A method and system for coordinated machining control of multiple electric discharge machines

Abstract

The application discloses a kind of electric spark machine multi-machine collaborative processing control method and system, it is related to electric spark machine technical field, mapping table is established, alignment state is generated, medium pollution threshold is set and monitored, candidate scheduling is generated and is simulated with discharge parameter template, update gap is combined with monitoring, write in electrode wear compensation, hand over before verification, retest generates receipt, the present application is through unified work order analysis and operation chart modeling, realize the centralized management of electrode, machine tool capacity and quality threshold, ensure the machining accuracy consistency between different machine tools, improve overall efficiency, maintain the stability of continuous processing, through hand over verification and quality closed loop, ensure each process is traceable, and through abnormal classification and experience database update realizes continuous optimization, overall reduce rework, improve the stability, precision and production rhythm of multi-machine collaborative processing.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric spark machines, in particular to a multi-machine collaborative processing control method and system for electric spark machines. BACKGROUND

[0002] Existing electric spark machine processing is mainly independent operation of a single machine, and there is a lack of unified task scheduling and parameter inheritance mechanism between different machine tools, resulting in difficulty in electrode reuse, inconsistent medium state, and large fluctuation in processing precision. During cross-machine handover, rework is often caused by alignment errors, parameter incompatibility and electrode wear, resulting in low processing efficiency and poor quality consistency. Especially in the scene of complex mold and multiple equipment parallel operation, the existing technology is difficult to meet the demand of high precision and high efficiency at the same time.

[0003] At present, the Chinese invention patent with publication number CN108213744B discloses a laser micro-hole machining collaborative control system and method based on an industrial computer, which comprises the following steps: an industrial computer obtains processing parameters from a process database according to user operation, sends control commands to a numerical control system according to the processing parameters, and sends configuration parameters to a collaborative control unit; the switch of the laser is controlled and the state information fed back by the laser is received; the numerical control system controls the movement of the machine tool axis according to the control command, and controls the switch of the collaborative control unit; the collaborative control unit receives the configuration parameters, the switch quantity sent by the numerical control system and the position information fed back by the adjustment motor of the machining head, sends a control signal to the laser, and realizes circular constant overlap rate spiral scanning machining control.

[0004] The above-mentioned technology is difficult to realize the unified management and cross-machine inheritance of processing parameters, workpiece reference and electrode wear compensation in multi-machine collaborative processing of electric spark machines, resulting in insufficient precision consistency and unstable handover process. SUMMARY

[0005] The technical problem solved by the present application is that the existing technology is difficult to realize the unified management and cross-machine inheritance of processing parameters, workpiece reference and electrode wear compensation in multi-machine collaborative processing of electric spark machines, resulting in insufficient precision consistency and unstable handover process.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] A multi-machine collaborative processing control method for electric spark machines, comprising the following steps:

[0008] Step S1, analyze the work order and CAD data, generate a work graph, and establish a mapping table;

[0009] Step S2, establish a three-dimensional reference and cross-machine coordinate, generate an alignment state, set a medium pollution threshold and monitor;

[0010] Step S3, generating candidate schedules and simulating with discharge parameter templates, outputting master schedule and handover list;

[0011] Step S4, generating machine tool parameter package, combining with monitoring and updating gap, writing electrode wear compensation;

[0012] Step S5, pre-handover verification, freezing and loading parameter package, re-measuring to generate receipt, deviation triggering rollback and archiving;

[0013] Step S6, classifying abnormalities and forming event fingerprints, warehousing and updating discharge parameter templates and wear rules.

[0014] Preferably, the step S1 comprises the following sub-steps:

[0015] Step S101, parsing work order data and CAD data, extracting process features, the process features including process sequence, workpiece reference and geometric features, forming operation diagram, and marking electrode type and machining path in the operation diagram;

[0016] Step S102, establishing a mapping table between electrodes and processes, binding electrode number, material, pre-grinding size, and allowed repetition number with corresponding processes;

[0017] Step S103, generating machine tool capability vector and machine tool state vector, the machine tool capability vector including discharge power supply capacity, motion accuracy, C-axis rotation range and cooling capacity corresponding to the machine tool, and the machine tool state vector including current running state;

[0018] Step S104, establishing a set of quality acceptance thresholds, including size deviation range, geometric tolerance range, surface roughness range, and in-machine detection tolerance range.

