Discrete manufacturing based order breaking method, system, and storage medium

Abstract

The present application relates to the technical field of manufacturing disassembly, and particularly relates to a disassembly method and system based on discrete manufacturing and a storage medium. The method comprises the following steps: obtaining manufacturing execution unit task flow data and collecting manufacturing unit processing component information, performing processing component disassembly processing to generate disassembly data; based on the disassembly data, evaluating a matching deviation condition, detecting a tool dynamic fatigue accumulation condition, and predicting a tool function progressive imbalance trend; further determining a tool trajectory deviation degree and a main shaft vibration growth condition, and calculating a component processing precision decline degree; finally, comprehensively analyzing disassembly adaptability abnormal conditions, realizing optimized management of the disassembly data, and outputting optimized processing component disassembly data; and the present application realizes more accurate manufacturing disassembly distribution through disassembly optimization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of manufacturing order splitting, and particularly relates to a manufacturing order splitting method and system based on discrete manufacturing and a storage medium. BACKGROUND

[0002] In the discrete manufacturing process, the tasks of the processing order usually need to be split into multiple sub-tasks and allocated to different manufacturing units for parallel processing, which is the order splitting process. Traditional order splitting methods rely on static process configuration and preset rules, and cannot dynamically respond to actual capacity matching deviations in the processing process, equipment state degradation, and differentiated processing requirements of complex structure parts, resulting in decreased production scheduling efficiency, increased part processing quality fluctuations, and even large-scale manufacturing delays and rework problems. In the prior art, some solutions attempt to integrate MES (Manufacturing Execution System) and ERP (Enterprise Resource Planning) systems to standardize the management of manufacturing unit processing tasks. In the long-term processing process, the progressive changes such as tool surface micro-crack growth, rigidity degradation, and stress fatigue are difficult to be sensed in time by traditional systems, causing processing path drift, abnormal cutting angle, and processing precision decline. However, the traditional order splitting of discrete manufacturing has the problems of inaccurate prediction of the progressive imbalance trend of the processing tool function and inaccurate detection of the progressive imbalance trend of the processing tool function. SUMMARY

[0003] Therefore, it is necessary to provide a manufacturing order splitting method and system based on discrete manufacturing and a storage medium to solve at least one of the above technical problems.

[0004] To achieve the above-mentioned purpose, a manufacturing order splitting method based on discrete manufacturing comprises the following steps:

[0005] Step S1: acquiring manufacturing execution unit task flow data; collecting manufacturing unit processing part information based on the manufacturing execution unit task flow data; performing processing part order splitting processing based on the manufacturing unit processing part information to obtain processing part order splitting data;

[0006] Step S2: evaluating the processing part order splitting matching deviation condition according to the processing part order splitting data; detecting the processing tool dynamic fatigue accumulation condition based on the processing part order splitting matching deviation condition; and predicting the progressive imbalance trend of the processing tool function based on the processing tool dynamic fatigue accumulation condition;

[0007] Step S3: determining the tool processing trajectory deviation degree according to the progressive imbalance trend of the processing tool function; detecting the machining spindle vibration growth condition based on the progressive imbalance trend of the processing tool function and the tool processing trajectory deviation degree; and calculating the part processing precision decline degree according to the machining spindle vibration growth condition;

[0008] Step S4: based on the degree of decline in component machining precision and the matching deviation condition of the machining component disassembling, analyze the disassembling adaptability abnormality of the machining order; according to the disassembling adaptability abnormality of the machining order, perform disassembling optimization management on the machining component disassembling data, and obtain optimized machining component disassembling data.

[0009] The present application realizes the standardization mapping of manufacturing tasks and the analysis of process node structure by obtaining and processing manufacturing execution unit task flow data, so that the physical matching relationship between the task flow and the machining components of the manufacturing unit has complete data source support, and provides a logical basis for subsequent machining component information collection and disassembling operation. Through disassembling processing based on actual manufacturing unit component information, the high coupling between manufacturing entity structure characteristics and task instruction structure is realized, and the disassembling data structure consistent with factors such as component form, clamping method, tool matching structure, and allowance configuration is established based on the original task, effectively ensuring the executability and process path consistency in the disassembling process. Based on the disassembling data, the difference identification and attribution calculation between the machining path instructions and the actual machining component contour characteristics are realized, so that the deviation phenomena such as clamping surface mismatch, interference area repetition, and tool path overlap caused by disassembling decision can be quantitatively identified. The deviation measure is used as key basic data for reverse derivation of the dynamic fatigue accumulation process of the machining tool, and a quantitative mapping mechanism between tool execution path load fluctuation, thermal stress disturbance, running frequency change, and structure microcrack is established, so that the tool function state evolution process can be accurately identified at the disassembling structure cascade feedback level. After obtaining the tool function progressive imbalance trend, further linkage application of the trend information in the trajectory control and spindle structure feedback layer is realized, the chain path between the tool state change and the mechanical system response is opened up through the precision determination of the tool trajectory deviation characteristics and the structure detection of the spindle vibration growth condition, and the machining system response dynamic chain caused by the tool function fluctuation is established, and through the quantitative determination of the spindle vibration frequency spectrum, displacement trend, and interference mode, the identification and structure attribution of the machining error source are completed. Therefore, the present application optimizes the traditional disassembling of discrete manufacturing, solves the problems of inaccurate prediction and detection of the tool function progressive imbalance trend in the traditional disassembling of discrete manufacturing, improves the accuracy of prediction and detection of the tool function progressive imbalance trend.

[0010] The present application also provides a disassembling system for discrete manufacturing, which is used to perform the disassembling method for discrete manufacturing as described above, and comprises:

[0011] The component disassembling processing module is configured to acquire manufacturing execution unit task flow data, collect manufacturing unit processing component information based on the manufacturing execution unit task flow data, and perform processing component disassembling processing based on the manufacturing unit processing component information to obtain processing component disassembling data.

[0012] The processing tool progressive imbalance trend evaluation module is configured to evaluate a processing component disassembling matching deviation condition according to the processing component disassembling data, detect a processing tool dynamic fatigue accumulation condition based on the processing component disassembling matching deviation condition, and predict a processing tool function progressive imbalance trend based on the processing tool dynamic fatigue accumulation condition.

[0013] The processing precision decline degree calculation module is configured to measure a tool processing track deviation degree according to the processing tool function progressive imbalance trend, detect a mechanical processing spindle vibration growth condition based on the processing tool function progressive imbalance trend and the tool processing track deviation degree, and calculate a component processing precision decline degree according to the mechanical processing spindle vibration growth condition.

[0014] The disassembling optimization management module is configured to analyze a processing order disassembling adaptability abnormal condition based on the component processing precision decline degree and the processing component disassembling matching deviation condition, perform disassembling optimization management on the processing component disassembling data according to the processing order disassembling adaptability abnormal condition, and obtain processing component disassembling optimization data.

[0015] The disassembling system for discrete manufacturing can realize any one of the disassembling methods for discrete manufacturing, and is used for combining the operation and signal transmission media between modules to complete the disassembling method for discrete manufacturing.

[0016] A computer readable storage medium stores a computer program, wherein the computer program is used to execute the disassembling method for discrete manufacturing. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 It is a step flow schematic diagram of the disassembling method for discrete manufacturing.

[0018] Figure 2 It is Figure 1 It is a detailed implementation step flow schematic diagram of step S3.

[0019] Figure 3 It is Figure 1 It is a detailed implementation step flow schematic diagram of step S4.

[0020] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. Detailed Implementation

[0021] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0022] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0023] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0024] To achieve the above objectives, please refer to Figures 1 to 3 A method for splitting orders based on discrete manufacturing includes the following steps:

[0025] Step S1: Obtain task flow data of the manufacturing execution unit; collect information on the parts processed by the manufacturing unit based on the task flow data of the manufacturing execution unit; perform part splitting processing based on the part processing information of the manufacturing unit to obtain part splitting data;

[0026] In the embodiment of the present application, in the discrete manufacturing system environment, the task flow data in the current period manufacturing execution unit is obtained through the task scheduling control module integrated in the workshop MES (manufacturing execution system). The task flow data includes task number, processing unit identification, task start and end time, designated process path, material tracking code, and corresponding component production process sequence information. The data is uploaded by the field industrial network collection device to the database of the central control server in real time, and is connected through the PLC and the edge computing terminal, ensuring that the task flow data is stored in the database according to the timestamp order. Based on the obtained manufacturing execution unit task flow data, the process flow interface is further called to obtain the component identification currently actually participating in processing of each manufacturing unit. The component information is extracted by reading the clamping station two-dimensional code recognition module, including component number, material code, belonging process section, process parameters (such as cutting depth, feed speed, etc.), processing tool number and initial dimensional tolerance, etc. The collected manufacturing unit processing component information is associated and analyzed with the task flow data to form a component and process section corresponding relationship table. Then, based on the manufacturing unit processing component information, the processing component order splitting processing operation is performed. The order splitting processing adopts a static feature matching algorithm combined with a process level time interval segmentation method, and the component task is split according to different process sections, different equipment capabilities and processing rhythm. For example, if a part needs to go through turning, milling and heat treatment three process sections, the order splitting processing will split the task based on the corresponding equipment distribution and process processing time interval of the three sections, and form multiple independent sub-tasks. The result after processing is the processing component order splitting data, including sub-task number, splitting node information, processing section identification, required tool number, component reference form description and corresponding equipment identification. The processing component order splitting data is output in this step.

