Laser welding adaptive clamping control method and system based on modular replacement

Abstract

The application discloses a laser welding adaptive clamping control method and system based on modular replacement, relates to the technical field of clamping control, and comprises the following steps: periodically calling a to-be-processed task in a production task pool, combining module configuration and processing sequence to perform production optimization to form a target welding task sequence; performing clamping process matching analysis to generate a module-level clamping timing curve; sequentially calling module configuration and welding path parameters of the current task; comparing the module configuration difference between the current task and the previous task, driving an automatic mechanism to complete quick replacement, and forming an adaptive station; after clamping the parts, dynamically and adaptively clamping according to the current path and the corresponding clamping timing curve to obtain a welding finished product; and the above process is cycled until all tasks are completed. The application solves the technical problems of low replacement efficiency and poor clamping control adaptability in the multi-task batch laser welding process in the prior art, and achieves the technical effects of improving the batch laser welding processing efficiency and the stability of the welding quality.

Description

Technical Field

[0001] This invention relates to the field of pressure control technology, and more specifically to an adaptive pressure control method and system for laser welding based on modular replacement. Background Technology

[0002] Laser welding, a common joining process in precision manufacturing, is widely used in batch processing of multi-specification parts. In multi-task continuous production, different parts to be welded typically correspond to different welding paths, clamping positions, and workstation module configurations. When the module configurations differ significantly between tasks, frequent workstation adjustments or clamping module replacements are required, easily causing processing interruptions and reducing batch production efficiency. Simultaneously, the heat input distribution and thermal deformation of parts vary under different welding paths. Fixed or single-parameter clamping methods are difficult to match the dynamic deformation requirements during welding, easily leading to insufficient clamping force, localized overpressure, or welding posture deviation, thus affecting the consistency and quality stability of the welded products. Summary of the Invention

[0003] This application provides a modular replacement-based adaptive holding control method and system for laser welding, which addresses the technical problems of low mold changing efficiency and poor holding control adaptability in multi-task batch laser welding processes in the prior art.

[0004] In view of the above problems, this application provides a laser welding adaptive holding control method and system based on modular replacement.

[0005] The first aspect of this application provides a laser welding adaptive holding control method based on modular permutation, the method comprising: The scheduling of multiple tasks to be processed periodically retrieved from the production task pool is optimized to obtain a target welding task sequence. Based on the multiple welding path parameters of the multiple tasks to be processed, welding adaptation holding process analysis is performed to obtain multiple sets of module-level holding timing curves, which are bound and written into the target welding task sequence. The Nth task module configuration and the Nth welding path parameters of the Nth task to be processed are sequentially called from the target welding task sequence. S1: The Nth task module configuration and the (N-1)th task module configuration are compared, and the automated execution mechanism is controlled to perform modular quick-change replacement to obtain a task-adaptive welding station. S2: After the parts to be welded are clamped at the task-adaptive welding station, dynamic adaptive holding control based on the Nth set of module-level holding timing curves is executed under the Nth welding path parameters to produce the Nth welded finished product. Steps S1 to S2 are executed repeatedly to perform multi-task batch laser welding processing until all tasks in the target welding task sequence are completed.

[0006] A second aspect of this application provides a modular displacement-based adaptive pressure holding control system for laser welding, the system comprising: The production scheduling optimization module is used to optimize the scheduling of multiple tasks to be processed periodically retrieved from the production task pool to obtain a target welding task sequence. The process analysis module is used to perform welding adaptation holding process analysis based on multiple welding path parameters of the multiple tasks to be processed to obtain multiple sets of module-level holding time sequence curves, which are then bound and written into the target welding task sequence. The calling module is used to sequentially call the Nth task module configuration and the Nth welding path parameters of the Nth task to be processed from the target welding task sequence. The replacement module is used for S1: comparing the Nth task module configuration and the (N-1)th task module configuration, controlling the automated execution mechanism to perform modular quick-change replacement to obtain a task-adaptive welding station. The holding control module is used for S2: after the parts to be welded are clamped at the task-adaptive welding station, dynamic adaptive holding control is performed based on the Nth set of module-level holding time sequence curves under the Nth welding path parameters to produce the Nth welded finished product. The loop module is used to repeatedly execute steps S1 to S2 to perform multi-task batch laser welding processing until all tasks in the target welding task sequence are completed.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: This application optimizes the scheduling of multiple tasks to be processed periodically retrieved from the production task pool to obtain a target welding task sequence. Based on multiple welding path parameters of the multiple tasks to be processed, welding adaptation holding process analysis is performed to obtain multiple sets of module-level holding timing curves, which are bound and written into the target welding task sequence. The Nth task module configuration and the Nth welding path parameters of the Nth task to be processed are sequentially called from the target welding task sequence. S1: The Nth task module configuration and the (N-1)th task module configuration are compared, and the automated execution mechanism is controlled to perform modular quick-change replacement to obtain a task-adaptive welding station. S2: After the parts to be welded are clamped at the task-adaptive welding station, dynamic adaptive holding control based on the Nth set of module-level holding timing curves is executed under the Nth welding path parameters to produce the Nth welded finished product. Steps S1 to S2 are executed repeatedly to perform multi-task batch laser welding processing until all tasks in the target welding task sequence are completed. This invention addresses the technical problems of low mold-changing efficiency and poor adaptability of holding control in multi-task batch laser welding processes in the prior art. By optimizing production scheduling, modular quick-change replacement, and dynamic control of module-level holding timing curves based on welding path parameters, it achieves the technical effect of improving the processing efficiency and welding quality stability of batch laser welding. Attached Figure Description

[0008] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1 A schematic diagram of the adaptive pressure holding control method for laser welding based on modular replacement provided in the embodiments of this application; Figure 2 A schematic diagram of the structure of the laser welding adaptive holding control system based on modular replacement provided in the embodiments of this application.

[0010] Figure labeling: Production scheduling optimization module 11, process analysis module 12, calling module 13, replacement module 14, pressure holding control module 15, cycle module 16. Detailed Implementation

[0011] This application provides a modular replacement-based adaptive holding control method and system for laser welding, which addresses the technical problems of low mold-changing efficiency and poor holding control adaptability in multi-task batch laser welding processes in the prior art. By optimizing production scheduling, modular quick-change replacement, and dynamic control of module-level holding timing curves based on welding path parameters, it achieves the technical effect of improving the processing efficiency and welding quality stability of batch laser welding.

[0012] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0013] It should be noted that any variation of the terms "comprising" and "having" is intended to cover non-exclusive inclusion, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to such processes, methods, products, or devices.

[0014] Example 1, as Figure 1 As shown, this application provides an adaptive holding control method for laser welding based on modular replacement, the method comprising: A100: Optimize the scheduling of multiple pending tasks periodically retrieved from the production task pool to obtain the target welding task sequence.

[0015] In this embodiment of the application, when optimizing the scheduling of multiple tasks to be processed periodically retrieved from the production task pool, welding feature analysis is performed on the multiple tasks to be processed periodically retrieved to parse and form the corresponding task module configuration, welding path parameters and task delivery limits. Based on the task module configuration, the multiple tasks to be processed are clustered and sorted according to their correlation to obtain an initial welding task sequence. Then, the priority of the initial welding task sequence is adjusted according to the task delivery limits so that the scheduling result simultaneously meets the module configuration correlation and delivery time limit constraints, and finally the target welding task sequence is generated.