[0019] Preferably, the step S2 comprises the following sub-steps:

[0020] Step S201, establishing a three-dimensional reference frame and generating a cross-machine coordinate mapping:

[0021] Establishing a three-dimensional reference system between the workpiece reference and the fixture reference, mapping the working coordinate system of the machine tool to the three-dimensional reference system, and correcting the coordinate error;

[0022] Step S202, collecting offset data of key positioning points and measurement points in-machine, forming an alignment state vector, the alignment state vector including translation bias, rotation attitude and thermal compensation parameters;

[0023] Step S203, setting a medium pollution index threshold and generating an on-machine monitoring plan, the on-machine monitoring plan defining a particle size threshold, a temperature threshold and a viscosity threshold based on the online monitoring results of the medium circulating system, forming a medium pollution index, and establishing a monitoring frequency and a pre-warning rule, the online monitoring results being linked with a quality acceptance threshold set, a process delay signal and a medium replacement signal being triggered when the medium pollution index exceeds the particle size threshold, the temperature threshold and the viscosity threshold.

[0024] Preferably, the step S3 comprises the following sub-steps:

[0025] Step S301, according to the process dependency relationship in the job graph, the mapping table between electrodes and processes and the machine tool capability vector, a scheduling path is listed according to the multi-objective principle of machine time balance, minimum electrode replacement frequency and key surface priority, a candidate cross-machine scheduling sequence set is generated, and the handover node and the corresponding electrode use information are marked in the candidate cross-machine scheduling sequence set;

[0026] Step S302, calling a pre-established discharge parameter template to predict the processing time, discharge stability, slag removal efficiency and thermal accumulation risk of the candidate cross-machine scheduling sequence set, the discharge parameter template being established based on typical workpiece characteristics and historical processing experience, the sequence violating the medium pollution threshold or exceeding the quality acceptance threshold being eliminated, and the comprehensive score of the remaining sequence being calculated;

[0027] Step S303, selecting the sequence with the highest comprehensive score to generate a main scheduling table, and listing the alignment state, medium pollution index, electrode wear compensation data and quality acceptance threshold checking requirements in the handover inspection checklist, and outputting the main scheduling table and the handover inspection checklist.

[0028] Preferably, the step S4 comprises the following sub-steps:

[0029] Step S401, selecting basic parameters from the discharge parameter template set based on process characteristics, and correcting them in combination with the machine tool capability vector and the alignment state vector to form a machine tool parameter package, the machine tool parameter package including voltage, current, pulse width, intermittent time and servo gain, and the machine tool parameter package being assigned a version number;

[0030] Step S402, real-time monitoring of spark statistical data, slag discharge signal and discharge stability during processing, generating gap adjustment parameters including servo response speed adjustment signal, intermittent time signal and polarity control signal when detecting discharge abnormalities or unstable gaps, and writing the gap adjustment parameters into the machine tool parameter package;

[0031] Step S403, specifically, calculating electrode wear estimation value according to cumulative electrode discharge time, energy distribution and in-machine measurement deviation, the electrode wear estimation value including electrode end face wear amount and sidewall wear amount, converting the electrode wear estimation value into trajectory offset, and writing into compensation record.

[0032] Preferably, the step S5 comprises the following sub-steps:

[0033] Step S501, before process handover, calling in-machine measurement data to check whether the offset of alignment state vector is within the allowed range, comparing the medium online monitoring result to confirm that the pollution index does not exceed the threshold, checking whether the version number of the currently loaded machine tool parameter package is consistent with the preset version number, and checking the processing data under the quality acceptance threshold, and outputting the pre-handover verification list;

[0034] Step S502, freezing the machine tool parameter package currently used by the machine tool in a version-locked manner, and transmitting the electrode wear compensation data and historical processing record to the next machine tool, and the next machine tool enters the processing state after completing parameter package loading and environment self-checking;

[0035] Step S503, after loading the machine tool parameter package, the next machine tool performs in-machine measurement and reference comparison, generates a handover receipt file, and if the detection result deviation exceeds the quality threshold, automatically triggers a rollback strategy to restore to the previous parameter package version or delayed execution, and records the rollback event information;

[0036] Step S504, summarizing the pre-handover verification list, the handover receipt file and the rollback event information and generating a quality signature record, and archiving in batch number and process number index mode.

[0037] Preferably, the step S6 comprises:

[0038] Step S601, according to the real-time data collected during in-machine monitoring and scheduling execution process, classifying the abnormality and outputting the classified abnormal type, the classified abnormal type including short circuit rate rising, deslagging efficiency decreasing, unstable discharge, size drift and medium pollution index exceeding limit type, and generating corresponding event fingerprint for each type of abnormality, the event fingerprint containing abnormal occurrence time, involved machine tool capability vector, electrode batch and alignment state vector information;

[0039] Step S602, establishing a mapping relationship between the machine tool parameter package of the classified abnormal type, the gap adjustment parameter, the electrode wear compensation record and the actual detection result, and storing them in the experience library;

[0040] Step S603, on the basis of experience library update, revising the discharge parameter template set and correcting the electrode wear prediction rule, and generating an update log, the content of the update log including modification time, modifier, modification content and version number.