[0027] Step S2: evaluating the processing component order splitting matching deviation condition according to the processing component order splitting data; detecting the processing tool dynamic fatigue accumulation condition based on the processing component order splitting matching deviation condition; predicting the processing tool function progressive imbalance trend based on the processing tool dynamic fatigue accumulation condition;

[0028] In the embodiment of the present application, the machining part disassembly data generated in step S1 is used to evaluate the matching deviation condition between the current disassembly task in the machining execution process and the preset process disassembly scheme according to the actual scheduling time axis set in the task scheduling system. The matching deviation evaluation takes the machining displacement trajectory of the tool corresponding to the task, the time difference between the task start and completion time, and the difference between the actual feed rate and the calibrated rate as input to construct a matching deviation vector. Among them, the displacement trajectory is obtained by reading the real-time tool position data in the numerical control system, the time difference is obtained by comparing the machining task start and end time stamps, and the feed rate is obtained from the CNC machine tool output feedback data. After the matching deviation vector is generated, further difference analysis is performed on the vector and the historical task standard trajectory library to obtain the abnormal load coefficient change of each sub-task in the execution process. The dynamic fatigue accumulation of the machining tool is obtained through difference calculation, the fatigue value is counted by the frequency of the actual load of the tool exceeding the set threshold value in the machining process, and is quantified in the form of "machining fatigue factor", the unit is N times / minute. Based on the fatigue accumulation value, combined with the machining path complexity factor (determined by the tool path point density and direction change frequency), the functional progressive imbalance trend of the tool is deduced. In this deduction process, the load evolution data of the same type tool under the same type process path in the tool life database is called to perform broken line integration on the load growth rate of the current tool under the machining path, calculate the tool function decline factor, and output the imbalance level of the current tool (divided into L1 to L5 five levels). Step S2 outputs the functional progressive imbalance trend level information and the corresponding imbalance factor of the machining tool.

[0029] Step S3: determining the tool machining trajectory deviation degree according to the functional progressive imbalance trend of the machining tool; detecting the mechanical machining spindle vibration growth based on the functional progressive imbalance trend of the machining tool and the tool machining trajectory deviation degree; calculating the part machining precision decline degree according to the mechanical machining spindle vibration growth;

[0030] In the embodiment of the present application, on the basis of the machining tool function progressive imbalance trend derived in step S2, the real-time path trajectory data of the tool machining process is called and vector difference analysis is performed with the standard CAD machining trajectory. The specific method is as follows: read the CNC control system recorded each tool position coordinates (X, Y, Z) during the processing task execution, and compare with the original tool position CAD path point by point, calculate the trajectory offset vector. The trajectory offset value is averaged according to the block after the machining area is gridded, and the overall trajectory offset degree is obtained, with the unit of μm. At the same time, the vibration signal of the main shaft machining process of the tool under the process is collected, and the signal collection device is a three-axis acceleration sensor arranged at the end of the main shaft. The collection frequency is set to 10 kHz, and after the vibration data is processed by FFT fast Fourier transform, the main frequency peak value and the corresponding number of subharmonics are extracted. The vibration intensity is normalized by the sum of the main frequency amplitude and the average amplitude of the harmonics, and the main shaft vibration growth factor is formed. Then, the decline degree of the part machining precision is evaluated according to the trajectory offset degree and the main shaft vibration growth factor. The machining precision decline evaluation adopts the size deviation deduction mechanism, and the key size data of the completed part is measured (using an online laser measurement system), and the difference between the measured size and the standard size is calculated, the deviation distribution trend is analyzed, and the machining precision decline degree of the part in the current task stage is output, with the unit of μm and its standard deviation. The output results of this step are the machining trajectory offset value (μm), the main shaft vibration growth factor, and the part machining precision decline value and deviation distribution.

[0031] Step S4: analyze the processing order disassembling adaptability abnormality based on the part machining precision decline degree and the processing part disassembling matching deviation condition; disassembling optimization management is performed on the processing part disassembling data according to the processing order disassembling adaptability abnormality, and the processing part disassembling optimization data is obtained.

[0032] In the embodiment of the present application, based on the component machining precision reduction value obtained in step S3 and the disassembling matching deviation vector in step S2, the disassembling adaptability abnormality of the machining order as a whole is evaluated. The adaptability abnormality analysis relies on the task-process path mapping model to establish a matching library between the task ID and the geometric shape, size requirement and material machining behavior it should complete. Then the machining precision reduction data and the disassembling deviation data are cross-analyzed. The specific processing flow includes: comparing the size deviation value corresponding to each subtask with the upper limit of the allowable deviation, if it exceeds the upper limit, it is marked as a first-level adaptability abnormality; if it does not exceed the limit but there is a disassembling deviation vector cumulative error greater than 20% of the equipment set error margin, it is marked as a second-level adaptability abnormality; if there is an increasing trend of trajectory deviation or abnormal spindle vibration frequency in the continuous three subtasks, it is marked as a third-level adaptability abnormality. According to the above abnormality level identification result, the original disassembling data is optimized and managed by means of machining path redistribution. The operation method is: the original tasks split according to the process sequence are recombined or re-disassembled according to the process load balance priority; the idle equipment load information is called through the task rearrangement module, the machining unit resources are re-matched, and the original task allocation path is adjusted; at the same time, the machining path code is regenerated, and the new disassembling task data set is output as the machining component disassembling optimization data, including the optimized task ID, the machining section path reconstruction data, the matching error control parameters and the task execution sequence correction table, providing input basis for subsequent production scheduling system loading and execution.

[0033] Preferably, step S1 comprises the following steps:

[0034] Step S11: obtaining manufacturing execution unit task flow data;

[0035] In the embodiment of the application, in the discrete manufacturing scene, the manufacturing execution unit generally includes hardware systems such as numerical control lathes, vertical machining centers, flexible manufacturing units, etc. The task flow data of the manufacturing execution unit is obtained by accessing the task issuing module in the manufacturing execution control platform (MES) and extracting the task flow log file therefrom. The task flow data is composed of fields such as manufacturing order number, operation step number, corresponding manufacturing unit identifier, planned processing time, processing sequence identifier code, material binding code, process path number, etc. In the specific operation, an industrial gateway based on an industrial Ethernet communication protocol (such as Modbus TCP / IP) or OPC UA protocol is deployed to connect the task data interface of the MES system with the manufacturing unit control system, and a 5-second sampling cycle is set to continuously read the real-time data in the task flow instruction cache area. All the read data are transferred through a local cache server, and are arranged by using the timestamp and task number double-index rule. The task flow data is subjected to cleaning operation, including eliminating missing field records, correcting field format inconsistency problems (for example, converting the time field to ISO 8601 standard format), and writing in the database in the PostgreSQL storage mode to build a standardized manufacturing execution unit task flow data set for subsequent analysis and processing order processing.

[0036] Step S12: acquiring manufacturing unit processing part information based on the manufacturing execution unit task flow data;

[0037] In the embodiment of the application, based on the task flow data obtained in step S11, the processing unit code and operation step number bound therein need to be parsed item by item to establish a one-to-one mapping relationship between the task flow instruction and the actual processing part. By calling the embedded process data interface of the manufacturing unit, the part information associated with each processing task is obtained, including part number, blank type, original shape size, pre-processing shape, process requirement, clamping method and expected surface roughness. In the data acquisition process, the industrial PLC (such as Siemens S7-1500 series) is used to read the sensor and control register values connected with the manufacturing equipment control logic to extract the clamping state change, spindle working state and tool switching event. In the manufacturing execution process, the visual recognition module (deploying industrial CCD camera + OpenCV image analysis framework) is used to recognize the part to be processed on the clamping device, and the consistency check is performed between the two-dimensional code identification (DPM code) and the material code in the task flow. After obtaining the preliminary information, the part design drawing (standard STP file format) and the manufacturing process parameter file (G code or tool path definition file) are used to structurally analyze the processing part information to generate a processing part information record with fields such as part number, geometric parameter, material type, rough machining-precision machining process sequence, etc. for supporting subsequent internal structure analysis operation.

[0038] Step S13: Machining part internal structure division is performed on the manufacturing unit machining part information to obtain machining part internal structure division data;

[0039] In the embodiment of the present application, the target of machining part internal structure division is to identify each geometric feature area in the part, and to perform module division according to machining sequence, tool contact path, cutting depth and process section. This step calls three-dimensional modeling data (input in STP format), and loads the part model using a CAD / CAE integrated platform (such as PTC Creo Parametric or Siemens NX). In the modeling platform, the built-in feature recognition module (Feature Recognition) is used to automatically analyze structural elements such as holes, grooves, steps, curved surfaces, threads and webs. By setting geometric recognition thresholds (such as defining a small deep hole as a round hole with a diameter less than 10 mm and a depth greater than 5 mm) and material removal strategies (based on the number of cutting layers in the Z-axis direction and the degree of coincidence of the tool profile), each part is divided into several structural machining units. Further combining the tool path section of the G code in the process file, the machining path is corresponded to each structural element, and the machining time, required tool number and clamping method of each structural unit are calculated. Using a rule engine, these structural units are divided into different unloading sections, such as "A zone outer contour rough machining", "B zone fine hole machining", "C zone thread tapping", etc., and each structural unit number, position index, structure type, machining process number, machining tool category, machining time, etc. fields constitute a structure division data table.

[0040] Step S14: Based on the machining part internal structure division data, machining part unloading processing is performed to obtain machining part unloading data.