[0016] Furthermore, the method provided in the application embodiments, which optimizes the scheduling of multiple tasks to be processed periodically retrieved from the production task pool to obtain a target welding task sequence, also includes: Welding feature analysis is performed on the multiple tasks to be processed to obtain multiple task module configurations, multiple welding path parameters, and multiple task delivery limits. Based on the multiple task module configurations, the multiple tasks to be processed are clustered and sorted according to their correlation to obtain an initial welding task sequence. The multiple task delivery limits are introduced to adjust the priority of the initial welding task sequence to obtain the target welding task sequence.

[0017] In this embodiment, when performing welding feature analysis on multiple tasks to be processed, multiple tasks to be processed that meet the processing status and equipment compatibility conditions are retrieved from the production task pool according to a preset retrieval cycle. The part structure, weld position, welding sequence, clamping requirements, and delivery requirements of each task to be processed are analyzed. Specifically, the task module configuration is determined based on the part structure and clamping requirements. The task module configuration is used to characterize the workstation module, holding module, and installation position relationship corresponding to the task to be processed. The welding path parameters are determined based on the weld position and welding sequence. The welding path parameters are used to characterize the welding trajectory, welding section, and time correlation information. The task delivery limit is determined based on the delivery requirements. The task delivery limit is used to characterize the latest completion constraint of the task to be processed. Thus, multiple task module configurations, multiple welding path parameters, and multiple task delivery limits corresponding to multiple tasks to be processed are obtained.

[0018] Next, based on the configuration of multiple task modules, the multiple tasks to be processed are clustered and sorted according to their relevance. In this process, firstly, multiple module configuration identifiers and module association features are extracted from the multiple task module configurations. The module configuration identifiers represent the types and combination relationships of the modules required by the tasks to be processed, and the module association features represent the position, connection, and collaborative assembly relationships between modules. Then, hierarchical relevance clustering is performed based on the multiple module configuration identifiers and multiple module association features to form K modular permutation task clusters. Further, the tasks within the K modular permutation task clusters are sorted according to multiple welding path parameters to obtain K welding task subsequences. Finally, the K modular permutation task clusters are sorted by the union of module permutation frequencies to obtain the cluster sorting result. The K welding task subsequences are then concatenated with reference to the cluster sorting result to form the initial welding task sequence.

[0019] Finally, multiple task delivery limits are introduced to adjust the priority of the initial welding task sequence. Specifically, based on the arrangement of multiple tasks to be processed in the initial welding task sequence and combined with production requirements, delivery delay conflict checks are performed on multiple task delivery limits to generate a delay warning mark sequence to identify whether there is a risk of delay for tasks to be processed. Then, based on the delay warning mark sequence, incremental conflict detection is performed on the tasks to be processed with delay warning marks to determine the number of new module replacements caused by the change of their position in the initial welding task sequence, and multiple task nodes to be adjusted are located accordingly. Finally, using the multiple task nodes to be adjusted as the adjustment starting point, under the constraint of minimizing the number of new module replacements, the initial welding task sequence is rearranged by task insertion, so that the tasks to be processed with delivery delay conflicts are adjusted to the position that meets the task delivery limits, thus obtaining the target welding task sequence.

[0020] To further illustrate the parameter calling relationships and sequence adjustment results of multiple pending tasks during the production scheduling optimization process, please refer to Table 1. Table 1 shows the task module configuration, welding path parameters, task delivery limits, initial welding task sequence positions, and target welding task sequence positions corresponding to multiple pending tasks periodically retrieved from the production task pool. Among them, the task module configuration represents the workstation module, holding module, and installation position corresponding to the pending task; the welding path parameters represent the welding trajectory, welding section, and welding time window; and the task delivery limits represent the latest completion constraint of the pending task. The correspondence between the initial welding task sequence positions and the target welding task sequence positions in Table 1 demonstrates the production scheduling changes after the pending tasks are sorted by correlation clustering and the task delivery limits are prioritized.

[0021] Table 1: Example Table of Scheduling Parameters for Tasks to be Processed T01 Workstation module M1; Holding module P1; Installation position A1 Welding trajectory L1; Welding section W1; Welding time window 0s~18s 10:30 1st place 1st place T02 Workstation module M1; Holding module P1; Installation position A1 Welding trajectory L2; welding section W2; welding time window 18s~36s 10:50 2nd place 2nd place T03 Workstation module M2; Holding module P2; Installation position A2 Welding trajectory L3; welding section W3; welding time window 36s~55s 10:45 4th place 3rd place T04 Workstation module M2; Holding module P2; Installation position A2 Welding trajectory L4; Welding section W4; Welding time window 55s~73s 11:10 3rd place 4th place T05 Workstation module M3; Holding module P3; Installation position A3 Welding trajectory L5; Welding section W5; Welding time window 73s~92s 11:30 5th place 5th place Furthermore, in the method provided in the application embodiments, based on the configuration of the plurality of task modules, the plurality of tasks to be processed are clustered and sorted according to their correlation to obtain an initial welding task sequence, which further includes: Multiple module configuration identifiers and module association features are extracted from the configurations of the multiple task modules. Hierarchical association clustering is performed based on these identifiers and features to obtain K modular permutation task clusters. The task connection order of the K modular permutation task clusters is sorted according to the multiple welding path parameters to obtain K welding task sub-sequences. The K modular permutation task clusters are then sorted by their union module permutation frequency to obtain a cluster sorting result. The K welding task sub-sequences are then used as the initial welding task sequence.

[0022] In this embodiment of the application, when parsing and extracting multiple module configuration identifiers and multiple module association features from multiple task module configurations, the task module configuration corresponding to each task to be processed is read sequentially, and information for distinguishing module type, module number and module combination relationship is extracted from the task module configuration to form a corresponding module configuration identifier; at the same time, according to the installation position, adjacent relationship and cooperation relationship of each module recorded in the task module configuration, the corresponding module association features are extracted; thus, each task to be processed is configured with a corresponding module configuration identifier and module association features.

[0023] Next, hierarchical clustering is performed based on multiple module configuration identifiers and multiple module association features. In this process, firstly, coarse-grained clustering is performed on multiple tasks to be processed based on multiple module configuration identifiers, grouping tasks to be processed with the same module type, module number, or module combination relationship into the same coarse cluster task group, resulting in P coarse cluster task groups; then, combined with multiple module association features, fine-grained clustering is performed on the tasks to be processed within each coarse cluster task group, grouping tasks to be processed with the same module installation location, adjacency relationship, or cooperation relationship into the same modular substitution task cluster, finally resulting in K modular substitution task clusters.