[0041] Preferably, the electrode wear estimation value is calculated by combining the electrode cumulative discharge time length, energy distribution characteristics and in-machine measurement deviation, to obtain the electrode end surface wear amount and side wall wear amount, and the electrode wear compensation data is transmitted together with the machine tool parameter package during cross-machine handover.

[0042] Preferably, the quality signature record and the handover receipt are associated and archived with the batch number, the process number and the machine tool number as the index, and are attached with the in-machine re-measurement results, the rollback event information and the parameter package version number.

[0043] A multi-machine collaborative machining control system of an electric spark machine comprises a data analysis module, an alignment detection module, a parameter simulation module, an update compensation module, a verification receipt module and an abnormality classification module.

[0044] The data analysis module is used for analyzing work orders and CAD data, generating a work graph, and establishing a mapping table.

[0045] The alignment detection module is used for establishing three-dimensional reference and cross-machine coordinates, generating an alignment state, setting a medium pollution threshold and monitoring.

[0046] The parameter simulation module is used for generating a candidate schedule and simulating with a discharge parameter template, outputting a main schedule and a handover list.

[0047] The update compensation module is used for generating a machine tool parameter package, combining a monitoring update gap, and writing an electrode wear compensation.

[0048] The verification receipt module is used for pre-handover verification, freezing and loading a parameter package, re-measurement to generate a receipt, deviation triggering rollback and archiving.

[0049] The abnormality classification module is used for classifying abnormalities and forming event fingerprints, warehousing and updating discharge parameter templates and wear rules.

[0050] The present application has the following advantages: the present application realizes centralized management of electrode, machine tool capacity and quality threshold by unified work order analysis and work graph modeling, ensures the consistency of machining precision among different machine tools, improves overall efficiency, maintains the stability of continuous machining, guarantees traceability of each process through handover verification and quality closed loop, and realizes continuous optimization through abnormality classification and experience database updating, thereby reducing rework, improving the stability, precision and production rhythm of multi-machine collaborative machining. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 A step flowchart of a multi-machine collaborative machining control method of an electric spark machine is provided for an embodiment of the present application.

[0052] Figure 2A basic flow diagram of a multi-machine collaborative processing control system of an electric spark machine is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all embodiments.

[0054] Embodiment 1, refer to Figure 1 , provides a multi-machine collaborative processing control method of an electric spark machine, comprising the following steps:

[0055] Step S1, analyze the work order and CAD data, generate a work graph, and establish a mapping table.

[0056] Step S2, establish a three-dimensional reference and a cross-machine coordinate, generate an alignment state, set a medium pollution threshold and monitor.

[0057] Step S3, generate a candidate schedule and simulate with a discharge parameter template, output a main schedule and a handover list.

[0058] Step S4, generate a machine tool parameter package, update the gap in combination with the monitoring, and write in the electrode wear compensation.

[0059] Step S5, pre-handover verification, freeze and load the parameter package, retest to generate a receipt, deviation triggers rollback and archiving.

[0060] Step S6, classify the exception and form an event fingerprint, store and update the discharge parameter template and wear rule.

[0061] The present application realizes centralized management of electrodes, machine tool capacity and quality threshold by unified work order analysis and work graph modeling, ensures the consistency of machining accuracy between different machine tools, improves overall efficiency, maintains the stability of continuous machining, guarantees traceability of each process through handover verification and quality closed loop, and realizes continuous optimization through exception classification and experience database updating, which overall reduces rework, improves the stability, accuracy and production rhythm of multi-machine collaborative processing.

[0062] Step S1 comprises the following sub-steps:

[0063] Step S101, analyze the work order data and CAD data, extract process features, process features include process sequence, workpiece reference and geometric features, form a work graph, and mark electrode type and machining path in the work graph.

[0064] Step S101 converts the work order data and CAD data into a structured work graph, clearly defines the process sequence, workpiece reference and geometric features, and marks the electrode type and machining path, realizing integrated description of machining tasks and electrode demand.

[0065] Step S102, a mapping table between electrodes and processes is established, and electrode number, material, pre-grinding size, and allowed number of repetitions are bound to corresponding processes.

[0066] Step S102 establishes a mapping relationship between electrodes and processes, binds electrode number, material, size, and number of uses to specific processes, ensures consistency and traceability of electrode calling, and avoids processing errors caused by electrode mixing.

[0067] Step S103, generate machine tool capability vector and machine tool state vector, machine tool capability vector includes machine tool corresponding discharge power supply capacity, motion accuracy, C-axis rotation range and cooling capacity, machine tool state vector includes current running state.

[0068] Step S103 generates machine tool capability vector and machine tool state vector, and structures the power supply capacity, motion accuracy, C-axis range, cooling capacity and the current running state of the machine tool, realizes the quantitative management of machine tool resources and the matching of task scheduling.

