the status data of each EDM device is collected in real time. Based on the association model and the status data of each of the electrical discharge machining (EDM) devices, and combined with the idle time of each of the EDM devices, the optimal EDM device for performing the current production task is determined. Based on the aforementioned association model, it is determined whether the current production task requires a cutting tool. If so, intelligent tool selection is performed and tool loading and unloading are automatically controlled to execute the production task; otherwise, the production task is executed directly. Determine whether production needs to continue. If so, return to the step of collecting real-time status data for each of the electrical discharge machining devices and continue production.

2. The production control method for flexible electrical discharge machining of precision parts according to claim 1, characterized in that, Establish a relationship model between the product's process route, operations, work units, and task execution items, including: Define the product's technological route and process sequence; Each process is bound to multiple executable work units; Configure appropriate electrical discharge machining equipment for each work unit; Define the auxiliary tasks that need to be performed before and after the task execution item of each work unit. The auxiliary tasks include at least one of workpiece clamping, positioning, cleaning or inspection.

3. The production control method for flexible electrical discharge machining of precision parts according to claim 1, characterized in that, Based on the aforementioned correlation model and the status data of each electrical discharge machining (EDM) device, and combined with the idle time of each EDM device, the optimal EDM device for performing the current production task is determined, including: The idle time period of each electrical discharge machining (EDM) equipment is calculated based on the planned production schedule of each EDM equipment, and the idle time index of each EDM equipment is determined based on the idle time period of each EDM equipment. Based on the aforementioned correlation model, the positional correlation between production tasks and the electrical discharge machining equipment is analyzed; The optimal electrical discharge machining (EDM) equipment is determined by multi-dimensional evaluation of the status data, idle time index, and location correlation of each EDM equipment.

4. A production control method for flexible electrical discharge machining of precision parts according to any one of claims 1 to 3, characterized in that, Intelligent tool selection and automatic tool feeding control are implemented to execute production tasks, including: The process requirements of the current production task are obtained based on the aforementioned association model; Query all tools in the tool library that meet the stated process requirements; compare the remaining life of each compliant tool; Select the tool with the longest remaining life as the optimal tool; Perform production tasks using the optimal cutting tools.

5. A production control system suitable for flexible electrical discharge machining of precision parts, characterized in that, It includes a correlation model establishment module, a state data acquisition module, an optimal electrical discharge machining equipment determination module, and a generation module; The association model building module is used to: upon receiving a production task for a product, establish an association model between the product's process route, procedures, work units, and task execution items; The status data acquisition module is used to: determine multiple electrical discharge machining (EDM) devices based on the association model, and acquire the status data of each EDM device in real time; The optimal electrical discharge machining (EDM) equipment determination module is used to: determine the optimal EDM equipment for performing the current production task based on the association model and the status data of each EDM equipment, and in combination with the idle time of each EDM equipment; The generation module is used to: determine whether the current production task requires tools based on the association model; if so, intelligently select tools and automatically control tool loading and unloading to execute the production task; if not, directly execute the production task. The generation module is also used to: determine whether production needs to continue; if so, return to the step of collecting the status data of each electrical discharge machining device in real time and continue production.

6. A production control system for flexible electrical discharge machining of precision parts according to claim 5, characterized in that, The association model building module is specifically used for: Define the product's technological route and process sequence; Each process is bound to multiple executable work units; Configure appropriate electrical discharge machining equipment for each work unit; Define the auxiliary tasks that need to be performed before and after the task execution item of each work unit. The auxiliary tasks include at least one of workpiece clamping, positioning, cleaning or inspection.

7. A production control system for flexible electrical discharge machining of precision parts according to claim 5, characterized in that, The optimal electrical discharge machining equipment determination module is specifically used for: The idle time period of each electrical discharge machining (EDM) equipment is calculated based on the planned production schedule of each EDM equipment, and the idle time index of each EDM equipment is determined based on the idle time period of each EDM equipment. Based on the aforementioned correlation model, the positional correlation between production tasks and the electrical discharge machining equipment is analyzed; The optimal electrical discharge machining (EDM) equipment is determined by multi-dimensional evaluation of the status data, idle time index, and location correlation of each EDM equipment.

8. A production control system for flexible electrical discharge machining of precision parts according to any one of claims 5 to 7, characterized in that, The generation module is also specifically used for: The process requirements of the current production task are obtained based on the aforementioned association model; Query all tools in the tool library that meet the stated process requirements; compare the remaining life of each compliant tool; Select the tool with the longest remaining life as the optimal tool; Perform production tasks using the optimal cutting tools.

9. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the production control method for flexible electrical discharge machining of precision parts as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements a production control method for flexible electrical discharge machining of precision parts as described in any one of claims 1 to 4.

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