[0024] Subsequently, based on multiple welding path parameters, the tasks of K modular replacement task clusters are sequentially ordered. Specifically, after obtaining the K modular replacement task clusters, the welding path parameters of each task to be processed within each cluster are read. These parameters include the welding start position, welding end position, welding direction, welding section, and welding execution time window. The welding start position of each task to be processed is recorded as the start coordinate, and the welding end position as the end coordinate. The start time of the welding execution time window is also read. For the same modular replacement task cluster, the first task to be processed is selected from earliest to latest according to the start time of the welding execution time window. If the start times are the same, the first task to be processed is selected from smallest to largest according to the task number formed in the production task pool. After identifying the first task to be processed, it is designated as the current task. The path switching distance is then calculated for each unsorted task within the modular replacement task cluster. The calculation method is as follows: the absolute values ​​of the differences between the current task's termination coordinates and the unsorted task's starting coordinates in the X, Y, and Z directions are summed to obtain the path switching distance from the current task to that unsorted task. Simultaneously, the welding directions of the current task and the unsorted task are compared; if the welding directions are the same, it is recorded as 0; if they are different, it is recorded as 1. Next, the welding segments of the current task and the unsorted task are compared; if the subsequent welding segment is adjacent to or continuous with the previous welding segment, it is recorded as 0; otherwise, it is recorded as 1. Subsequently, among the unsorted tasks awaiting processing, the task with the smallest path switching distance is first selected and placed after the current task. When the path switching distances are the same, the task with the welding direction marked as 0 is selected. When the welding direction marks are the same, the task with the welding segment mark as 0 is selected. When the welding segment marks are still the same, the next task awaiting processing is selected in ascending order of task number generated in the production task pool. The selected next task awaiting processing is updated as the current task, and the above process of calculating the path switching distance, comparing the welding direction, comparing the welding segment, and sorting the task number is repeated until all tasks awaiting processing within the modular replacement task cluster are sorted. This generates K welding task subsequences for each of the K modular replacement task clusters.

[0025] Finally, the K modular replacement task clusters are sorted by their union module replacement frequency to obtain the cluster sorting result. The initial welding task sequence is then obtained by concatenating the K welding task sub-sequences. In this process, the task module configurations of all tasks to be processed within each modular replacement task cluster are read, and the module type, module number, and module installation position are extracted from the task module configurations. Duplicate module types, module numbers, and module installation positions that appear repeatedly within the same modular replacement task cluster are deduplicated, retaining only the unique module items to obtain the union module configuration corresponding to that modular replacement task cluster. Subsequently, any two modular replacement task clusters are compared item by item. The number of module items in the union module configuration of the previous modular replacement task cluster that do not appear in the subsequent modular replacement task cluster is counted, denoted as the number of removed module items. The number of module items in the union module configuration of the subsequent modular replacement task cluster that do not appear in the previous modular replacement task cluster is counted, denoted as the number of newly added module items. The number of removed module items and the number of newly added module items are added to obtain the union module replacement frequency between the two modular replacement task clusters. After calculating the union module permutation frequency between any two modular permutation task clusters, list all possible permutations of the K modular permutation task clusters. For each permutation, sum the union module permutation frequencies between adjacent modular permutation task clusters sequentially to obtain the total number of module permutations for that permutation. Select the permutation with the smallest total number of module permutations as the cluster sorting result. If multiple permutations have the same total number of module permutations, calculate the number of differences between the union module configuration of the first modular permutation task cluster in each permutation and the current workstation module configuration, and select the permutation with the smallest number of differences. If the number of differences is still the same, determine the cluster sorting result by ascending the task number of the first task to be processed within the first modular permutation task cluster in each permutation. Finally, read the corresponding K welding task sub-sequences sequentially according to the cluster sorting result, and concatenate them end-to-end according to the permutation order in the cluster sorting result to obtain the initial welding task sequence with the fewest consecutive permutations of tasks with the same module configuration.

[0026] Furthermore, in the method provided in the application embodiments, hierarchical correlation clustering is performed based on the multiple module configuration identifiers and multiple module association features to obtain K modular permutation task clusters, and the method further includes: Based on the configuration identifiers of the multiple modules, coarse-grained clustering of the multiple tasks to be processed is performed to obtain P coarse clustered task groups; combined with the association features of the multiple modules, fine-grained clustering of the P coarse clustered task groups is performed to obtain the K modular permutation task clusters.

[0027] In this embodiment, when performing coarse-grained clustering of multiple tasks to be processed based on multiple module configuration identifiers, the multiple tasks to be processed are taken as the processing objects. The module configuration identifier corresponding to each task to be processed is read. The module configuration identifier includes the module type, module number, and module combination relationship in the task module configuration. The module type, module number, and module combination relationship of each task to be processed are combined into coarse-grained clustering fields in a fixed order. Tasks to be processed with completely identical coarse-grained clustering fields are assigned to the same coarse-grained clustering task group, and tasks to be processed with inconsistent coarse-grained clustering fields are assigned to different coarse-grained clustering task groups. The multiple tasks to be processed are traversed according to the above division rules, and the number of coarse-grained clustering task groups is counted to obtain P coarse-grained clustering task groups.

[0028] When performing fine-grained clustering of P coarse clustering task groups by combining multiple module association features, for each coarse clustering task group, the module association features corresponding to each task to be processed within the group are read. The module association features include the installation position, adjacency relationship, and cooperation relationship of each module in the task module configuration. The installation position, adjacency relationship, and cooperation relationship of each task to be processed are combined into fine-grained clustering fields in a fixed order. Within the same coarse clustering task group, tasks to be processed with completely consistent fine-grained clustering fields are assigned to the same modular replacement task cluster, while tasks to be processed with inconsistent fine-grained clustering fields are assigned to different modular replacement task clusters. Fine-grained clustering is performed on each of the P coarse clustering task groups, and the number of modular replacement task clusters formed is counted to obtain K modular replacement task clusters.

[0029] Furthermore, in the method provided in the application embodiment, the initial welding task sequence is prioritized by introducing the plurality of task delivery limits to obtain the target welding task sequence, and the method further includes: Based on the arrangement of the multiple tasks to be processed in the initial welding task sequence, and in conjunction with production requirements, a delivery delay conflict check is performed on the delivery limits of the multiple tasks to obtain an overdue warning marker sequence; incremental conflict detection for mold replacement is performed on the overdue warning marker sequence to locate multiple task nodes to be adjusted; taking the multiple task nodes to be adjusted as the adjustment starting point, under the constraint of minimizing the number of new module replacements, task insertion and rearrangement are performed to obtain the target welding task sequence.

[0030] In this embodiment, when verifying delivery delay conflicts of multiple task delivery limits based on the arrangement of multiple tasks in the initial welding task sequence and in conjunction with production requirements, the task delivery limits corresponding to each task are read according to the arrangement order of the multiple tasks in the initial welding task sequence, and the start processing time and delivery order requirements are read from the production requirements. For the task at position i, the module configuration of task i is first compared with that of task (i-1) to obtain the module configuration differences that need to be replaced, added, or removed. Then, according to the preset execution time of each module replacement action in the automated actuator, the execution times corresponding to the module configuration differences are summed to obtain the modular replacement time corresponding to task i. At the same time, the clamping time recorded in the clamping process is read from the process file corresponding to the task, and the welding execution time is determined according to the welding execution time window in the welding path parameters of task i. The completion time, modular replacement time, clamping time, and welding execution time of the (i-1)th pending task are added together to obtain the completion time of the i-th pending task in its current arrangement. For the pending task at the top of the arrangement, the start processing time in the production requirements, the clamping time corresponding to the pending task, and the welding execution time are added together to obtain the completion time of the first pending task. The completion time of each pending task is compared with its corresponding task delivery deadline. If the completion time is not later than the task delivery deadline, a "not overdue" flag is written; if the completion time is later than the task delivery deadline, an "overdue warning" flag is written. The flags are then arranged according to the arrangement order in the initial welding task sequence to obtain the overdue warning flag sequence.