[0041] In the embodiment of the present application, the structure division data generated according to step S13 is executed by a single disassembly rule engine. The single disassembly rule engine is composed of a preset logic control module, and the division criteria include: 1) whether it is a structure that can be continuously machined by the same tool; 2) whether the fixture needs to be replaced or repositioned; 3) whether there is a heat deformation sensitive area across multiple process sections; 4) whether the spindle load corresponding to each machining section exceeds the rated threshold of the manufacturing unit, etc. The single disassembly processing flow is as follows: a structure dependency graph is established for all structure units according to the machining sequence and geometric position, and the structure units with process logic sequence relationship are connected by directed edges. Then, according to the tool type, clamping times and machining heat distribution, the graph partition operation is performed on the structure dependency graph. In the graph partition process, depth-first traversal is adopted, and the position points where the clamping needs to be switched or the tool needs to be switched are marked on each path as splitting breakpoints. After all the breakpoints are marked, the structure units are grouped, and each group constitutes a machining subtask. Each subtask defines the machining structure unit set, the estimated machining time, the tool number used, the machining sequence number, the corresponding machining unit number, etc. All subtask data is summarized to form the machining part single disassembly data, which is output in CSV format and imported into the manufacturing planning system as the basis for subtask issuance. The machining part single disassembly data will be used to guide the detection and analysis of the single disassembly matching deviation, tool fatigue accumulation and spindle vibration trend in the subsequent steps.

[0042] Preferably, step S13 comprises the following steps:

[0043] Step S131: extracting the machining part material parameter according to the manufacturing unit machining part information;

[0044] In the embodiment of the present application, in the manufacturing execution process, the manufacturing unit machining part information is composed of manufacturing task flow data and machining part list data, wherein the part information field contains part number, corresponding work order number, structure code, material number, etc. In the implementation process, the original data relied on for extracting the machining part material parameter is the material number in the part information field, and the extraction operation is based on field mapping of the manufacturing resource database, which predefines a one-to-one correspondence table of all material codes and their physical property parameters (yield strength, elastic modulus, thermal conductivity, thermal expansion coefficient, etc.). In the implementation operation, an SQL statement is called to perform matching search on the material number in each part information in the database, and the returned fields include yield strength (unit MPa), Brinell hardness HB, work hardening index n, etc. After the query is completed, the ETL tool is used to perform field-level data fusion of the extracted material parameters and the original part information through the part number, and a part material parameter table with clear fields is output, with each row of data corresponding to a manufacturing part and containing its complete material physical property parameters.

[0045] Step S132: evaluate the machining difficulty coefficient of the machining part according to the yield strength of the machining part material parameter exceeding 355 MPa;

[0046] In the embodiment of the application, the empirical data table in the sequence of "hardness-cutting resistance-machining difficulty" is used as a reference, and the machining difficulty coefficient is set for different yield strength intervals. The machining difficulty coefficient is defined as a dimensionless coefficient, which reflects the comprehensive reflection of machining force, cutting heat and tool wear rate, and the value range is usually 0.7 to 1.2. In the evaluation operation, according to the yield strength interval, the built-in static parameter mapping table is called to return the corresponding machining difficulty coefficient. For example, when the yield strength is between 355-420 MPa, the corresponding machining difficulty coefficient is set to 0.927; if the yield strength exceeds 550 MPa, the corresponding coefficient is set to 1.035. The processing result forms a "machining part number-yield strength-machining difficulty coefficient" three-field data table.

[0047] Step S133: collect the three-dimensional structure of the machining part of the manufacturing unit based on the machining part information of the manufacturing unit;

[0048] In the embodiment of the application, the three-dimensional structure information of the machining part is collected by combining the analysis of the CAD file and the actual manufacturing path file. In the implementation process, the corresponding 3D modeling file is located by analyzing the structure code field in the part information, and the file format is limited to the STEP format (.stp) of the ISO 10303 standard. In the machining scheduling system, these files are imported in batches by using the CreoParametric software, and a Boundary Representation (B-Rep) model is generated for each part. In the B-Rep model, three-dimensional coordinates, topological relationships, and geometric body types (such as holes, bosses, grooves, and curved surfaces) are extracted to generate a topological index structure of faces-edges-vertices. At the same time, the NC program file (G code) associated with the part is read through the tool path analysis module to identify the tool contact area and the machining contour line, determine the machining direction and the accessible area. In addition, an industrial vision recognition module is integrated, and a CCD camera device is deployed at key workstations on the production line, and a data channel is established with the vision server. By projecting the outline of the physical part and fitting the outline with the B-Rep model, the structural consistency between the CAD model and the physical object is evaluated, and the data offset within the error range of ±0.2 mm is corrected. The collected three-dimensional structure is output in the structure body format, including the geometric body type, the space bounding box parameter (Bounding Box), the Z-direction projection area, the machining surface identifier, and the material removal volume ratio.

[0049] Step S134: identify the spatial connectivity of the machining part according to the three-dimensional structure of the machining part of the manufacturing unit;

[0050] In the embodiment of the present application, based on the B-Rep topological structure data formed in step S133, the spatial connectivity of the machined part is identified. This operation is based on the boundary body topological graph, constructs a spatial graph structure, performs triangular meshing processing (Triangular Mesh Generation) on all surfaces, divides each curved surface area using the Delaunay triangulation algorithm, and constructs a normal vector angle matrix between adjacent surfaces. According to the angle threshold between the triangular nets, it is judged whether there is a continuous machining path on the curved surface, and the angle less than 5° is regarded as a continuous region. On this basis, the depth-first search algorithm is used to find the connected subgraph in the topological graph, and all continuously machinable structure subsets are classified into a spatially connected unit. Each connected region is defined as a "structure machining domain" and numbered (such as C1, C2...). Through connectivity analysis, it can be judged whether the structure has "deep cavity", "blind hole", "interlocking structure" or "hollow area", and the area requiring special machining path (such as reverse clamping, multi-step clamping, etc.) is marked. The output spatial connectivity data includes structure number, connected domain number, machining continuity state, whether multi-axis linkage is required, etc. Field, which provides the basis for subsequent path evaluation.

[0051] Step S135: According to the three-dimensional structure of the manufacturing unit machined part and the spatial connectivity of the machined part, the machining path requirement data of the part is evaluated;

[0052] In the embodiment of the present application, a machining path requirement evaluation model is established for each part. The machining path requirement evaluation is based on the general rules of NC program, and a path complexity scoring system is constructed. The scoring criteria consist of the following dimensions: 1) the number of connected domains; 2) the number of times the tool needs to be switched; 3) the angle deviation of each connected region; 4) the number of deep hole / slot structures. According to the machining direction required by each machining surface in the structure, the tool incidence angle interval is calculated, and it is determined whether it can be completed by the current manufacturing unit (if five-axis linkage or special tool is required, the path complexity score is increased). At the same time, the shortest length of the tool path, the number of cutting times and the predicted machining heat load of all structure domains are calculated to form a path requirement quantization matrix. The "path requirement score" (value range 0~1) of each part is output, which is used to indicate the complexity of the machining path; at the same time, the machining sequence number and recommended incidence direction of each machining structure domain are output.

[0053] Step S136: When the machining path requirement data of the part and the machining difficulty coefficient of the machined part material exceed 0.927, the internal structure of the machined part is divided, and the internal structure division data of the machined part is obtained.

[0054] In the embodiment of the present application, the part record obtained in step S132 with the machining difficulty coefficient greater than 0.927 is combined and processed with the path demand score output in step S135. The processing logic is that when the machining difficulty coefficient of a part is higher than 0.927 and its path demand score is higher than 0.65 (indicating complex structure and high machining energy consumption), it enters the structure division module. The structure division module calls the B-Rep structure, structure machining domain division and path demand matrix generated in the foregoing steps. The division operation is performed according to the following two rules: 1) the structure domain that must be clamped or repositioned in the machining sequence is separately divided into a structure unit; 2) the structure surface that needs to switch the tool type or direction is aggregated and divided into a structure unit. When dividing, the graph theory partition method is used, the “machining breakpoint” is marked in the spatial topology graph, and the breakpoint is taken as the division basis to form multiple machining sub-unit structure domains. The parameters of each structure unit include its belonging connected domain, tool incident direction, predicted machining time, required cutting force and clamping mode, forming a complete machining structure division table, which is output in CSV format and used as the key input for the next step of single execution.

[0055] Preferably, the machining part single matching deviation condition evaluation in step S2 includes:

[0056] According to the machining part single data, the machining equipment capability mismatch condition is counted;

[0057] In the embodiment of the present application, the machining part single data set obtained through the manufacturing execution system MES is used to extract the machining process parameters of each machining task and the equipment capability parameters of the interfaced manufacturing unit. The machining process parameters involved include spindle speed, feed speed, tool type, clamping form and cooling mode required by the process; and the equipment capability parameters include equipment spindle speed limit range, maximum cutting torque, tool magazine tool position number, automatic tool changing speed, worktable stroke range and load bearing, etc. A linear comparison algorithm based on field precision matching is used to compare the process parameter requirements and equipment capability limits in the database one by one to form a mechanical capability matching relationship matrix. In each comparison item, the capability overflow value is recorded. When the overflow value is positive, it means that the equipment capability is insufficient. The ratio of the number of non-zero overflow values in the matrix to the total number of comparison items is obtained to obtain the mechanical capability mismatch rate data for subsequent step calling.

[0058] According to the machining part machining equipment capability mismatch condition, the machining equipment function adaptation deficiency condition is detected;

[0059] In the embodiment of the present application, the mechanical capability mismatch rate data generated in the previous step is taken as an input parameter, and further combined with the function configuration description table (including the number of supported tools, positioning mode, fixture type and switching mode, spindle cooling function, laser tool setting or visual recognition auxiliary positioning function module) recorded in the device function library for cross comparison. The function set analysis is performed using the conditional difference set cross logic, and the configuration items not contained or mutually exclusive between the current task requirements and the device functions are extracted one by one and marked as function adaptation missing. Each missing function is weighted according to the process criticality level set by the manufacturing system, and the overall machining instrument function adaptation deficiency score value is obtained. This value is used to reflect the degree of satisfaction of the current device function to the task requirements.