[0069] Step S104, establish a quality acceptance threshold set, the quality acceptance threshold set includes size deviation range, shape and position tolerance range, surface roughness range and in-machine detection tolerance range.

[0070] Step S104 establishes a quality acceptance threshold set, and clearly defines the allowed range of size, shape, tolerance and roughness, forms a unified standard for subsequent scheduling review, processing determination and cross-machine transfer verification, and ensures the consistency and executability of quality control.

[0071] Step S1 realizes task modeling and resource baseline construction through work order analysis, process modeling, electrode mapping, machine tool capability modeling and quality standard setting, provides a unified data basis and judgment standard for subsequent cross-machine scheduling, parameter inheritance and quality control, and ensures controllable processing process and traceable resources.

[0072] Step S2 includes the following sub-steps:

[0073] Step S201, establish a three-dimensional reference frame and generate a cross-machine coordinate mapping:

[0074] A three-dimensional reference frame is established between the workpiece reference and the fixture reference, the working coordinate system of the machine tool is mapped to the three-dimensional reference frame, and the coordinate error is corrected.

[0075] Step S201 establishes a unified three-dimensional reference frame between the workpiece reference and the fixture reference, and maps the working coordinates of each machine tool to the reference frame, while correcting the coordinate error caused by installation, thermal drift or mechanical deviation, achieving consistency of cross-machine coordinates and accuracy of workpiece processing positioning.

[0076] Step S202, collect the offset data of the key positioning points and the measurement points in the machine to form an alignment state vector, which includes the translation bias, the rotation attitude and the thermal compensation parameters.

[0077] Step S202 collects the offset data of the key positioning points and the measurement points in the machine in real time to form an alignment state vector including the translation bias, the rotation attitude and the thermal compensation parameters, which provides accurate input for precision control and dynamic compensation in subsequent processing, and ensures that different machines maintain consistent alignment reference during processing.

[0078] Step S203, set the medium pollution index threshold and generate the in-machine monitoring plan, which is based on the online monitoring results of the medium circulation system, defines the particle size threshold, temperature threshold and viscosity threshold, forms the medium pollution index, and establishes the monitoring frequency and early warning rules, and links the online monitoring results with the quality acceptance threshold set, and triggers the process delay signal and the medium replacement signal when the medium pollution index exceeds the particle size threshold, temperature threshold and viscosity threshold.

[0079] Step S203 monitors the particle size, temperature and viscosity online through the medium circulation system, sets the pollution index threshold and forms the monitoring plan, links the monitoring results with the quality acceptance threshold set, and triggers the process delay and medium replacement signal when the pollution index is out of limit, to ensure that the processing medium is in a qualified state and avoid unstable discharge and processing defects caused by medium degradation.

[0080] Step S2 realizes the consistency of the workpiece coordinates and attitude in the cross-machine processing, and the real-time controllability of the discharge medium state, by establishing a three-dimensional reference frame, generating a cross-machine coordinate mapping, forming an alignment state vector, and setting a medium pollution monitoring mechanism, thereby ensuring the geometric precision and process environment stability of multi-machine collaborative processing.

[0081] Step S3 includes the following sub-steps:

[0082] Step S301, according to the process dependency relationship in the job graph, the mapping table between electrodes and processes, and the machine tool capability vector, a scheduling path is listed according to the multi-objective principle of machine time balance, minimum electrode replacement frequency and key surface priority, a candidate cross-machine scheduling sequence set is generated, and the handover node and the corresponding electrode use information are marked in the candidate cross-machine scheduling sequence set.

[0083] Step S301 generates a candidate cross-machine scheduling sequence according to the process dependency relationship in the job graph, the electrode-process mapping table and the machine tool capability vector, and marks the handover node and the electrode use information according to the principles of machine time balance, minimum electrode replacement frequency and key surface priority, thereby forming a scheduling scheme set covering multiple possible processing paths.

[0084] Step S302, call the pre-established discharge parameter template to process the candidate cross-machine scheduling sequence set to predict the processing time, discharge stability, deslagging efficiency and thermal accumulation risk. The discharge parameter template is established based on typical workpiece characteristics and historical processing experience. The sequences that violate the medium pollution threshold or exceed the quality acceptance threshold are removed, and the comprehensive scores of the remaining sequences are calculated.

[0085] Step S302 calls the discharge parameter template established based on typical workpiece characteristics and historical experience to predict the processing time, discharge stability, deslagging efficiency and thermal accumulation risk of the candidate scheduling sequence. Sequences that do not meet the medium pollution threshold or quality acceptance threshold are removed to ensure that only feasible and reliable process solutions are retained.

[0086] Step S303 selects the sequence with the highest comprehensive score to generate the main scheduling table, and lists the alignment state, medium pollution index, electrode wear compensation data and quality acceptance threshold check requirements in the handover checklist. The main scheduling table and the handover checklist are output.