[0031] Next, incremental conflict detection is performed on the overdue warning mark sequence to locate multiple task nodes to be adjusted. Tasks with overdue warning marks are read according to the order of the overdue warning mark sequence, and a correspondence is established between each task and its current position in the initial welding task sequence. For any task with an overdue warning mark, each insertable position before its current position is selected sequentially. For each insertable position, the task module configuration between the two previously adjacent tasks is compared, and the number of module items that need to be replaced, added, or removed between the two previously adjacent tasks is counted to obtain the number of module replacements before insertion. Then, the task is placed in the insertable position, and the task module configuration between the task and the previous and next tasks are compared. The sum of the number of module items that need to be replaced, added, or removed in the two comparisons is counted to obtain the number of module replacements after insertion. The number of new module replacements corresponding to the insertable position is obtained by subtracting the number of module replacements before insertion from the number of module replacements after insertion. Simultaneously, the completion time of the task to be processed is recalculated according to the arrangement position after insertion, and the recalculated completion time is compared with its task delivery deadline. Insertable positions with completion times no later than the task delivery deadline and their number of new module replacements are written into the detection results corresponding to the task to be processed. When the task to be processed has at least one detection result, it is positioned as a task node to be adjusted. The detection of tasks to be processed with overdue warning flags is performed sequentially to obtain multiple task nodes to be adjusted.

[0032] Finally, starting with multiple task nodes to be adjusted, and under the constraint of minimizing the number of new module replacements, task insertion rearrangement is performed. Specifically, task insertion rearrangement is performed sequentially according to the arrangement order of multiple task nodes to be adjusted in the initial welding task sequence. For the current task node to be adjusted, the number of new module replacements and the recalculated completion time corresponding to each insertable position are read. First, insertable positions with completion times later than the task delivery deadline are eliminated. Then, the position with the smallest number of new module replacements is selected from the remaining insertable positions. When the number of new module replacements is the same for multiple insertable positions, the position with the earliest completion time is selected. When the completion times are the same, the position with the earliest arrangement in the initial welding task sequence is selected. The current task node to be adjusted is moved out of its original arrangement position and inserted into the selected insertable position. Then, the completion time of the affected tasks to be processed and the number of module replacements between adjacent tasks are recalculated according to the updated sequence. The insertion and rearrangement of multiple task nodes to be adjusted are completed sequentially. After each insertion and rearrangement, the arrangement position, completion time and adjacency relationship of the task to be processed are updated until all task nodes to be adjusted are completed, forming a target welding task sequence that meets the task delivery limit while minimizing the number of module replacements.

[0033] A200: Based on the multiple welding path parameters of the multiple tasks to be processed, perform welding adaptation and holding process analysis to obtain multiple sets of module-level holding timing curves, and bind and write them into the target welding task sequence.

[0034] In this embodiment, when performing welding adaptation and holding process analysis based on multiple welding path parameters of multiple tasks to be processed, the Nth welding path parameter is divided into Q welding execution time windows according to the spatial position of Q replaceable modules in the Nth module configuration identifier, and the corresponding Q welding thermal feature information is extracted. Then, the thermal deformation amount is estimated on the Q welding thermal feature information to obtain Q thermal deformation interference pressures corresponding to each replaceable module. Then, the Q thermal deformation interference pressures are restored and compensated according to the standard welding pressure baseline to form Q initial holding parameters. Finally, the Q welding execution time windows are used as the time axis reference, and the holding reverse thermal deformation correction is performed according to the Q initial holding parameters to generate Q module-level holding time sequence curves. These curves are then bound and written into the target welding task sequence after forming the Nth set of module-level holding time sequence curves.

[0035] Furthermore, in the method provided in the application embodiment, based on multiple welding path parameters of the multiple tasks to be processed, welding adaptation holding process analysis is performed to obtain multiple sets of module-level holding timing curves, which are then bound and written into the target welding task sequence, and the method further includes: Based on the spatial positions of the Q replaceable modules in the Nth module configuration identifier, Q welding thermal characteristic information is segmented into Q welding execution time windows from the Nth welding path parameters; thermal deformation is estimated for the Q welding thermal characteristic information to obtain Q thermal deformation interference pressures; restoration compensation calculations are performed on the Q thermal deformation interference pressures based on the standard welding pressure baseline to obtain Q initial holding parameters; using the Q welding execution time windows as the time axis reference, reverse thermal deformation correction is performed based on the Q initial holding parameters to obtain Q module-level holding time sequence curves, which constitute the Nth group of module-level holding time sequence curves.

[0036] In this embodiment, based on the spatial positions of the Q replaceable modules in the Nth module configuration identifier, when segmenting the Q welding thermal feature information within the Q welding execution time windows from the Nth welding path parameters, the spatial positions corresponding to the Q replaceable modules in the Nth module configuration identifier are read, and the Nth welding path parameters are read. The Nth welding path parameters are determined by the welding process file or the processing program of the laser welding equipment corresponding to the Nth task to be processed, and include the coordinates of the welding trajectory points, the corresponding time of the path points, the welding power, the welding speed, and the welding start and end positions. Continuous welding path segments are determined according to the order of the welding trajectory points, and the start and end coordinates of each welding path segment are respectively mapped to the spatial positions of the Q replaceable modules. When a welding path segment is located within the spatial position corresponding to a certain replaceable module, or corresponds to the holding area corresponding to that replaceable module, the welding path segment is assigned to the corresponding replaceable module. Subsequently, based on the earliest and latest path point times corresponding to the welding path segment for each replaceable module, the welding execution time window for that module is determined. Within this window, the welding power and speed of the corresponding welding path segment are read from the Nth welding path parameters, and the welding duration and path length are calculated. The welding duration is obtained by subtracting the earliest path point time from the latest, and the path length is obtained by summing the distances between adjacent welding trajectory points within the corresponding welding path segment. The welding thermal characteristic information for that replaceable module is formed by the welding power, speed, duration, and path length. This process of dividing the welding path segment, determining the welding execution time window, and extracting the welding thermal characteristic information is performed sequentially for Q replaceable modules, resulting in Q welding thermal characteristic information distributed across Q welding execution time windows.

[0037] Next, thermal deformation is estimated for the Q welding thermal feature information. First, Q material property parameters of the Q parts to be welded corresponding to the Q replaceable modules are extracted from the Nth processing task. Then, the boundary correspondence between the Q parts to be welded and each replaceable module is determined based on the association features of the Nth module. Subsequently, according to the boundary positioning results, the material property parameters of each part to be welded are bound to the corresponding replaceable module and the corresponding welding thermal feature information in a partition to form Q groups of pressure calculation units. Finally, based on the Q welding thermal feature information, the deformation pressure conversion based on material properties is performed on the Q groups of pressure calculation units to obtain Q thermal deformation interference pressures that correspond one-to-one with the Q replaceable modules.