[0060] Based on the machining instrument function adaptation deficiency and the part machining mechanical capability mismatch, the machining instrument coverage range overrun degree is evaluated;

[0061] In the embodiment of the present application, the mechanical capability mismatch rate obtained in step S21 and the function adaptation deficiency score value obtained in step S22 are taken as double evaluation inputs into the coverage evaluation module. The evaluation module takes the device workspace model in three-dimensional space as a reference standard, and combines the part geometric size, the outermost contour boundary, and the machining angle requirement in the task to perform coverage rate spatial analysis. In the specific operation, the multi-boundary Bounding Box modeling method is used to analyze whether the task requirement workpiece boundary exceeds the effective range of the device workbench travel and tool rotation path. If there is an overflow area, the volume proportion of the overflow area to the total machining volume of the task is defined as the coverage range overrun degree index, and the overrun score value is calculated in combination with the score weight.

[0062] According to the machining instrument function adaptation deficiency and the machining instrument coverage range overrun degree, the instrument structure positioning ability collapse condition is identified;

[0063] In the embodiment of the present application, based on the function adaptation deficiency score value and the coverage range overrun score value, the machining instrument is jointly analyzed whether it can stably complete the positioning operation in actual operation. In the analysis, a three-dimensional registration coordinate system of the clamping system and the part positioning point is established, and the K coordinate set is used to connect the fixture installation structure, and the three-dimensional fitting is performed with the positioning hole structure required in the machining task. If the fitting error exceeds the maximum error tolerance threshold (such as ±0.2mm) of the device positioning system, it is marked as a structure positioning collapse point. At the same time, the risk points of the fixture in the actual positioning process are evaluated, the finite element static analysis method is used to simulate the fixture structure node displacement under the clamping state, and the displacement exceeding the predetermined critical value is recorded as the positioning ability collapse index. The index constructs the positioning ability collapse condition score with the number of collapse points and the proportion of the total contact points covered by the task.

[0064] According to the instrument structure, the collapse state of the alignment ability is measured, and the abnormal force condition of the instrument clamping is measured;

[0065] In the embodiment of the present application, the collapse state score of the alignment ability is combined with the real-time clamping data of the equipment clamp system to enter the clamping force analysis process. In the process, the real-time clamping data is derived from the feedback value of the clamping force sensor array (such as piezoelectric or strain gauge) collected by the equipment control system, the clamping pressure of each jaw node is time-sampled and the load change curve is drawn. At the same time, the offset of the initial setting force value and the actual force peak value is extracted to form the clamping abnormal strength index. If the force offset at the alignment collapse position exceeds 5%, it is recorded as a stress concentration point of the structure. The ratio of the number of concentration points to the area of abnormal force is the clamping force abnormality degree score.

[0066] Based on the abnormal clamping force condition of the instrument and the collapse state of the alignment ability of the instrument structure, the instrument clamping posture drift parameter is calculated;

[0067] In the embodiment of the present application, the clamping force abnormality degree score and the collapse state score of the alignment ability are jointly input into the posture offset analysis module. Based on the geometric parameters of the clamp (such as the number of jaws, the opening angle, the length of the jaw arm, the joint stiffness, etc.), a multi-rigid-body dynamics simulation system is constructed, the corresponding clamping load and torque are applied in the offline state, and the deflection angle change in the clamping process is simulated. The Euler angle difference method is used to measure the rotation offset angle of the clamping clamp between the initial setting posture and the clamping completion state to obtain the clamping posture drift parameter. The posture offset is expressed in angle units (°). The parameter is an important basis for evaluating the stability of the instrument clamping.

[0068] According to the instrument clamping posture drift parameter and the abnormal clamping force condition of the instrument, the processing part disassembly matching deviation condition is evaluated.

[0069] In the embodiment of the present application, the posture drift parameter and the clamping force abnormality degree score are comprehensively introduced into the deviation evaluation module. The module sets multiple combination threshold intervals to judge the deviation level of the processing part in the disassembly matching process caused by unstable instrument posture or uneven clamping. A weighted fusion function is used to perform numerical normalization and weighted summation on the posture drift angle and the clamping abnormal strength, and output the processing part disassembly matching deviation score. The higher the score, the worse the physical adaptability between the instrument and the disassembly task, and the greater the deviation. The score is output for subsequent process adjustment and equipment scheduling strategy optimization.

[0070] Preferably, the processing tool dynamic fatigue accumulation condition detection in step S2 comprises:

[0071] Based on the processing part disassembly matching deviation condition, the processing instrument tool cutting force overrun condition is detected;

[0072] In the embodiment of the application, the score value obtained in the previous step is matched with the rated threshold value of the cutting force of the tool. The cutting force of the tool is collected in real time by a spindle power sensor and a three-component cutting force sensor (Fx, Fy, Fz) with a sampling frequency of 1000 Hz, and a sliding time window of 0.5 seconds is used for average filtering algorithm to denoise the force signal. The system reads the three-dimensional force vector recorded by the sensor in each cutting task, combines the maximum allowable cutting force data provided by the tool manufacturer, and determines whether there is an event of instantaneous cutting force exceeding the rated upper limit by vector length calculation method. The number of cutting force over-limit events occurring in one disassembly task for each machining tool is counted, and the ratio of the number to the total machining time is formed into a cutting force over-limit rate, which is used as input data for subsequent judgment of tool mechanical impact growth.

[0073] According to the cutting force over-limit condition of the machining tool, the degree of tool mechanical impact growth is detected.

[0074] In the embodiment of the application, the cutting force over-limit rate data generated in the previous step is imported into the impact detection module, and the impact recognition standard in the cutting period is established in the module. The acceleration mutation points in unit time are detected by using the first derivative calculation method of the three-component cutting force curve, and the position and intensity of the instantaneous impact are judged. The derivative value at each force mutation moment is subtracted from the average derivative value of the stable cutting segment in the previous period to obtain the impact amplitude; at the same time, the frequency of impact events in unit time is counted to form the impact frequency parameter. The impact growth degree index is calculated by multiplying the impact amplitude and the impact frequency. The index is used to reflect the non-steady state loading frequency and intensity of the tool in the actual machining process, and is used for subsequent load step analysis.

[0075] According to the cutting force over-limit condition of the machining tool, the degree of tool mechanical impact growth is detected.

[0076] In the embodiment of the application, based on the cutting force over-limit rate of step S21 and the impact growth degree index of step S22, a load superposition curve is constructed. The load superposition curve takes the total cutting load (unit: Newton) of each machining time period as the vertical axis and the machining time as the horizontal axis. The piecewise linear regression analysis method is used to judge the sudden increase breakpoints in the curve, and the load slope change multiple is recorded. If the load slope increases more than twice before and after a certain time period, it is determined that this period is a load step period. The ratio of the length and number of all step periods to the total machining time is weighted to form a load step score value, which is used as a measurement basis for the nonlinear load evolution of the machining tool and is provided to the subsequent stress evaluation module.

[0077] Based on the machining tool step load growth condition, the stress growth degree of the machining tool is calculated.

[0078] In the embodiment of the application, the obtained load step score value is combined with tool structure data (including tool body length, spindle interface diameter, material elastic modulus, tool tip effective stress area) to establish a tool static force stress model. Through finite element uniaxial compression shear coupling method, static stress simulation calculation is carried out, actual stress data under different load steps are applied, and internal stress distribution of the tool under the action of spindle rotation and cutting feed combined force is simulated. In the simulation output result, the maximum equivalent stress point stress value is selected, and the stress growth rate is calculated by comparing the stress value with the yield limit of the tool material. The stress growth rate represents the degree of nonlinear growth of structural stress caused by load fluctuation in the machining process, which directly affects the development trend of subsequent micro cracks.

[0079] According to the stress growth degree of the machining tool and the step load growth condition of the machining tool, the micro crack growth trend of the machining tool is monitored.

[0080] In the embodiment of the application, the stress growth rate calculated in the loading step S24 and the load step score value in the step S23 are used to detect the tool micro crack development path. The detection tool is a high-frequency vibration detection sensor assembled between the tool handle and the spindle, the signal acquisition frequency is 10 kHz, and the vibration spectrum at the tool cutting-in and cutting-out moment in the machining section is subjected to Fourier transform analysis. If the stability decreases, the spectrum line width increases, and the harmonic peak value enhances in the high-frequency band, it can be determined that there is crack propagation behavior on the tool surface. By time fitting the crack propagation speed, a micro crack growth trend curve is obtained, and the crack length growth rate is taken as a trend parameter output, which provides a basis for subsequent roughness evolution analysis.

[0081] Based on the micro crack growth trend of the machining tool, the surface roughness growth trend of the machining tool is identified.

[0082] In the embodiment of the application, after the micro crack growth trend parameter is determined, the machining surface roughness data collected by the corresponding machining surface profilometer is read, and the time correlation coefficient between the tool machining surface roughness parameter and the crack growth rate is calculated by using the surface profile statistical standard (such as Ra, Rz, Rp, etc.). If the correlation coefficient is higher than 0.8, it is determined that the micro crack propagation behavior significantly affects the stability of the tool cutting trajectory, thereby causing the surface roughness deterioration trend. The Ra average growth speed is taken as the roughness growth trend index, and the sequence comparison is carried out between the machining batches, and the tool life degradation trend sequence is constructed. The trend data is used as the basis for micro deformation estimation, and the tool structure stability change path is further extended.

[0083] Based on the surface roughness growth trend of the machining tool and the micro crack growth trend of the machining tool, the micro deformation accumulation degree of the machining tool is determined.

[0084] In the embodiment of the application, the roughness growth trend parameter of step S26 and the crack propagation rate of step S25 are combined to construct a tool micro-deformation accumulation model. With the tool end as the coordinate reference, the offset between the tool end point after processing and the theoretical geometric profile is detected by multi-point scanning of the tool end point after processing by the laser displacement sensor, by comparing the initial geometric size. The maximum displacement value in the multi-point scanning is recorded as the micro-deformation index value, and a three-dimensional surface fitting relationship is established with the crack growth rate and the roughness growth rate, the deformation path corresponding to the maximum offset coordinate point in the fitted surface is extracted, and the deformation path is quantified as the tool micro-deformation accumulation degree index. The index reflects the micro-deformation stability trend of the tool after long-term stress.