[0087] Step S303 selects the scheduling sequence with the highest comprehensive score from the remaining solutions to generate the main scheduling table, and clearly lists the alignment state, medium pollution index, electrode wear compensation data and quality acceptance threshold check requirements in the handover checklist, so that complete operational instructions and inspection standards are provided for subsequent cross-machine handover execution.

[0088] Step S3 realizes optimal scheduling decision under multi-objective constraints by generating candidate scheduling sequences, simulating and screening based on discharge parameter templates, and outputting the main scheduling table and the handover checklist. This step can ensure processing efficiency while considering accuracy and stability, and provides clear execution basis and verification standards for cross-machine handover.

[0089] Step S4 includes the following sub-steps:

[0090] Step S401 selects the basic parameters from the discharge parameter template set based on the process characteristics, and corrects them in combination with the machine tool capability vector and the alignment state vector to form the machine tool parameter package. The machine tool parameter package includes voltage, current, pulse width, intermittent time and servo gain, and a version number is assigned to the machine tool parameter package.

[0091] Step S401 selects and corrects parameters from the discharge parameter template set by combining process characteristics, machine tool capability vector and alignment state vector, generates a machine tool parameter package containing voltage, current, pulse width, intermittent time and servo gain, and assigns a version number to realize parameter consistency and traceable management.

[0092] Step S402, the spark statistics, slag discharge signal and discharge stability are monitored in real time during the machining process, when the discharge abnormality or gap instability is detected, the gap adjustment parameters are generated, the gap adjustment parameters include the servo response speed adjustment signal, the intermittent time signal and the polarity control signal, and the gap adjustment parameters are written into the machine tool parameter package.

[0093] Step S402, the spark statistics, slag discharge signal and discharge stability are monitored in real time during the machining process, when the discharge abnormality or gap instability is detected, the gap adjustment parameters are generated, the gap adjustment parameters include the servo response speed adjustment signal, the intermittent time signal and the polarity control signal, and the gap adjustment parameters are written into the machine tool parameter package.

[0094] Step S403, specifically, the electrode cumulative discharge time, energy distribution and in-machine measurement deviation are used to calculate the electrode wear estimation value, the electrode wear estimation value includes the electrode end face wear amount and the side wall wear amount, the electrode wear estimation value is converted into trajectory offset and written into the compensation record.

[0095] Step S403, the electrode cumulative discharge time, energy distribution and in-machine measurement deviation are used to calculate the electrode wear estimation value, the electrode wear estimation value includes the electrode end face wear amount and the side wall wear amount, the electrode wear estimation value is converted into trajectory offset and written into the compensation record.

[0096] Step S4, by establishing the machine tool parameter package, dynamically updating the gap adjustment parameters and calculating the electrode wear compensation, the machining parameters are unified and dynamically adapted between different machine tools, the discharge process is kept stable, the electrode wear is compensated in real time, and the accuracy consistency and process inheritability of cross-machine machining are guaranteed.

[0097] Step S5 includes the following sub-steps:

[0098] Step S501, before the process handover, the offset of the in-machine measurement data calibration alignment state vector is called to check whether it is within the allowed range, the pollution index is confirmed to be below the threshold by comparing the medium online monitoring result, the version number of the currently loaded machine tool parameter package is checked to be consistent with the preset version number, and the machining data under the quality acceptance threshold is checked, and the pre-handover verification list is output.

[0099] Step S501, before the process handover, the alignment state vector, the medium pollution index, the machine tool parameter package version and the machining data are comprehensively verified, and the pre-handover verification list is output, so as to ensure the accuracy and environmental eligibility of the handover from the source.

[0100] Step S502, the machine tool parameter package used by the current machine tool is frozen in a version-locked manner, and the electrode wear compensation data and historical machining records are transmitted to the next machine tool, and the next machine tool enters the machining state after completing the parameter package loading and environmental self-checking.

[0101] Step S502 freezes the parameter package of the current machine tool in a version-locked manner, and transmits the electrode wear compensation data and processing history to the next machine tool, realizing consistent inheritance of parameters and compensation information, and ensuring the continuity of cross-machine processing.

[0102] Step S503, after loading the machine tool parameter package, the next machine tool performs in-machine measurement and reference comparison, generates a handover receipt file, and if the detection deviation exceeds the quality threshold, automatically triggers a rollback strategy, restores to the previous parameter package version or delays execution, and records the rollback event information.

[0103] Step S503 performs in-machine measurement and reference comparison immediately after the next machine tool loads the parameter package, generates a handover receipt, and if the detection deviation exceeds the quality threshold, automatically triggers a rollback strategy and records the rollback event, avoiding the execution of unqualified processes.