[0038] Subsequently, the restoration and compensation calculations for Q thermal deformation interference pressures are performed based on the standard welding pressure baseline. Specifically, the standard welding pressure baseline, upper limit value of holding pressure, and lower limit value of holding pressure are read from the process file corresponding to the Nth processing task, and the corresponding Q thermal deformation interference pressures are read in the order of the numbers of the Q replaceable modules. Among them, the thermal deformation interference pressure is estimated from the aforementioned thermal deformation amount and has a positive or negative sign in the holding direction. The positive or negative sign is determined according to the deformation displacement of the part to be welded in the holding direction. When the deformation displacement direction is opposite to the holding direction, the corresponding thermal deformation interference pressure is recorded as a positive value, and when the deformation displacement direction is the same as the holding direction, the corresponding thermal deformation interference pressure is recorded as a negative value. For the q-th replaceable module, the standard welding pressure baseline and the q-th heat deformation interference pressure are algebraically added to obtain the q-th pressure calculation value. The q-th pressure calculation value is compared with the upper limit and lower limit of the holding pressure. When the q-th pressure calculation value is greater than the upper limit of the holding pressure, the upper limit of the holding pressure is determined as the q-th initial holding parameter. When the q-th pressure calculation value is less than the lower limit of the holding pressure, the lower limit of the holding pressure is determined as the q-th initial holding parameter. When the q-th pressure calculation value is between the lower limit and the upper limit of the holding pressure, the q-th pressure calculation value is determined as the q-th initial holding parameter. The above algebraic addition and limit comparison process is used to process Q heat deformation interference pressures sequentially to obtain Q initial holding parameters.

[0039] Finally, using Q welding execution time windows as the time axis reference, reverse thermal deformation correction is performed based on Q initial pressing parameters. In this process, the welding execution time window and initial holding parameters corresponding to each of the Q replaceable modules are read sequentially according to their numbering order. For the q-th replaceable module, the start and end times of its welding execution time window are read, and multiple control time points are generated between the start and end times according to the control cycle of the holding controller. Before the start time, the holding pressure value of the q-th replaceable module is recorded as the standard welding pressure baseline. Between the start and end times, the holding pressure values ​​at each control time point are recorded as the q-th initial holding parameters. After the end time, the holding pressure value is recorded as the standard welding pressure baseline. The time points and holding pressure values ​​corresponding to the q-th replaceable module are arranged in chronological order to form the q-th module-level holding timing curve. The welding execution time window reading, control time point generation, holding pressure value recording, and timing arrangement are performed sequentially on the Q replaceable modules to obtain the Q module-level holding timing curves. The Q module-level holding timing curves are then combined into the Nth group of module-level holding timing curves.

[0040] Furthermore, in the method provided in the application embodiment, estimating the thermal deformation amount of the Q welding thermal characteristic information to obtain Q thermal deformation interference pressures further includes: Extract the Q material property parameters of the Q parts to be welded corresponding to the Q replaceable modules from the Nth processing task; perform boundary positioning on the Q parts to be welded according to the association features of the Nth module, and bind the Q material property parameters to the Q groups of pressure calculation units; perform deformation pressure conversion based on material properties on the Q groups of pressure calculation units according to the Q welding thermal feature information to obtain the Q thermal deformation interference pressures.

[0041] In this embodiment, when extracting the Q material property parameters of the Q parts to be welded corresponding to the Q replaceable modules from the Nth processing task, the parts to be welded held or supported by each replaceable module are determined according to the parts list and the Nth module configuration identifier in the Nth processing task, following the numbering order of the Q replaceable modules. The material property parameters of the parts to be welded are then read from the process document, material list, or material database corresponding to the Nth processing task. The material property parameters include material grade, material thickness, elastic modulus, coefficient of thermal expansion, density, and specific heat capacity. When only the material grade is recorded in the Nth processing task, the corresponding elastic modulus, coefficient of thermal expansion, density, and specific heat capacity are retrieved from the material database based on the material grade, and these are combined with the material thickness of the parts to be welded to form the corresponding material property parameters. The process of confirming the parts to be welded and reading the material property parameters is performed according to each of the Q replaceable modules, resulting in the Q material property parameters of the Q parts to be welded that correspond one-to-one with the Q replaceable modules.

[0042] Next, based on the associated features of the Nth module, the boundaries of the Q parts to be welded are located, and the Q material property parameters are bound to the Q groups of clamping calculation units. Specifically, the installation position, adjacency relationship, clamping contact position, and clamping boundary of each replaceable module in the associated features of the Nth module are read, and the installation position, clamping contact position, and clamping boundary are unified to the same welding station coordinate system. For the qth replaceable module, the clamping area boundary is determined on the corresponding part to be welded according to its clamping contact position, the constrained boundary of the part to be welded in the area is determined according to the clamping boundary, and the distance between the clamping area boundary and the constrained boundary is determined as the equivalent length of the boundary of the qth group of clamping calculation units. The area enclosed by the clamping area boundary is determined as the clamping area of ​​the qth group of clamping calculation units. Subsequently, the qth replaceable module, the qth part to be welded, the qth material property parameter, the qth welding execution time window, the qth welding thermal characteristic information, the boundary equivalent length and the holding area are bound together to form the qth set of holding calculation units; the Q parts to be welded are bounded and partitioned in the same way to obtain the Q sets of holding calculation units.

[0043] Finally, based on Q welding thermal characteristic information, deformation pressure conversion based on material properties is performed on Q groups of holding calculation units. In this process, for the q-th holding calculation unit, the welding power, welding speed, welding duration, and welding path length from the q-th welding thermal characteristic information are read, along with the material thickness, elastic modulus, coefficient of thermal expansion, density, and specific heat capacity from the q-th material property parameters. First, the heat input within the q-th welding execution time window is calculated. When the welding power is constant, the welding power is multiplied by the welding duration to obtain the heat input. When the welding power changes with time, multiple time intervals are formed according to the corresponding times of adjacent path points. The welding power within each time interval is multiplied by the corresponding time interval and then summed to obtain the heat input. Then, the amount of heated material is calculated based on the material thickness, holding area, density, and specific heat capacity of the q-th holding calculation unit. The heat input is divided by the amount of heated material to obtain the temperature rise value of the q-th holding calculation unit; the amount of heated material is obtained by multiplying the material thickness, holding area, and density. Subsequently, the thermal deformation is calculated based on the coefficient of thermal expansion, temperature rise, and equivalent boundary length. The calculation method involves multiplying the coefficient of thermal expansion, temperature rise, and equivalent boundary length to obtain the thermal deformation of the q-th pressure calculation unit within the associated region of the pressure direction. The thermal deformation is then divided by the equivalent boundary length to obtain the deformation strain, which is multiplied by the elastic modulus to obtain the deformation pressure value of the q-th pressure calculation unit. This deformation pressure value is used as the q-th thermal deformation interference pressure, and a positive or negative sign is assigned to it according to the displacement direction of the thermal deformation in the pressure direction. The calculations for heat input, temperature rise, thermal deformation, deformation strain, and deformation pressure value conversion are performed sequentially on the Q-th pressure calculation unit to obtain Q thermal deformation interference pressures.

[0044] A300: Sequentially call the Nth task module configuration and Nth welding path parameters of the Nth task to be processed from the target welding task sequence.