[0085] The tool dynamic fatigue accumulation is detected according to the tool micro-deformation accumulation degree and the tool surface roughness growth trend.

[0086] In the embodiment of the application, the micro-deformation accumulation degree index and the surface roughness growth trend parameter are input to construct a tool fatigue loss integral function. The function forms the cumulative fatigue loss value by integrating the area of the micro-deformation on the time axis and the slope of the roughness curve. The fatigue tolerance thresholds of different types of tools (such as hard alloy end mills, indexable inserts, drills, etc.) are set, and according to the relative relationship between the cumulative value and the threshold, the current tool fatigue state is determined to be low, medium or high. The fatigue state data is output to the tool management system for tool replacement plan scheduling or maintenance reminder, and the whole process detection closed loop of the tool dynamic fatigue accumulation is completed.

[0087] Preferably, the processing tool function gradual imbalance trend prediction in step S2 comprises:

[0088] The tool material surface layer hardness decline degree is detected based on the tool dynamic fatigue accumulation.

[0089] In the embodiment of the application, after the identification of the dynamic fatigue accumulation of the machining tool is completed, the fatigue accumulation data is mapped to the machining history database with the tool number as the index, and the machining batch, machining material attribute, cutting path, cutting time, spindle speed and other key parameters corresponding to the number are extracted. Such data is input into the machining tool surface state detection device, which is composed of a laser rebound wave speed measurement module, a micro-texture comparison analysis module and a thermal decay response scanning module. The laser rebound wave speed measurement module emits a short pulse laser beam at a frequency of 10MHz, and determines the change of material surface hardness through the rebound time difference; the micro-texture comparison analysis module compares and analyzes the original manufacturing state and the current state of the tool surface, and if the unit area roughness peak-valley deviation increases by more than 0.5μm, it is considered that the surface layer material hardness has decreased; the thermal decay response scanning module applies a standard thermal load and monitors the local thermal diffusion rate of the tool surface, and if the diffusion coefficient decreases by more than 8%, it is also marked as indirect evidence of hardness decrease. After the above three detection data are normalized and scored, the tool surface layer hardness decrease degree determination result is formed and output to the downstream step in percentage form.

[0090] According to the decrease of the material surface layer hardness of the machining tool, the rigidity attenuation of the machining tool is detected when the decrease exceeds 10%;

[0091] In the embodiment of the application, when the hardness decrease of the tool material surface layer exceeds the 10% threshold, the system calls the historical rigidity measurement record associated with the tool number and starts the machining tool rigidity detection system for real-time detection. The system is composed of a laser interference rigidity measurement device, a stress rebound response comparison module and a precision load response bench. The laser interference device measures the unit deformation of the tool top at three fixed loads of 10N, 20N and 50N, and outputs the rigidity curve; the stress rebound response module detects the stress residual ratio in the unit load-unload cycle, and if the residual deformation accounts for more than 3% of the total deformation, it is marked as rigidity attenuation; combined with the historical curve, the change percentage between the current rigidity and the factory reference rigidity is calculated, and if the attenuation amplitude exceeds 10%, the cutting force fluctuation trend analysis step is triggered.

[0092] According to the decrease of the rigidity attenuation of the machining tool and the decrease of the material surface layer hardness of the machining tool, the cutting force fluctuation growth trend of the machining tool is analyzed when the decrease exceeds 10%;

[0093] In the embodiment of the present application, the current tool rigidity and the percentage data of the decrease in material hardness are obtained from the first two steps, and the cutting force fluctuation growth trend is analyzed by combining the force signal curve collected in real time by the cutting force sensor in the current machining task. The measuring device used is a three-dimensional torque sensing device, which is arranged between the main shaft tool handle and the machine tool workbench to collect three-dimensional cutting forces in the transverse, longitudinal and normal directions. The frequency distribution and peak value of the current cutting force fluctuation curve are compared with the historical reference curve. If the peak value in the low frequency band (0-10 Hz) increases by more than 15%, and the average fluctuation amplitude in the high frequency band (100-500 Hz) rises by more than 8%, it is determined that the cutting force fluctuation trend is growing. The relevant data is normalized after Fourier transform, and the fluctuation growth amplitude percentage is output as a prediction index.

[0094] According to the cutting force fluctuation growth trend of the machining tool, the intermittent cutting failure state of the machining tool is predicted;

[0095] In the embodiment of the present application, when the cutting force fluctuation growth trend amplitude exceeds the threshold value of 12%, the system accesses the cutting failure particle image monitoring channel in the machining task. The particle collection system separates the particles through the cutting area coolant, and then uses a scanning electron microscope imaging device (magnification 20000 times) to collect the microscopic morphology of the cutting failure particles, and combines a laser particle size distribution analyzer to extract particle size, edge sharpness, crack angle and other parameters. If the number of cutting failure particles increases sharply in unit time, and the average particle size exceeds 3 times the grain size of the machining material, it is judged that the intermittent cutting failure state exists; in addition, the end surface scanning profile of multiple batches of processed products is statistically analyzed, if the roughness Ra value fluctuation frequency is more than 5 times per minute, it can also be used as evidence of the existence of intermittent cutting failure. The system combines and encodes the above two types of data, and outputs the cutting failure probability judgment value.

[0096] Based on the intermittent cutting failure state of the machining tool, the torque coupling force distortion state of the machining tool is detected;

[0097] In the embodiment of the present application, after confirming the intermittent cutting failure state, the tool spindle drive unit torque data bound with the machining task is automatically called. The high-precision dynamic torque sensor (sampling frequency 2000 Hz) collects the spindle rotation torque in real time, and extracts the original torque curve within a 5-second time window before and after the tool cutting failure. The torque change rate is analyzed by first-order difference processing, and it is coupled with the machining path direction for analysis. If the included angle between the main component of the torque and the feed direction is between 90°±15°, and more than 20% abnormal fluctuation still occurs, it is judged that the torque coupling force distortion phenomenon exists. The distortion frequency, distortion amplitude and distortion density index are output as reference basis for subsequent bias load determination.

[0098] According to the torque coupling force distortion state of the machining tool and the intermittent cutting failure state of the machining tool, the bias load degree of the machining tool body is determined;

[0099] In the embodiment of the present application, the distortion density index output in the fifth step is combined with the intermittent collapse probability in the fourth step to construct a tool body load distribution surface map. The distribution of machining axial force and radial force at different points on the tool surface is used to calculate the maximum deviation value of the resultant force offset distance from the standard center axis. If the offset distance exceeds 20% of the tool edge radius, it is confirmed that the off-load exists. Further, based on the ratio of the off-load occurrence period to the total machining time in each machining cycle, the off-load frequency is calculated, and the off-load frequency and the offset amplitude are combined to output the off-load degree value to distinguish between mild, moderate or severe off-load categories.

[0100] According to the machining tool torque coupling force distortion condition and the machining tool off-load degree prediction, the functional progressive imbalance trend of the machining tool is predicted.

[0101] In the embodiment of the present application, the off-load degree classification obtained in the sixth step and the distortion density value in the fifth step are used in the prediction logic to set the functional imbalance prediction starting threshold as the distortion frequency being greater than 3 times per minute and the off-load degree being moderate or above. The cumulative load imbalance index (calculated from the ratio of cumulative off-load to total machining number) is used as the dominant variable, combined with the current machining product quality abnormality record (such as size deviation exceeding the upper limit of tolerance or surface defect density increasing), to set the functional progressive imbalance degree output as a risk level score of 0-100 to generate the machining tool functional progressive imbalance trend evaluation result and mark it as high, medium and low risk levels, which are used for controlling system to schedule tool replacement time and optimize single disassembly strategy.

[0102] Preferably, step S3 comprises:

[0103] Step S31: determining the tool machining trajectory offset degree according to the functional progressive imbalance trend of the machining tool;

[0104] In the embodiment of the present application, the risk level score of the output machining tool functional progressive imbalance trend is used as an input parameter, and the actual machining trajectory of the corresponding tool in the specific machining task is retrieved from the machining task scheduling system. The machining trajectory is recorded by a high-precision trajectory recording module in the embedded five-axis linkage machining center, which collects machining path point coordinate data in real time through photoelectric position sensors and encoders, once every 1 ms, covering the entire machining process. The actual trajectory data is aligned with the theoretical trajectory set in the machining task setting, and the difference is analyzed. For each machining section, the tool end trajectory coordinates are used as the reference to calculate the offset values in X, Y and Z directions. The machining offset at each time is obtained by using the Euclidean distance formula. Then when the functional imbalance risk level exceeds the threshold of 70, the offset is locally weighted and analyzed to calculate the maximum trajectory offset degree in the machining process, and the offset value data is output with millimeter-level precision, and the trajectory offset area is marked in the form of thermal distribution map on the machining console.

[0105] Step S32: calculating the cutting angle difference value data of the machining tool according to the tool machining trajectory offset degree and the machining tool function progressive imbalance trend;

[0106] In the embodiment of the application, the angle difference value calculation module is started by using the trajectory offset value data generated in step S31 and the function imbalance trend risk level. The module takes the initial angle of the tool cutting into the workpiece as the reference angle, and combines the trajectory offset direction and the angle variation amount to perform spatial posture inversion analysis. Specifically, in a three-dimensional coordinate system, a normal vector is constructed in the main direction of the tool cutting edge, and then a directional vector formed by the offset trajectory in a unit time period is used to calculate the included angle between the two vectors, and the included angle is the cutting angle difference value. In this process, a posture capture unit installed on the spindle head is used, which uses a gyro inertial unit and a three-axis acceleration sensor to output real-time posture change data, and simultaneously calls the function imbalance risk level parameter to perform risk weighting and correct the included angle change range. If the continuous angle difference value growth rate is detected to be more than 5°, the abnormal cutting area is recorded, the angle difference value data of each machining section is identified and stored by machining task number, and the output format includes timestamp, position index and angle difference value.