[0104] Step S504, the pre-handover verification list, the handover receipt file and the rollback event information are summarized and a quality signature record is generated, which is archived in a batch number and process number index manner.

[0105] Step S504 summarizes the pre-handover verification list, the handover receipt and the rollback event information into a quality signature record, and archives them according to batch number and process number, forming a complete quality closed-loop tracing system, ensuring the verifiability of process handover and the traceability of responsibility.

[0106] Step S5 establishes a closed-loop control system for cross-machine handover through pre-handover verification, parameter package freezing transmission, in-machine re-measurement and rollback mechanism, and quality signature archiving. This step ensures that different machine tools have unified reference, parameters and quality standards when handing over processes, avoids processing deviations caused by improper handover, and ensures the stability and traceability of multi-machine cooperation.

[0107] Step S6 includes:

[0108] Step S601, according to the real-time data collected during the in-machine monitoring and scheduling execution process, the abnormality is classified, and the classified abnormal type is output, the classified abnormal type includes short circuit rate rising, deslagging efficiency decreasing, unstable discharge, size drift and medium pollution index exceeding limit type, and the corresponding event fingerprint is generated for each type of abnormality, the event fingerprint includes abnormal occurrence time, machine tool capability vector, electrode batch and alignment state vector information.

[0109] Step S601 analyzes the real-time data collected during the in-machine monitoring and scheduling execution process, classifies the abnormality, such as short circuit rate rising, deslagging efficiency decreasing, unstable discharge, size drift and medium pollution exceeding limit, and generates an event fingerprint for each type of abnormality. The event fingerprint includes occurrence time, machine tool capability vector, electrode batch and alignment state vector information, realizing standardized and traceable description of abnormal events.

[0110] Step S602, the machine tool parameter package of the classified abnormal type, the gap adjustment parameter, the electrode wear compensation record and the actual detection result are mapped and stored in the experience library.

[0111] Step S602 maps the machine tool parameter package corresponding to the classified abnormality, the gap adjustment parameter, the electrode wear compensation record and the actual detection result, and stores them in the experience library, forms the causal relationship between the parameter input and the processing result, and facilitates the comparison and optimization of the subsequent scene.

[0112] Step S603, on the basis of the experience library update, the discharge parameter template set is revised, the electrode wear prediction rule is corrected, and an update log is generated. The content of the update log includes modification time, modifier, modification content and version number.

[0113] Step S603 revises the discharge parameter template and the electrode wear prediction rule based on the new data of the experience library, and generates an update log containing modification time, modifier, modification content and version number, realizes the continuous iteration and traceable management of processing knowledge and control strategy.

[0114] Step S6 establishes a self-learning and continuous optimization mechanism through abnormal classification, experience library mapping and parameter template iterative update, so that the multi-machine collaborative machining system of the spark machine can accumulate experience in long-term operation, dynamically correct the discharge parameters and wear prediction rules, thereby improving the robustness and adaptability of the system, reducing the occurrence rate of repeated abnormalities, and improving the processing quality and efficiency.

[0115] The electrode wear estimation value is calculated by combining the cumulative discharge time of the electrode, the energy distribution characteristics and the in-machine measurement deviation to obtain the electrode end face wear amount and the side wall wear amount, and the electrode wear compensation data is transmitted together with the machine tool parameter package when the machine is transferred.

[0116] The quality signature record and the transfer receipt are associated and archived with batch number, process number and machine tool number as index, and are attached with in-machine retest results, rollback event information and parameter package version number.

[0117] Embodiment 2, refer to Figure 2 , provides a spark machine multi-machine collaborative machining control system, which comprises a data analysis module, an alignment detection module, a parameter simulation module, an update compensation module, a verification receipt module and an abnormal classification module.

[0118] The data analysis module is used for analyzing work orders and CAD data, generating a work graph, and establishing a mapping table.

[0119] The alignment detection module is used to establish three-dimensional reference and cross-machine coordinates, generate alignment state, set medium pollution threshold and monitor.

[0120] The parameter simulation module is used for generating a candidate schedule and simulating with a discharge parameter template, outputting a main schedule and a handover list.

[0121] The update compensation module is used for generating a machine tool parameter package, combining a monitoring update gap, and writing an electrode wear compensation.

[0122] The verification reply module is used for pre-handover verification, freezing and loading a parameter package, retesting to generate a reply, triggering a rollback and archiving in the case of deviation.

[0123] The abnormality classification module is used for classifying abnormalities and forming an event fingerprint, warehousing and updating a discharge parameter template and a wear rule.