[0045] In this embodiment, when sequentially calling the Nth task module configuration and Nth welding path parameters of the Nth task to be processed from the target welding task sequence, the Nth task to be processed corresponding to the current execution position is read sequentially according to the arrangement order of multiple tasks to be processed in the target welding task sequence, and the Nth task module configuration and Nth welding path parameters bound to the Nth task to be processed are extracted from the target welding task sequence. The Nth task module configuration is used to record the replaceable modules required by the Nth task to be processed, the module installation positions, and the module combination relationships. The Nth welding path parameters are used to record the welding trajectory, welding start and end positions, path point corresponding time, and welding process parameters corresponding to the Nth task to be processed. After the Nth task module configuration and Nth welding path parameters are called, they are used as the data basis for the modular quick-change replacement and dynamic adaptive pressure holding control of the Nth task to be processed. After the Nth task to be processed is completed, N is updated to the next sequence position, and the task module configuration and welding path parameters corresponding to the next task to be processed are called.

[0046] A400: S1: Compare the configuration of the Nth task module and the configuration of the (N-1)th task module, control the automated actuator to perform modular quick-change replacement, and obtain the task-adapted welding station.

[0047] In this embodiment, step S1 involves comparing the configuration of the Nth task module and the (N-1)th task module. When controlling the automated actuator to perform modular quick replacement, the module type, module number, module installation location, and module combination relationship in the Nth and (N-1)th task module configurations are compared item by item to identify multiple module configuration differences that require addition, removal, or replacement. Subsequently, based on the Nth module's association characteristics, the positioning and installation requirements corresponding to the multiple module configuration differences are matched and verified. After the matching is completed, the automated actuator is controlled to perform local module differentiation replacement in the area where the difference modules are located, so that the module configuration of the welding station is adapted to the Nth task to be processed, thus obtaining a task-adapted welding station.

[0048] Furthermore, in the method provided in the application embodiment, comparing the configuration of the Nth task module and the configuration of the (N-1)th task module, controlling the automated actuator to perform modular quick-change replacement to obtain the task-adapted welding station, further includes: By comparing the configuration of the Nth task module and the configuration of the (N-1)th task module, multiple module configuration differences are identified; the positioning and installation requirements of the multiple module configuration differences are matched with the associated features of the Nth module, and local module differential replacement is performed to obtain the welding station adapted to the task.

[0049] In this embodiment, when comparing the configuration of the Nth task module and the (N-1)th task module to locate multiple module configuration differences, the module type, module number, module installation location, and module combination relationship recorded in the Nth and (N-1)th task module configurations are read, and a corresponding relationship is established according to the module number or module installation location. When a module item exists in the Nth task module configuration but not in the (N-1)th task module configuration, the module item is identified as a newly added module configuration difference item. When a module item exists in the (N-1)th task module configuration but not in the Nth task module configuration, the module item is identified as a removed module configuration difference item. When both have corresponding module items but the module type, module number, module installation location, or module combination relationship is inconsistent, the module item is identified as a replaced module configuration difference item. All module items in the Nth and (N-1)th task module configurations are compared item by item according to the above comparison method to obtain multiple module configuration differences items. The module configuration differences items are used to characterize the module content that needs to be added, removed, or replaced when switching from the (N-1)th task to the Nth task.

[0050] When performing local module differential replacement, the system reads the installation position, adjacency relationship, and coordination relationship corresponding to the multiple module configuration differences in the Nth module's associated features, and uses these as the positioning and installation requirements for the corresponding module configuration differences. For newly added module configuration differences, the automated actuator moves the corresponding module to the installation position specified by the Nth module's associated features and completes the positioning and installation according to the adjacency relationship and coordination relationship. For removed module configuration differences, the automated actuator disconnects the corresponding module from its adjacent modules and moves the module out of its original installation position. For replacement module configuration differences, the automated actuator first removes the original module corresponding to the N-1th task module configuration, and then installs the target module corresponding to the Nth task module configuration to the position specified by the Nth module's associated features. After completing the addition, removal, and replacement operations for multiple module configuration differences, the system verifies the replaced module type, module number, module installation position, and module combination relationship. When the verification result matches the Nth task module configuration, the task-adapted welding station is obtained.

[0051] A500: S2: After the parts to be welded are clamped at the task-adaptive welding station, the Nth welding path parameters are executed, and the dynamic adaptive holding control based on the Nth group of module-level holding timing curves is used to produce the Nth welded product.

[0052] In this embodiment, step S2 involves clamping the part to be welded at the task-adaptive welding station, placing the part to be welded corresponding to the Nth task to be processed into the positioning area of ​​the task-adaptive welding station according to the module installation position, holding area, and clamping reference recorded in the Nth task module configuration, and supporting, positioning, and initially holding the part to be welded through the corresponding replaceable module; after clamping is completed, the Nth welding path parameters and the Nth group of module-level holding timing curves bound to the Nth task to be processed are read, wherein the Nth welding path parameters include the welding trajectory, welding start and end positions, path point corresponding time, and welding process parameters, and the Nth group of module-level holding timing curves include the time point and holding pressure value of each replaceable module within the corresponding welding execution time window; when the laser welding equipment executes the welding trajectory movement according to the Nth welding path parameters, the time corresponding to the path point is determined. The current welding time is matched with the welding execution time window in the Nth group of module-level pressing timing curves. When the current welding time falls into the welding execution time window corresponding to a certain replaceable module, the pressing pressure value corresponding to that time point is read and sent to the corresponding pressing actuator, so that the pressing actuator applies pressure to the corresponding pressing area. As the path points in the Nth welding path parameters advance sequentially, the current welding time and corresponding pressing pressure value are continuously read, and the welding trajectory, welding power, welding speed and pressing pressure of each replaceable module are controlled synchronously until the welding trajectory reaches the welding termination position in the Nth welding path parameters. After welding is completed, the pressing of the pressing actuator is released according to the end time of the Nth group of module-level pressing timing curves, and the welded part is taken out from the task-adaptive welding station to obtain the Nth welded finished product.

[0053] Step A600: Repeat steps S1 to S2 to perform multi-task batch laser welding processing until all tasks in the target welding task sequence are completed.

[0054] In this embodiment, when performing multi-task batch laser welding, steps S1 to S2 are executed cyclically. Each task to be processed is processed sequentially according to the order of multiple tasks to be processed in the target welding task sequence. For the current task to be processed, step S1 is executed first, comparing the task module configuration of the current task to be processed with the task module configuration of the previous task to be processed, and completing the modular quick-change replacement to obtain the corresponding task-adaptive welding station. Then, step S2 is executed, clamping the parts to be welded at the task-adaptive welding station, and performing dynamic adaptive holding control and laser welding based on the welding path parameters of the current task to be processed and the corresponding module-level holding timing curve. After the current task to be processed is completed, the next task to be processed in the target welding task sequence is processed until all multiple tasks to be processed in the target welding task sequence have completed the welding process, and the multi-task batch laser welding process ends.

[0055] Furthermore, the method provided in the application embodiments also includes: During the execution of the Nth processing task, the production task pool is dynamically monitored for new tasks based on delivery limits, so as to update the production schedule in real time by inserting the minimum mold change increment in the target welding task sequence.