[0107] Step S33: estimating the machining tool contact instability condition according to the machining tool cutting angle difference value data;

[0108] In the embodiment of the application, the cutting angle difference value data obtained in the previous steps is input into the cutting path data sequence recorded by the tool posture sensor as the main input parameter, and the incident contact points of the tool and the workpiece surface are compared and analyzed frame by frame. By setting a three-dimensional reference frame under a multi-axis machining coordinate system, the ideal cutting angle and the current actual cutting angle are geometrically decomposed to obtain the tangential and normal force difference, and on this basis, a three-dimensional vector included angle error analysis method is introduced to perform integral calculation on the contact surface perturbation. If the tangential force fluctuation caused by the cutting angle difference value exceeds the critical offset value of 2.8N / mm, it is judged that the section has contact instability phenomenon. The laser displacement sensor and the high-frequency strain gauge are used to jointly collect the contact point drift frequency between the tool and the workpiece per unit time as a verification means to make a second confirmation on the contact point stability. All time periods with a contact point disturbance frequency higher than 5 times / s are marked as contact instability regions, and the contact instability condition data sequence is output as the basis for the subsequent steps.

[0109] Step S34: detecting the machining tool friction growth degree based on the machining tool contact instability condition;

[0110] In the embodiment of the application, the thermal energy release trend of the cutting end of the tool and the surface of the workpiece is monitored in real time by the infrared thermal imaging monitoring module arranged in the machining area using the contact instability data as the basic information source. According to the change rate of the thermal radiation intensity, the friction heat growth coefficient in unit time is calculated by combining the contact frequency per unit time measured in the previous sequence. The friction growth degree is expressed in the form of friction power growth rate, and the cutting speed, normal force change value and heat flux per unit area parameters are introduced into the formula. The friction power curve is obtained by using the area difference of the infrared spectrum gray scale curve, and the friction growth degree is quantified as percentage change data by this method. Parallel comparison verification is carried out on a plurality of tool samples, when the friction growth rate of a certain section exceeds 15% and the contact point disturbance frequency shows an increasing trend, the friction growth degree of the time section is marked as a high-risk area, which is used as the input data for evaluating the tool wear aggravation.

[0111] Step S35: detecting the vibration growth of the machining spindle based on the friction growth degree of the machining tool and the contact instability of the machining tool;

[0112] In the embodiment of the application, the friction growth degree data and the contact instability state index data obtained in the receiving step S34 are input into the machining spindle vibration monitoring module. The three-way acceleration sensor embedded in the module is arranged on the spindle bearing seat shell and the machining platform support, vibration acceleration data of the spindle during machining is collected, and the vibration power spectrum density of the spindle is calculated by using the time-frequency domain analysis method in combination with the tool unbalanced load degree and the friction force change trend. Especially for the acceleration response in the low frequency (30-60Hz) and medium frequency (60-200Hz) frequency band, the vibration peak characteristics highly coupled with the abnormal contact behavior of the tool are extracted. If periodic vibration peak growth occurs and the vibration power amplitude exceeds 1.5 times of the reference value, the spindle vibration growth phenomenon occurs, and the root mean square acceleration value (mm / s 2 ) and the vibration peak frequency (Hz) are used as data output and sent to the next machining precision evaluation and calculation process.

[0113] Step S36: calculating the part machining precision decline degree according to the machining spindle vibration growth and the machining tool cutting angle difference data.

[0114] In the embodiment of the present application, based on the spindle vibration growth index output in step S33 and the cutting angle difference data of each machining section in step S32, the part machining precision change calculation process is entered. This process relies on an offline detection platform to perform three-dimensional profile scanning and precision measurement on the completed part. The device used is a combination of a blue light three-dimensional scanner and a precise contact cylindrical probe system. The profile point cloud data of the machined surface is scanned and obtained, and the contact probe is used to accurately measure key geometric features such as hole diameter, end face perpendicularity, axis concentricity, etc. The measurement error of each key dimension point is analyzed by standard deviation, and the deviation ratio is calculated. The spindle vibration growth index and the cutting angle difference of each section are associated with the size error of the corresponding machining section, and the deviation attribution mapping matrix is used to deduce the influence proportion coefficient of vibration and angle change on precision. The precision reduction amount corresponding to the unit vibration increase or angle difference is calculated, and the overall part machining precision reduction degree data is summarized, and the average precision error growth rate is output, which is marked in microns. The machining quality heat map is output in combination with the specific machining trajectory section.

[0115] Especially important is that step S33 includes the following steps:

[0116] Step S331: predicting the machining tool wear aggravation condition based on the machining tool friction growth degree and the machining tool contact instability condition;

[0117] In the embodiment of the present application, the friction growth rate and the contact instability data are used as input conditions, and a differential analysis chain of multi-time point wear profile images is established to digitally reconstruct the edge profile of the machining tool cutting edge line. A three-dimensional profile scanning device is used to collect the edge shape at the initial and target time points, and a topographic matching method is used to calculate the wear width of a single tool edge line at 0.1 mm intervals. A time series linear interpolation algorithm is used to fit the wear process, and under the premise that the friction growth rate is constant, the super-linear deviation degree of the actual wear rate is analyzed. When the wear growth rate is greater than the reference rate (for example, 0.02 mm / min) by more than 30%, the tool state is defined as a wear aggravation state. The wear aggravation trend graph and the corresponding time stamp are output as the input basis for gap degree growth trend detection.

[0118] Step S332: detecting the machining tool gap degree growth trend according to the machining tool wear aggravation condition;

[0119] In the embodiment of the application, based on the wear profile image and its aggravation trend data, the real-time position offset data of the tool in the working state is obtained through the micro-gap monitoring device (using laser interference measurement technology) at the joint of the tool holder and the main shaft. 0.01mm is set as the micro-gap reference critical value, the tool holder swing amplitude under different loads is compared, and the synchronous error of the main shaft rotation angle is introduced for comparison data, and the deviation of the tool rotation center is analyzed. The circular runout value of the tool center is sampled with a resolution of 0.001mm, and the gap growth trend under the time sequence is calculated. If the gap growth amplitude exceeds 0.05mm in 30 minutes of continuous sampling and there is an overlapping interval with the wear aggravation time node, the gap growth trend data sequence is output as the basis for subsequent cutting vibration analysis.

[0120] Step S333: estimating the tool cutting vibration growth degree according to the tool gap growth trend and the tool friction growth degree;

[0121] In the embodiment of the application, the tool gap growth trend and the friction growth degree are coupled as input, and the three-axis vibration acceleration data at the joint of the main shaft and the tool holder is collected in real time by the in-embedded vibration accelerometer of the machine tool. The vibration frequency spectrum is deconstructed by Fourier transform, and the main frequency change data in the range of 10Hz to 5000Hz is extracted. The gap change is taken as an input parameter to modulate the frequency drift, and the friction growth degree is introduced as an amplitude adjustment factor to generate a theoretical vibration growth curve, which is fitted with the actual collected data. If the fitting deviation is less than 3%, the vibration growth trend is confirmed to be reliable, and a tool cutting vibration growth degree data table is formed, including three core parameters of vibration main frequency, peak acceleration and change rate.

[0122] Step S334: identifying the tool cutting vibration conduction condition based on the tool cutting vibration growth degree;

[0123] In the embodiment of the application, according to the vibration growth data and the main shaft structure parameters, the vibration propagation path mapping relationship is constructed. A plurality of MEMS acceleration sensors are arranged on the main shaft, bearing interface and workbench structural parts to synchronously collect vibration responses of different structural levels in the machining process. The phase difference and amplitude attenuation degree of vibration signals of different monitoring points are calculated through the waveform phase delay analysis method, and then the main direction of the vibration conduction path is identified. If the vibration signal intensity of the upper section of the main shaft and the waveform frequency of the cutting point are highly matched, and the phase difference is less than 0.02 seconds, it is confirmed that the vibration has been effectively conducted from the tool to the main body of the main shaft. The vibration conduction identification data including the conduction path, the phase difference and the energy attenuation ratio are output.

[0124] Step S335: detecting the vibration growth condition of the machining main shaft according to the tool cutting vibration conduction condition and the tool cutting vibration growth degree.

[0125] In the embodiment of the present application, on the basis of the existing vibration conduction data and the vibration growth degree, the total amount of spindle vibration increment analysis method is used to compare the total energy of the process initial stage and the current stage (the energy index is calculated by acceleration square integration). Combined with the overlap degree of the natural frequency of the spindle structure and the cutting frequency, whether the spindle has entered the critical resonance interval is evaluated. When the spindle vibration amplitude is higher than the reference vibration value by 20% or more for 10 consecutive minutes, and the frequency deviation rate is 1.5% or more, it is confirmed that the spindle vibration state has entered the growth interval, and a spindle vibration growth report is output, which covers the average spindle vibration amplitude, frequency change trend and vibration duration, which is used to drive downstream precision decay analysis.

