Spring machine forming precision feedforward control method and system thereof

Abstract

This invention provides a feedforward control method and system for spring forming accuracy, belonging to the field of spring machine control technology. The feedforward control method for spring forming accuracy includes the following steps: acquiring abnormal wire feeding data, tension drift data, winding offset data, cutting deviation data, dimensional fluctuation data, and equipment status data to establish an accuracy anomaly monitoring dataset; identifying mild, moderate, and severe deviation states based on the accuracy anomaly monitoring dataset, and calculating the accuracy instability index, cycle time disturbance index, and forming risk index; generating graded feedforward judgment results, parameter over-limit results, anomaly aggregation results, and cycle time reset requirement results based on the accuracy instability index, cycle time disturbance index, and forming risk index; and determining the parameter fine-tuning amount corresponding to the mild deviation state, the linkage compensation amount corresponding to the moderate deviation state, and the cycle time reset amount corresponding to the severe deviation state based on the graded feedforward judgment results.

Description

Technical Field

[0001] This invention belongs to the field of spring machine control technology, and in particular to a spring machine forming accuracy feedforward control method and system. Background Technology

[0002] When continuously processing compression springs, tension springs, and irregularly shaped springs, spring machines need to maintain stable forming dimensions under coordinated conditions of wire feeding, winding, cutting, and cycle time.

[0003] In existing technologies, common methods often rely on post-forming inspection results to correct equipment parameters. This involves manual intervention or fixed rule corrections after dimensional fluctuations, abnormal wire feeding, winding offsets, or cutting deviations occur. While this approach can reduce the risk of batch deviations to some extent, tension drift, equipment status fluctuations, and cycle disturbances often occur suddenly and in clusters under high-speed continuous forming conditions. If post-forming corrections are still relied upon, the abnormalities can easily spread to subsequent forming cycles. Especially when different levels of deviation coexist, the lack of differentiated feedforward control strategies that match different deviation states leads to parameter lag and reduced yield. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in existing technologies by proposing a feedforward control method and system for spring forming accuracy.

[0005] The objective of this invention can be achieved through the following technical solutions: A method for feedforward control of spring forming accuracy includes the following steps: S1, acquires abnormal wire feeding data, tension drift data, winding offset data, cutting deviation data, size fluctuation data and equipment status data, and establishes an accuracy anomaly monitoring dataset; S2, based on the accuracy anomaly monitoring dataset, identify mild deviation, moderate deviation and severe deviation states, and calculate the accuracy instability index, cycle time disturbance index and forming risk index; S3 generates graded feedforward judgment results, parameter over-limit results, abnormal clustering results, and cycle time reset requirement results based on the accuracy instability index, cycle time disturbance index, and forming risk index. S4. Based on the graded feedforward judgment results, determine the parameter fine-tuning amount corresponding to the mild deviation state, the linkage compensation amount corresponding to the moderate deviation state, and the cycle reset amount corresponding to the severe deviation state. S5 generates differentiated feedforward control strategies based on parameter fine-tuning, linkage compensation, and cycle reset, and establishes calling rules corresponding to mild, moderate, and severe deviation states. S6 executes preset control before the next forming cycle according to the differentiated feedforward control strategy, and outputs the abnormal classification result, accuracy optimization result and subsequent state switching result.

[0006] Furthermore, step S1 includes the following steps: The abnormal feed data, tension drift data, winding offset data, cutting deviation data, dimensional fluctuation data, and equipment status data are divided into cycles and time-marked according to the forming cycle. Based on the acquisition time corresponding to each forming cycle, the abnormal data of wire feeding, tension drift data, winding offset data, cutting deviation data, dimensional fluctuation data and equipment status data are time-series aligned and workstation bound; Extract abnormal amplitude, fluctuation frequency, duration and associated working condition identifiers from the time-aligned abnormal wire feeding data, tension drift data, winding offset data, cutting deviation data, size fluctuation data and equipment status data; By uniformly associating abnormal amplitude, fluctuation frequency, duration, and related operating condition identifiers, a precision anomaly monitoring dataset is established.

[0007] Furthermore, step S2 includes the following steps: The degree of accuracy deviation in the current forming cycle is identified by the deviation relationship between the wire feeding anomaly data, winding offset data and dimensional fluctuation data in the accuracy anomaly monitoring dataset. Identify the degree of cycle disturbance corresponding to the current forming cycle based on the disturbance relationship between tension drift data, cutting deviation data, and equipment status data; The accuracy instability index, the cycle time disturbance index, and the forming risk index are calculated based on the degree of accuracy deviation and the degree of cycle time disturbance, respectively. The slight deviation, moderate deviation, and severe deviation states are identified based on the correspondence between the accuracy instability index, the cycle disturbance index, the forming risk index, and the preset level range.