[0056] In this embodiment, during the execution of the Nth task to be processed, dynamic task addition monitoring based on delivery limits is performed on the production task pool. Task records in the production task pool are read according to a preset monitoring cycle, and the read task records are compared with tasks already written into the target welding task sequence to identify newly added tasks not yet written into the target welding task sequence. For each newly added task, its task delivery limit, task module configuration, and welding path parameters are read, and its task delivery limit is compared with the estimated completion time of the remaining tasks in the current target welding task sequence. If processing the newly added task at the end of the original sequence would be later than its task delivery limit, an insertable position is selected one by one from the remaining sequence after the Nth task to be processed. For each insertable position… The algorithm first identifies the insertion position, compares the number of module configuration differences between adjacent task modules before insertion, and then compares the number of module configuration differences between the newly inserted task and the adjacent task modules before and after insertion. The sum of the number of differences after insertion is subtracted from the number of differences before insertion to obtain the mold change increment for that insertion position. Simultaneously, the algorithm calculates the completion time of the newly inserted task after insertion at that position and retains insertion positions with completion times no later than the task delivery deadline. From the retained insertion positions, the algorithm selects the position with the minimum mold change increment and inserts the newly inserted task into the target welding task sequence. The algorithm also synchronously updates the arrangement position and estimated completion time of each task after the insertion position, thus completing the minimum mold change increment insertion and real-time scheduling update of the target welding task sequence.

[0057] In summary, the embodiments of this application have at least the following technical effects: This application optimizes the scheduling of multiple tasks to be processed periodically retrieved from the production task pool to obtain a target welding task sequence. Based on multiple welding path parameters of the multiple tasks to be processed, welding adaptation holding process analysis is performed to obtain multiple sets of module-level holding timing curves, which are bound and written into the target welding task sequence. The Nth task module configuration and the Nth welding path parameters of the Nth task to be processed are sequentially called from the target welding task sequence. S1: The Nth task module configuration and the (N-1)th task module configuration are compared, and the automated execution mechanism is controlled to perform modular quick-change replacement to obtain a task-adaptive welding station. S2: After the parts to be welded are clamped at the task-adaptive welding station, dynamic adaptive holding control based on the Nth set of module-level holding timing curves is executed under the Nth welding path parameters to produce the Nth welded finished product. Steps S1 to S2 are executed repeatedly to perform multi-task batch laser welding processing until all tasks in the target welding task sequence are completed. This invention addresses the technical problems of low mold-changing efficiency and poor adaptability of holding control in multi-task batch laser welding processes in the prior art. By optimizing production scheduling, modular quick-change replacement, and dynamic control of module-level holding timing curves based on welding path parameters, it achieves the technical effect of improving the processing efficiency and welding quality stability of batch laser welding.

[0058] Example 2, based on the same inventive concept as the modular substitution-based adaptive pressure control method for laser welding in the previous examples, such as... Figure 2 As shown, this application provides a modular replacement-based adaptive pressure holding control system for laser welding. The system and method embodiments in this application are based on the same inventive concept. The system includes: The scheduling optimization module 11 is used to optimize the scheduling of multiple tasks to be processed periodically retrieved from the production task pool to obtain a target welding task sequence; the process analysis module 12 is used to perform welding adaptation holding process analysis based on multiple welding path parameters of the multiple tasks to be processed to obtain multiple sets of module-level holding time sequence curves, and bind and write them into the target welding task sequence; the calling module 13 is used to sequentially call the Nth task module configuration and the Nth welding path parameters of the Nth task to be processed from the target welding task sequence; the replacement module 14 is used for S1: comparing the Nth task module configuration and the Nth welding path parameters of the Nth task to be processed. The N-th task module configuration and the N-1-th task module configuration control the automated actuator to perform modular quick-change replacement to obtain a task-adaptive welding station; the holding control module 15 is used in S2: after the parts to be welded are clamped in the task-adaptive welding station, the dynamic adaptive holding control based on the Nth group of module-level holding timing curves is executed under the Nth welding path parameters to produce the Nth welded product; the loop module 16 is used to repeatedly execute steps S1 to S2 to perform multi-task batch laser welding processing until all tasks in the target welding task sequence are completed.

[0059] Furthermore, the system is also used to implement the following functions: Welding feature analysis is performed on the multiple tasks to be processed to obtain multiple task module configurations, multiple welding path parameters, and multiple task delivery limits. Based on the multiple task module configurations, the multiple tasks to be processed are clustered and sorted according to their correlation to obtain an initial welding task sequence. The multiple task delivery limits are introduced to adjust the priority of the initial welding task sequence to obtain the target welding task sequence.

[0060] Furthermore, the system is also used to implement the following functions: Based on the spatial positions of the Q replaceable modules in the Nth module configuration identifier, Q welding thermal characteristic information is segmented into Q welding execution time windows from the Nth welding path parameters; thermal deformation is estimated for the Q welding thermal characteristic information to obtain Q thermal deformation interference pressures; restoration compensation calculations are performed on the Q thermal deformation interference pressures based on the standard welding pressure baseline to obtain Q initial holding parameters; using the Q welding execution time windows as the time axis reference, reverse thermal deformation correction is performed based on the Q initial holding parameters to obtain Q module-level holding time sequence curves, which constitute the Nth group of module-level holding time sequence curves.

[0061] Furthermore, the system is also used to implement the following functions: Extract the Q material property parameters of the Q parts to be welded corresponding to the Q replaceable modules from the Nth processing task; perform boundary positioning on the Q parts to be welded according to the association features of the Nth module, and bind the Q material property parameters to the Q groups of pressure calculation units; perform deformation pressure conversion based on material properties on the Q groups of pressure calculation units according to the Q welding thermal feature information to obtain the Q thermal deformation interference pressures.

[0062] Furthermore, the system is also used to implement the following functions: Multiple module configuration identifiers and module association features are extracted from the configurations of the multiple task modules. Hierarchical association clustering is performed based on these identifiers and features to obtain K modular permutation task clusters. The task connection order of the K modular permutation task clusters is sorted according to the multiple welding path parameters to obtain K welding task sub-sequences. The K modular permutation task clusters are then sorted by their union module permutation frequency to obtain a cluster sorting result. The K welding task sub-sequences are then used as the initial welding task sequence.

[0063] Furthermore, the system is also used to implement the following functions: Based on the configuration identifiers of the multiple modules, coarse-grained clustering of the multiple tasks to be processed is performed to obtain P coarse clustered task groups; combined with the association features of the multiple modules, fine-grained clustering of the P coarse clustered task groups is performed to obtain the K modular permutation task clusters.

[0064] Furthermore, the system is also used to implement the following functions: Based on the arrangement of the multiple tasks to be processed in the initial welding task sequence, and in conjunction with production requirements, a delivery delay conflict check is performed on the delivery limits of the multiple tasks to obtain an overdue warning marker sequence; incremental conflict detection for mold replacement is performed on the overdue warning marker sequence to locate multiple task nodes to be adjusted; taking the multiple task nodes to be adjusted as the adjustment starting point, under the constraint of minimizing the number of new module replacements, task insertion and rearrangement are performed to obtain the target welding task sequence.

[0065] Furthermore, the system is also used to implement the following functions: By comparing the configuration of the Nth task module and the configuration of the (N-1)th task module, multiple module configuration differences are identified; the positioning and installation requirements of the multiple module configuration differences are matched with the associated features of the Nth module, and local module differential replacement is performed to obtain the welding station adapted to the task.

[0066] Furthermore, the system is also used to implement the following functions: During the execution of the Nth processing task, the production task pool is dynamically monitored for new tasks based on delivery limits, so as to update the production schedule in real time by inserting the minimum mold change increment in the target welding task sequence.