[0126] Especially important is that step S34 comprises the following steps:

[0127] Step S341: Calculate the axial force component variation according to the machining tool cutting angle difference data;

[0128] In the embodiment of the present application, based on the machining tool cutting angle difference data measured in the previous steps, the static force decomposition method is used to calculate the axial force component variation in the three-axis coordinate system. The real-time offset of the cutting incident angle is recorded by the laser displacement interferometer and the tool posture recording device synchronously, and the cutting angle error Δθ is calculated by the included angle between the tool center line and the workpiece surface normal. Based on the error angle, the cutting force F is decomposed into normal force F and axial force F two components by using the force vector decomposition formula, where F axial = F cut *sin(Δθ). The real-time power output of the spindle is read by the load acquisition module in the numerical control machining system, and the cutting force is obtained by numerical integration, and the axial force component is calculated in real time based on the value. The axial force change value is archived according to the time sequence and the change curve is drawn, and 1N is set as the unit change threshold value. If the axial force change range exceeds 3N within 3 consecutive minutes, it is recorded as the axial force component fluctuation area, and the axial force change data is output, which is used to judge the stability of the structure stress in the machining process.

[0129] Step S342: Detect part machining stress instability based on axial force component variation;

[0130] In the embodiment of the application, the axial force change data obtained in step S341 is input, combined with the machining clamp force transmission structure parameters, and calculated and judged by the mechanical stability index. The workpiece is installed on a high-precision force-sensitive platform with six-point distribution, and the platform is embedded with a piezoelectric ceramic sheet, which can sense the micro-change of external force in real time. The axial force change value recorded every second during the machining process is analyzed by ratio with the clamp stiffness Kclamp, and the stress fluctuation degree under unit stiffness Pfluctuation=ΔFaxis / Kclamp is defined. If the fluctuation value continuously exceeds the threshold value 0.35 within a one-minute window, it is judged as a stress unstable region. The displacement response of the clamp surface in the unstable period is synchronously collected, and the spatial force field is mapped through the distributed strain fiber Bragg grating sensing network, and the three-dimensional stress disturbance distribution map and the corresponding stress fluctuation data are output.

[0131] Step S343: determining the fine displacement disturbance of the part machining based on the stress instability of the part machining;

[0132] In the embodiment of the application, in the region detected as stress unstable in step S342, a laser micro-interference displacement measurement system is arranged on the surface of the workpiece, and a laser triangulation module is used to non-contact real-time tracking of the fine displacement of the workpiece machining surface. The sampling accuracy is set to 0.01 μm, and the maximum displacement amplitude and its change frequency in each contact period are recorded. The displacement data obtained from the multi-point laser measurement device is synchronously transmitted to the central processing unit, and the machining displacement disturbance time course curve is established by the time domain reconstruction method. The dynamic frequency response analysis method is introduced, the interference frequency distribution density is calculated, and the main disturbance frequency band is extracted. The disturbance data and the stress fluctuation data are compared to confirm whether there is a frequency resonance phenomenon in the same time window, so as to verify whether the disturbance is caused by the stress instability. The displacement disturbance amplitude curve and the disturbance frequency band statistical data are output, and the quantitative index is provided for subsequent precision reduction calculation.

[0133] Step S344: detecting the center position drift of the main shaft according to the vibration growth of the machining main shaft;

[0134] In the embodiment of the present application, the vibration growth of the main shaft obtained in the previous step is cited as a trigger condition, and a main shaft center position tracking system is deployed, which is composed of a high-precision eddy current sensor array arranged at the end of the main shaft, which is distributed in a three-dimensional manner around the main shaft in a 360-degree direction at a spacing of 0.1 mm, and a total of 32 sensing points. The system collects the dynamic concentricity change and the radial eccentricity of the main shaft in real time. The theoretical trajectory of the main shaft center is set as a reference, and the actual trajectory of the main shaft center is analyzed through the eddy current signal to construct a real-time main shaft center drift trajectory graph. The center point trajectory offset value in the continuous time window is clustered and counted by using the coordinate barycenter migration algorithm. If the total displacement amplitude of the main shaft center exceeds 1.8 times the initial static eccentricity within a 5-minute processing period, and the trajectory change presents a linear offset trend, it is judged that there is a center position drift condition. The main shaft drift trajectory data and the center position change curve are output.

[0135] Step S345: calculating the part processing precision degradation degree based on the main shaft center position drift condition and the part processing subtle displacement disturbance condition.

[0136] In the embodiment of the present application, the subtle displacement disturbance amplitude curve in step S343 and the main shaft center position drift trajectory data in step S344 are combined to construct an error superposition analysis chain to perform coordinate mapping of the workpiece local micro-displacement disturbance region and the main shaft center offset vector field, and the superposition error region is calculated by using a spatial coincidence degree analysis method. The superposition value of the displacement amplitude and the offset trajectory in the error region is regarded as a local processing precision loss factor. The error point density in the entire processing region is integrated by using a surface domain integration method, and the processing precision degradation degree is expressed as a unit area processing precision degradation coefficient Dprecision, which is defined as the average geometric offset amount in a unit area of the processing surface. If Dprecision exceeds the critical value of 0.015 mm, a processing precision degradation alarm signal is output to form a processing precision degradation report, which includes a three-dimensional error distribution graph, an average error curve and a degradation degree evaluation result, which serves as an input basis for single-piece process adjustment or process compensation.

[0137] Preferably, step S4 comprises the following steps:

[0138] Step S41: evaluating the part manufacturing process failure trend based on the part processing precision degradation degree and the processing part single-piece matching deviation condition;

[0139] In the embodiment of the present application, the component processing precision decline degree index output in step S34 is called and the disassembling matching deviation data recorded in the discrete manufacturing task system is introduced. The disassembling matching deviation data is output by an assembly precision control module, which quantifies the processing component assembly interface deviation in different time periods under the same process path, and uses a grating alignment platform and a digital micro displacement measuring head to obtain the component assembly surface parallelism, hole distance matching error and surface contact characteristic deviation value. The same batch of processing components are matched and compared one by one to obtain the standard deviation index. The component processing precision decline degree and the disassembling matching deviation are processed in a synchronous time window to divide the corresponding matrix between each processing batch and each component matching interface. Then, the matrix is analyzed using a manufacturing node trend analysis module to extract the systematic deviation and repetitive precision defect sections existing on the same process path, and record the occurrence frequency and severity. This analysis generates a trend curve based on the time axis to output the component manufacturing process failure trend index, label the failure probability and potential impact range of each section according to the processing section number, and store it in the failure trend database as a subsequent process determination basis.

[0140] Step S42: Detecting component manufacturing disassembling process defect data according to the component manufacturing process failure trend;

[0141] In the embodiment of the present application, the component manufacturing process failure trend index obtained from step S41 is used as an input parameter to start the disassembling process flow comparison and analysis unit. Based on the process layout file in the disassembling task, the unit compares the standard process path with the task execution path in the failure trend concentrated area section by section. In the process section where the failure trend index exceeds 0.6, the operation log and processing state data stored in the manufacturing execution system (MES) are called to compare the actual disassembling execution path, identify disassembling level process defects such as task scheduling deviation, fixture not updated, and tool selection inconsistency. The defect data is marked by time stamp, station number and process task number, and the corresponding impact parameters of each type of defect are extracted, including process repetition rate decline percentage, interface alignment failure frequency, clamping slip ratio, etc. Combined with the time period of disassembling execution path deviating from the standard path and the processing precision defect time overlap rate, the disassembling process defect data set is formed. The data set will be the basis for subsequent abnormal adaptability analysis, and all defect data is stored in CSV format, with fields including defect type, occurrence station, corresponding task ID, impact index value and failure trend index corresponding value.

[0142] Step S43: Analyzing the disassembling adaptability abnormality of the processing order based on the component manufacturing disassembling process defect data and the component manufacturing process failure trend;

[0143] In the embodiment of the present application, step S43 takes the process defect data set generated in step S42 as input, and references the failure trend index output in step S41, and uses the order splitting adaptability analysis engine to perform deviation mapping between the current order splitting path execution and the standard process path. The adaptability analysis engine constructs a splitting logic graph, taking processing nodes as graph nodes and process execution dependency relationships as graph edges, and combining node failure records in the defect data set to evaluate the degree of influence of key paths in the splitting logic graph. The engine scores the effectiveness of each splitting path execution, based on indicators such as defect node proportion, key process influence ratio, and path interruption frequency. If there are three consecutive processes in the splitting path that simultaneously satisfy the conditions of a failure trend index higher than 0.7 and more than two splitting process defect records, the path is marked as an adaptability abnormal path. The adaptability abnormal path is mapped to the order dimension, and the analysis result is output as a list of order IDs and adaptability scores, with abnormal process segment IDs and abnormal type labels. This analysis process generates a structured abnormality list of all abnormal paths, which is used for subsequent data-driven splitting optimization management operations, and the abnormal path information is transmitted synchronously to the process scheduling center and the task adjustment module.

[0144] Step S44: Splitting optimization management of the processing component splitting data according to the splitting adaptability abnormality of the processing order, to obtain splitting optimization data of the processing component.

[0145] In the embodiment of the present application, the generated splitting adaptability abnormality list is taken as input, and the splitting task optimization management controller is used for scheduling path reconstruction. The controller relies on the manufacturing resource state monitoring platform to analyze existing machine tool resources, processing load conditions, tool life states, and queued sequences of tasks to be processed. The system determines the optimal splitting adjustment strategy based on the priority of task importance and influence path length. There are two splitting optimization methods: one is to migrate task nodes with excessively high failure trend in abnormal paths to low-risk sections or adjacent production lines, and the other is to introduce backup fixtures and different cutting parameter schemes through alternative processing strategies to avoid repeated triggering of overlapping risk nodes. After optimization, the system regenerates the splitting execution path, rebinds each path with machine tool number, execution time period, and corresponding tool number, and updates the process flow file. The optimization result forms a splitting optimization data set, which includes path comparison before and after optimization, beat adjustment data, and workstation resource reallocation records, and is output to the splitting control hub system, while updating the splitting path graph in the MES system. The system also cross- validates the optimization data with historical processing abnormality records to ensure that the optimization scheme does not introduce new risk paths. The output splitting optimization data is recorded in JSON structure and synchronized to each subsystem to complete splitting execution preparation.