[0124] The present application aims at the problem of lack of unified scheduling and precision guarantee in multi-machine parallel processing in the prior art, and constructs a whole-process control chain from task analysis to abnormality learning. First, work orders and CAD data are analyzed, a work graph, electrode and machine tool capability mapping and quality threshold are established, and unified resource modeling is realized from the source. Then, three-dimensional reference alignment and medium pollution index monitoring are introduced in cross-machine processing, so as to ensure the alignment precision and discharge environment consistency between different machine tools. In the task allocation link, through simulation calculation based on the capability vector and the parameter template, the optimal scheduling path is selected, and the processing rhythm and quality requirements are considered. In the processing execution process, the machine tool parameter package is generated and updated, the discharge gap is adjusted in real time, and the electrode wear is predicted, so as to realize the cross-machine inheritance of compensation data. In the process of handing over, the quality closed loop is formed through verification, freezing parameters and in-machine retesting, so as to ensure the reliability of handover. In the aspect of abnormality processing, the abnormal process is converted into an event fingerprint written into the experience database, and the parameter template and the wear prediction rule are continuously revised. Overall, the error propagation and rework rate in multi-machine cooperation are effectively reduced, and the precision stability and production efficiency of complex workpiece processing are improved.

[0125] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for controlling multi-machine collaborative machining of electrical discharge machines, characterized in that, Includes the following steps: Step S1: Parse the work order and CAD data, generate the work drawing, and establish a mapping table; Step S2: Establish a three-dimensional reference and cross-machine coordinates, generate alignment status, set media contamination threshold and monitor it; Step S3: Generate candidate schedules and simulate them using discharge parameter templates, then output the main schedule and handover list; Step S4: Generate machine tool parameter package, and write electrode wear compensation based on monitoring and updating gap; Step S5: Verify before handover, freeze and load parameter package, retest to generate receipt, deviation trigger rollback and archive; Step S6: Classify anomalies and generate event fingerprints, store them in the database, and update the discharge parameter template and wear rules; Step S3 includes the following sub-steps: Step S301: Based on the process dependencies in the work diagram, the mapping table between electrodes and processes, and the machine tool capability vector, and in accordance with the multi-objective principles of machine time balance, minimum number of electrode changes, and priority of critical surfaces, list scheduling paths, generate a candidate cross-machine scheduling sequence set, and mark the handover nodes and corresponding electrode usage information in the candidate cross-machine scheduling sequence set. Step S302: Call the pre-established discharge parameter template to predict the processing time, discharge stability, slag removal efficiency and heat accumulation risk of the candidate cross-machine scheduling sequence set. The discharge parameter template is established based on typical workpiece characteristics and historical processing experience. Sequences that violate the medium contamination threshold or exceed the quality acceptance threshold are eliminated, and the comprehensive score of the remaining sequences is calculated. Step S303: Select the sequence with the highest comprehensive score to generate the master schedule table, and list the verification requirements for alignment status, medium contamination index, electrode wear compensation data and quality acceptance threshold in the handover checklist, and output the master schedule table and the handover checklist.

2. The multi-machine collaborative machining control method for electrical discharge machining as described in claim 1, characterized in that, Step S1 includes the following sub-steps: Step S101: Analyze the work order data and CAD data, extract process features, including process sequence, workpiece datum and geometric features, form a work drawing, and mark the electrode type and processing path in the work drawing; Step S102: Establish a mapping table between electrodes and processes, and bind electrode number, material, pre-grinding size, and allowable number of repetitions to the corresponding processes; Step S103: Generate machine tool capability vector and machine tool state vector. The machine tool capability vector includes the discharge power supply capacity, motion accuracy, C-axis rotation range and cooling capacity of the machine tool. The machine tool state vector includes the current operating state. Step S104: Establish a quality acceptance threshold set, which includes dimensional deviation range, geometric tolerance range, surface roughness range, and in-machine inspection tolerance range.

3. The multi-machine collaborative machining control method for electrical discharge machining as described in claim 2, characterized in that, Step S2 includes the following sub-steps: Step S201: Establish a three-dimensional reference frame and generate cross-machine coordinate mapping: A three-dimensional reference system is established between the workpiece datum and the fixture datum. The working coordinate system of the machine tool is mapped to the three-dimensional reference system, and the coordinate error is corrected. Step S202: On-site acquisition of offset data between key positioning points and measurement points to form an alignment state vector, wherein the alignment state vector includes translation offset, rotation attitude and thermal compensation parameters; Step S203: Set the media contamination index threshold and generate an on-machine monitoring plan. The on-machine monitoring plan is based on the online monitoring results of the media circulation system, defines the particle size threshold, temperature threshold and viscosity threshold, forms the media contamination index, and establishes monitoring frequency and early warning rules. The online monitoring results are linked with the quality acceptance threshold set. When the media contamination index exceeds the particle size threshold, temperature threshold and viscosity threshold, a process delay signal and a media replacement signal are triggered.