[0067] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0068] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A laser welding adaptive holding control method based on modular replacement, characterized in that, The method includes: The scheduling of multiple pending tasks periodically retrieved from the production task pool is optimized to obtain the target welding task sequence. Based on the multiple welding path parameters of the multiple tasks to be processed, welding adaptation and holding process analysis is performed to obtain multiple sets of module-level holding timing curves, which are then bound and written into the target welding task sequence. The Nth task module configuration and Nth welding path parameters of the Nth task to be processed are sequentially invoked from the target welding task sequence; S1: Compare the configuration of the Nth task module and the configuration of the (N-1)th task module, and control the automated actuator to perform modular quick-change replacement to obtain the task-adapted welding station; S2: After the parts to be welded are clamped at the task-adaptive welding station, the Nth welding path parameters are executed, and the dynamic adaptive holding control based on the Nth group of module-level holding timing curves is used to produce the Nth welded product. Steps S1 to S2 are executed repeatedly to perform multi-task batch laser welding processing until all tasks in the target welding task sequence are completed.

2. The adaptive pressure holding control method for laser welding based on modular replacement as described in claim 1, characterized in that, The method involves optimizing the scheduling of multiple pending tasks periodically retrieved from the production task pool to obtain a target welding task sequence. Welding feature analysis was performed on the multiple tasks to be processed to obtain multiple task module configurations, multiple welding path parameters, and multiple task delivery limits. Based on the configuration of the multiple task modules, the multiple tasks to be processed are clustered and sorted according to their relevance to obtain an initial welding task sequence; The initial welding task sequence is prioritized by introducing the multiple task delivery limits to obtain the target welding task sequence.

3. The adaptive pressure holding control method for laser welding based on modular replacement as described in claim 1, characterized in that, Based on multiple welding path parameters of the multiple tasks to be processed, welding adaptation and holding process analysis is performed to obtain multiple sets of module-level holding timing curves, which are then bound and written into the target welding task sequence. The method includes: Based on the spatial positions of the Q replaceable modules in the Nth module configuration identifier, Q welding thermal feature information is segmented from the Nth welding path parameters within the Q welding execution time windows; The thermal deformation amount is estimated from the Q welding thermal characteristic information to obtain Q thermal deformation interference pressures; Based on the standard welding pressure baseline, the Q thermal deformation interference pressures are restored and compensated to obtain Q initial holding parameters; Using the Q welding execution time windows as the time axis reference, and based on the Q initial pressing parameters, pressing reverse thermal deformation correction is performed to obtain Q module-level pressing timing curves, which constitute the Nth group of module-level pressing timing curves.

4. The adaptive holding control method for laser welding based on modular replacement as described in claim 3, characterized in that, The method involves estimating the thermal deformation of the Q welding thermal characteristic information to obtain Q thermal deformation interference pressures, and includes: Extract the Q material property parameters of the Q parts to be welded corresponding to the Q replaceable modules from the Nth task to be processed; Based on the associated features of the Nth module, the Q parts to be welded are positioned at the boundary, and the Q material property parameters are bound to the Q groups of pressing calculation units. Based on the Q welding thermal characteristic information, the Q sets of pressure calculation units are converted into deformation pressures based on material properties to obtain the Q thermal deformation interference pressures.

5. The adaptive pressure holding control method for laser welding based on modular replacement as described in claim 2, characterized in that, Based on the configuration of the multiple task modules, the multiple tasks to be processed are clustered and sorted according to their relevance to obtain an initial welding task sequence. The method includes: Extract multiple module configuration identifiers and multiple module association features from the configuration parsing of the multiple task modules; Based on the configuration identifiers of the multiple modules and the association features of the multiple modules, hierarchical association clustering is performed to obtain K modular permutation task clusters; Based on the multiple welding path parameters, the task connection and sorting of the K modular replacement task clusters are performed to obtain K welding task sub-sequences; The K modular permutation task clusters are sorted by their frequency of permutation in a union to obtain the cluster sorting result. The K welding task subsequences are then used as the reference to form the initial welding task sequence.

6. The adaptive pressure holding control method for laser welding based on modular replacement as described in claim 5, characterized in that, Based on the multiple module configuration identifiers and multiple module association features, hierarchical association clustering is performed to obtain K modular permutation task clusters. The method includes: Based on the configuration identifiers of the multiple modules, coarse-grained clustering of the multiple tasks to be processed is performed to obtain P coarse clustering task groups; By combining the associated features of the multiple modules, fine-grained clustering is performed on the P coarse clustering task groups to obtain the K modular permutation task clusters.

7. The adaptive pressure holding control method for laser welding based on modular replacement as described in claim 5, characterized in that, The method involves prioritizing the initial welding task sequence by introducing the multiple task delivery limits to obtain the target welding task sequence. Based on the arrangement of the multiple tasks to be processed in the initial welding task sequence, and in conjunction with production requirements, a delivery delay conflict check is performed on the delivery limits of the multiple tasks to obtain an overdue warning mark sequence. The overdue warning marker sequence is subjected to incremental conflict detection to locate multiple task nodes that need to be adjusted; Taking the multiple task nodes to be adjusted as the starting point, and under the constraint of minimizing the number of replacements of newly added modules, the task insertion and rearrangement are performed to obtain the target welding task sequence.

8. The adaptive pressure holding control method for laser welding based on modular replacement as described in claim 4, characterized in that, By comparing the configuration of the Nth task module and the (N-1)th task module, the automated actuator is controlled to perform modular quick-change replacement to obtain a task-adapted welding station. The method includes: By comparing the configuration of the Nth task module with the configuration of the (N-1)th task module, multiple module configuration differences were identified. The positioning and installation requirements of the multiple module configuration differences are matched based on the associated features of the Nth module, and local module differential replacement is performed to obtain the task-adapted welding station.

9. The adaptive pressure holding control method for laser welding based on modular replacement as described in claim 1, characterized in that, During the execution of the Nth processing task, the production task pool is dynamically monitored for new tasks based on delivery limits, so as to update the production schedule in real time by inserting the minimum mold change increment in the target welding task sequence.

10. A modular displacement-based adaptive pressure holding control system for laser welding, characterized in that, The system is used to execute the modular permutation-based adaptive holding control method for laser welding as described in any one of claims 1-9, the system comprising: The production scheduling optimization module is used to optimize the production scheduling of multiple tasks to be processed periodically retrieved from the production task pool to obtain the target welding task sequence. The process analysis module is used to perform welding adaptation and holding process analysis based on multiple welding path parameters of the multiple tasks to be processed, obtain multiple sets of module-level holding time sequence curves, and bind and write them into the target welding task sequence. The calling module is used to sequentially call the Nth task module configuration and the Nth welding path parameters of the Nth task to be processed from the target welding task sequence; The replacement module is used in S1 to compare the configuration of the Nth task module and the configuration of the (N-1)th task module, and control the automated actuator to perform modular quick replacement to obtain a task-adaptive welding station. The holding control module is used in S2: after the workpiece to be welded is clamped at the task-adaptive welding station, it executes the dynamic adaptive holding control based on the Nth group of module-level holding timing curves under the Nth welding path parameters to produce the Nth welded finished product. The loop module is used to repeatedly execute steps S1 to S2 to perform multi-task batch laser welding processing until all tasks in the target welding task sequence are completed.

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