[0146] The present application also provides a discrete manufacturing splitting system for performing the discrete manufacturing splitting method as described above, which comprises:

[0147] The component disassembly processing module is configured to acquire manufacturing execution unit task flow data, collect manufacturing unit processing component information based on the manufacturing execution unit task flow data, and perform processing component disassembly processing based on the manufacturing unit processing component information to obtain processing component disassembly data.

[0148] The processing tool progressive imbalance trend evaluation module is configured to evaluate a processing component disassembly matching deviation condition according to the processing component disassembly data, detect a processing tool dynamic fatigue accumulation condition based on the processing component disassembly matching deviation condition, and predict a processing tool function progressive imbalance trend based on the processing tool dynamic fatigue accumulation condition.

[0149] The processing precision decline degree calculation module is configured to measure a tool processing track deviation degree according to the processing tool function progressive imbalance trend, detect a mechanical processing spindle vibration growth condition based on the processing tool function progressive imbalance trend and the tool processing track deviation degree, and calculate a component processing precision decline degree according to the mechanical processing spindle vibration growth condition.

[0150] The disassembly optimization management module is configured to analyze a processing order disassembly adaptability abnormality condition based on the component processing precision decline degree and the processing component disassembly matching deviation condition, perform disassembly optimization management on the processing component disassembly data according to the processing order disassembly adaptability abnormality condition, and obtain processing component disassembly optimization data.

[0151] A computer readable storage medium storing a computer program, wherein the computer program is configured to execute the disassembly method of the discrete manufacturing.

[0152] The above description is merely a specific implementation of the present application, which enables those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method of deconsolidation based on discrete manufacturing, characterized in that, The method comprises the following steps: Step S1: acquiring manufacturing execution unit task flow data; Collect manufacturing unit processing component information based on manufacturing execution unit task flow data; Process the processing component order based on the manufacturing unit processing component information to obtain the processing component order data; Step S2: evaluate the processing component order matching deviation condition according to the processing component order data, wherein the processing component order matching deviation condition evaluation includes: According to the processing component order data, the component processing machine capacity mismatch situation is counted; According to the component processing machine capacity mismatch situation, the processing equipment function adaptation deficiency situation is detected; Based on the processing equipment function adaptation deficiency situation and the component processing machine capacity mismatch situation, the processing equipment coverage range overrun degree is evaluated; According to the processing equipment function adaptation deficiency situation and the processing equipment coverage range overrun degree, the equipment structure alignment ability collapse condition is identified; According to the equipment structure alignment ability collapse condition, the equipment clamping stress abnormal situation is measured; Based on the equipment clamping stress abnormal situation and the equipment structure alignment ability collapse condition, the equipment clamping posture drift parameter is calculated; According to the equipment clamping posture drift parameter and the equipment clamping stress abnormal situation, the processing component order matching deviation condition is evaluated; Based on the processing component order matching deviation condition, the processing tool dynamic fatigue accumulation situation is detected, wherein the processing tool dynamic fatigue accumulation situation detection includes: Based on the processing component order matching deviation condition, the processing equipment tool cutting force overrun situation is detected; According to the processing equipment tool cutting force overrun situation, the tool mechanical impact growth degree is detected; According to the tool mechanical impact growth degree and the processing equipment tool cutting force overrun situation, the processing tool step load growth condition is determined; Based on the processing tool step load growth condition, the processing tool stress growth degree is calculated; According to the processing tool stress growth degree and the processing tool step load growth condition, the processing tool micro crack growth trend is monitored; Based on the processing tool micro crack growth trend, the processing tool surface roughness growth trend is identified; Based on the processing tool surface roughness growth trend and the processing tool micro crack growth trend, the processing tool micro deformation accumulation degree is determined; According to the processing tool micro deformation accumulation degree and the processing tool surface roughness growth trend, the processing tool dynamic fatigue accumulation situation is detected; Based on the processing tool dynamic fatigue accumulation situation, the processing tool function progressive imbalance trend is predicted; Step S3: determine the tool processing track deviation degree according to the processing tool function progressive imbalance trend; Based on the processing tool function progressive imbalance trend and the tool processing track deviation degree, the mechanical processing spindle vibration growth situation is detected; According to the mechanical processing spindle vibration growth situation, the component processing precision decline degree is calculated; Step S4: based on the component processing precision decline degree and the processing component order matching deviation condition, analyze the processing order order matching abnormality; According to the processing order order matching abnormality, the processing component order data is optimized and managed to obtain the processing component order optimization data.

2. The discrete manufacturing-based order deconsolidation method of claim 1, wherein, Step S1 includes the following steps: Step S11: acquiring manufacturing execution unit task flow data; Step S12: collect manufacturing unit processing component information based on manufacturing execution unit task flow data; Step S13: manufacturing unit processing component information is processed, and component internal structure division data is obtained; Step S14: based on the processing component internal structure division data, the processing component is processed, and the processing component data is obtained.

3. The discrete manufacturing-based order deconsolidation method of claim 2, wherein, Step S13 includes the following steps: Step S131: according to the manufacturing unit processing component information, the processing component material parameter is extracted; Step S132: when the yield strength of the processing component material parameter exceeds 355MPa, the processing difficulty coefficient of the processing component material is evaluated, including: setting the processing difficulty coefficient for different yield strength intervals, the processing difficulty coefficient is defined as a dimensionless coefficient, the value range is 0.7 to 1.2, according to the yield strength interval, the built-in static parameter mapping table is called, and the corresponding processing difficulty coefficient is returned, when the yield strength is between 355~420MPa, the corresponding processing difficulty coefficient is set to 0.927, and if the yield strength exceeds 550MPa, the corresponding coefficient is set to 1.035; Step S133: based on the manufacturing unit processing component information, the manufacturing unit processing component three-dimensional structure is collected; Step S134: according to the manufacturing unit processing component three-dimensional structure, the processing component space connectivity is identified; Step S135: according to the manufacturing unit processing component three-dimensional structure and the processing component space connectivity, the component processing path requirement data is evaluated; Step S136: based on the component processing path requirement data and the processing component material processing difficulty coefficient, the processing component internal structure division is carried out, and the processing component internal structure division data is obtained.

4. The discrete manufacturing-based order deconsolidation method of claim 1, wherein, The processing tool function progressive imbalance trend prediction in step S2 includes: Based on the processing tool dynamic fatigue accumulation condition, the processing tool material surface hardness decline degree is detected; According to the processing tool material surface hardness decline condition, when it exceeds 10%, the processing tool rigidity attenuation condition is detected; According to the processing tool rigidity attenuation condition, when it drops by more than 10% and the processing tool material surface hardness decline degree is analyzed, the processing tool cutting force fluctuation growth trend is analyzed; According to the processing tool cutting force fluctuation growth trend, the processing tool intermittent collapse condition is predicted; Based on the processing tool intermittent collapse condition, the processing tool torque coupling force distortion condition is detected; According to the processing tool torque coupling force distortion condition and the processing tool intermittent collapse condition, the processing tool body bias load degree is determined; According to the processing tool torque coupling force distortion condition and the processing tool body bias load degree, the processing tool function progressive imbalance trend is predicted.

5. The discrete manufacturing-based order deconsolidation method of claim 1, wherein, Step S3 includes: Step S31: according to the processing tool function progressive imbalance trend, the tool processing track offset degree is determined; Step S32: according to the tool processing track offset degree and the processing tool function progressive imbalance trend, the processing tool cutting angle difference value data is calculated; Step S33: according to the processing tool cutting angle difference value data, the processing tool contact instability condition is estimated; Step S34: based on the processing tool contact instability condition, the processing tool friction growth degree is detected; Step S35: based on the processing tool friction growth degree and the processing tool contact instability condition, the mechanical processing spindle vibration growth condition is detected; Step S36: calculating the degree of decline of the part machining precision according to the machining spindle vibration growth and the machining tool cutting angle difference data.

6. The discrete manufacturing-based order deconsolidation method of claim 1, wherein, Step S4 includes the following steps: Step S41: evaluating the part manufacturing process failure trend based on the degree of decline of the part machining precision and the machining part order matching deviation condition; Step S42: detecting the part manufacturing order process defect data according to the part manufacturing process failure trend; Step S43: analyzing the machining order order matching adaptability abnormality condition based on the part manufacturing order process defect data and the part manufacturing process failure trend; Step S44: performing order optimization management on the machining part order data according to the machining order order matching adaptability abnormality condition, and obtaining machining part order optimization data.

7. A discrete manufacturing order breaking system characterized by, The computer program is executed to implement the discrete manufacturing order method as claimed in any one of claims 1 to 6. The part order processing module is configured to acquire manufacturing execution unit task flow data, collect manufacturing unit part information based on the manufacturing execution unit task flow data, and perform machining part order processing based on the manufacturing unit part information to obtain machining part order data. The machining tool progressive imbalance trend evaluation module is configured to evaluate a machining part order matching deviation condition according to the machining part order data, detect a machining tool dynamic fatigue accumulation condition based on the machining part order matching deviation condition, and predict a machining tool function progressive imbalance trend based on the machining tool dynamic fatigue accumulation condition. The machining precision decline degree calculation module is configured to determine a tool machining track deviation degree according to the machining tool function progressive imbalance trend, detect a machining spindle vibration growth condition based on the machining tool function progressive imbalance trend and the tool machining track deviation degree, and calculate a degree of decline of the part machining precision according to the machining spindle vibration growth condition. The order optimization management module is configured to analyze a machining order order matching adaptability abnormality condition based on the degree of decline of the part machining precision and the machining part order matching deviation condition, and perform order optimization management on the machining part order data according to the machining order order matching adaptability abnormality condition to obtain machining part order optimization data.

8. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed to implement the discrete manufacturing order method as claimed in any one of claims 1 to 6.

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