4. The multi-machine collaborative machining control method for electrical discharge machining as described in claim 3, characterized in that, Step S4 includes the following sub-steps: Step S401: Based on the process characteristics, select basic parameters from the discharge parameter template set, and modify them in combination with the machine tool capability vector and alignment state vector to form a machine tool parameter package. The machine tool parameter package includes voltage, current, pulse width, interval time and servo gain, and assign a version number to the machine tool parameter package. Step S402: During the machining process, the spark statistics, slag discharge signal and discharge stability are monitored in real time. When an abnormal discharge or gap instability is detected, gap adjustment parameters are generated. The gap adjustment parameters include servo response speed adjustment signal, intermittent time signal and polarity control signal. The gap adjustment parameters are written into the machine tool parameter package. Step S403 specifically involves calculating the electrode wear estimate based on the cumulative discharge time, energy distribution, and in-machine measurement deviation of the electrode. The electrode wear estimate includes the wear amount of the electrode end face and the wear amount of the sidewall. The electrode wear estimate is then converted into a trajectory bias and written into the compensation record.

5. The multi-machine collaborative machining control method for electrical discharge machining as described in claim 4, characterized in that, Step S5 includes the following sub-steps: Step S501: Before the process handover, call the on-machine measurement data to verify whether the offset of the alignment state vector is within the allowable range, compare the online monitoring results of the medium to confirm that the pollution index does not exceed the threshold, check that the version number of the currently loaded machine tool parameter package is consistent with the preset version number, verify the processing data under the quality acceptance threshold, and output the pre-handover verification list. Step S502: Freeze the machine tool parameter package currently used by the machine tool in a version-locked manner, and transmit the electrode wear compensation data and historical machining records to the next machine tool. After the next machine tool completes the parameter package loading and environmental self-check, it enters the machining state. In step S503, after loading the machine tool parameter package, the next machine tool performs in-machine measurement and benchmark comparison, generates a handover receipt file, and if the deviation of the detection result exceeds the quality threshold, the rollback strategy is automatically triggered to restore to the previous parameter package version or delay execution, and the rollback event information is recorded. Step S504: Summarize the pre-transfer verification checklist, transfer receipt documents, and return event information to generate a quality approval record, and archive it using batch number and process number index.

6. The multi-machine collaborative machining control method for electrical discharge machining as described in claim 5, characterized in that, Step S6 includes: Step S601: Based on the real-time data collected during the on-machine monitoring and scheduling execution process, the anomalies are classified and the classified anomaly types are output. The classified anomaly types include short circuit rate increase, slag discharge efficiency decrease, discharge instability, size drift and medium contamination index exceeding the limit. A corresponding event fingerprint is generated for each type of anomaly. The event fingerprint includes the anomaly occurrence time, the machine tool capability vector involved, electrode batch and alignment status vector information. Step S602: Establish a mapping relationship between the machine tool parameter package, clearance adjustment parameters, electrode wear compensation records and actual test results for the classification of abnormal types, and store them in the experience database; Step S603: Based on the updated experience base, revise the discharge parameter template set, correct the electrode wear prediction rules, and generate an update log. The update log includes the modification time, modifier, modification content, and version number.

7. The multi-machine collaborative machining control method for electrical discharge machining as described in claim 6, characterized in that, The electrode wear estimate is calculated by combining the cumulative discharge time of the electrode, energy distribution characteristics and in-machine measurement deviation to obtain the wear amount of the electrode end face and the wear amount of the side wall. The electrode wear compensation data is transmitted together with the machine tool parameter package during cross-machine handover.

8. The multi-machine collaborative machining control method for electrical discharge machining as described in claim 7, characterized in that, The quality approval records and handover receipts are archived in association with the batch number, process number, and machine tool number, and are accompanied by on-machine retest results, rollback event information, and parameter package version number.

9. A multi-machine collaborative machining control system for electrical discharge machining (EDM), applied in a multi-machine collaborative machining control method for EDM as described in any one of claims 1-8, characterized in that, It includes a data parsing module, an alignment detection module, a parameter simulation module, an update and compensation module, a verification receipt module, and an anomaly classification module; The data parsing module is used to parse work orders and CAD data, generate work drawings, and establish a mapping table. The alignment detection module is used to establish a three-dimensional benchmark and cross-machine coordinates, generate alignment status, set media contamination thresholds, and monitor them. The parameter simulation module is used to generate candidate schedules and simulate them using discharge parameter templates, and output the main schedule and handover list; The update compensation module is used to generate machine tool parameter packages and, in conjunction with monitoring update gaps, write electrode wear compensation. The verification receipt module is used for pre-handover verification, freezing and loading parameter packages, generating receipts through retesting, and triggering rollback and archiving due to deviations. The anomaly classification module is used to classify anomalies and generate event fingerprints, which are then stored in the database and updated with discharge parameter templates and wear rules.

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