[0008] Furthermore, the calculation of the accuracy instability index, cycle time disturbance index, and forming risk index includes the following steps: The abnormal contribution value of the feed line side is determined based on the abnormal feed line data and the dimensional fluctuation data. The abnormal contribution value on the winding side is determined based on tension drift data and winding offset data; The abnormal contribution values ​​on the cutting side and the abnormal contribution values ​​on the cycle time side are determined based on the cutting deviation data and equipment status data. The abnormal contribution values ​​from the wire feeding side, winding side, cutting side, and cycle time side are fused together to generate the accuracy instability index and the cycle time disturbance index. A forming risk index is generated based on the continuous changes in the precision instability index, the cycle time disturbance index, and the dimensional fluctuation data.

[0009] Furthermore, step S3 includes the following steps: Based on the combined relationship between the precision instability index, the cycle time disturbance index, and the forming risk index, a graded feedforward judgment result corresponding to the current forming cycle is generated. The parameter over-limit results are generated based on the comparison between the accuracy instability index and the preset parameter boundary, and the abnormal aggregation results are generated based on the aggregation distribution of the cycle disturbance index in the continuous forming cycle. The cycle time reset requirement result is generated based on the coupling relationship between the forming risk index, parameter over-limit results, and abnormal aggregation results.

[0010] Furthermore, step S4 includes the following steps: When the graded feedforward judgment result is a slight deviation state, the parameter fine-tuning amount corresponding to the slight deviation state is determined based on the accuracy instability index and parameter over-limit result; When the graded feedforward judgment result is a moderate deviation state, the linkage compensation amount corresponding to the moderate deviation state is determined based on the accuracy instability index, the cycle disturbance index, and the abnormal aggregation result. When the graded feedforward judgment result is a severe deviation state, the cycle time reset amount corresponding to the severe deviation state is determined based on the forming risk index and cycle time reset requirement result. Boundary and priority checks are performed on parameter fine-tuning amounts, linkage compensation amounts, and cycle reset amounts; The parameter fine-tuning amount, linkage compensation amount, and cycle reset amount after boundary verification and priority verification are bound to the mild deviation state, moderate deviation state, and severe deviation state, respectively.

[0011] Furthermore, determining the beat reset amount includes the following steps: The basic reset range of the cycle time reset is determined based on the forming risk index corresponding to the severe deviation state. Determine the additional correction range for the cycle reset amount based on the cycle reset requirement results and equipment status data; The basic reset amplitude and the additional correction amplitude are superimposed to generate the beat reset amount corresponding to the severe deviation state.

[0012] Furthermore, step S5 includes the following steps: The parameter fine-tuning amount, linkage compensation amount, and cycle reset amount are respectively mapped to wire feeding control, winding control, cutting control, and cycle control to generate differentiated feedforward control strategies; Establish calling rules based on the execution priority, calling boundary, and exit boundary corresponding to the mild deviation state, moderate deviation state, and severe deviation state, respectively; The differentiated feedforward control strategy is uniformly associated with the invocation rules to generate a feedforward control invocation set; Write the set of feedforward control calls into the preset control call table for the next forming cycle.

[0013] Furthermore, step S6 includes the following steps: Based on the differentiated feedforward control strategy, the preset control is executed before the next forming cycle, and the abnormal data of wire feeding, tension drift data, winding offset data, cutting deviation data, size fluctuation data and equipment status data after the preset control is called are recorded. Based on the changes in the accuracy anomaly monitoring dataset before and after the preset control call, anomaly classification results and accuracy optimization results are generated; The subsequent state switching results are generated based on the changing trends among the anomaly classification results, parameter over-limit results, anomaly aggregation results, and cycle time reset requirement results. The anomaly classification results, accuracy optimization results, and subsequent state switching results are bound to the current forming cycle; The results of the bound anomaly classification, precision optimization, and subsequent state switching will be used as the basis for subsequent preset control calls.

[0014] A spring forming accuracy feedforward control system includes: The data acquisition module is used to acquire data on abnormal wire feeding, tension drift, winding offset, cutting deviation, dimensional fluctuation, and equipment status, and to establish a dataset for monitoring accuracy anomalies.

[0015] The calculation module is used to identify mild, moderate and severe deviation states based on the accuracy anomaly monitoring dataset, and to calculate the accuracy instability index, cycle time disturbance index and forming risk index.

[0016] The feedforward judgment module is used to generate graded feedforward judgment results, parameter over-limit results, abnormal clustering results, and cycle time reset requirement results based on the accuracy instability index, cycle time disturbance index, and forming risk index.

[0017] The control judgment module is used to determine the parameter fine-tuning amount corresponding to the mild deviation state, the linkage compensation amount corresponding to the moderate deviation state, and the cycle reset amount corresponding to the severe deviation state based on the graded feedforward judgment results.

[0018] The module is used to generate differentiated feedforward control strategies based on parameter fine-tuning, linkage compensation, and cycle reset, and to establish calling rules corresponding to mild, moderate, and severe deviation states.

[0019] The results output module is used to execute preset control before the next forming cycle according to the differentiated feedforward control strategy, and output the abnormal classification results, accuracy optimization results and subsequent state switching results.

[0020] Compared with the prior art, this application has the following advantages: This invention first acquires data on abnormal wire feeding, tension drift, winding offset, cutting deviation, dimensional fluctuation, and equipment status to establish a precision anomaly monitoring dataset. Then, based on the precision anomaly monitoring dataset, it identifies mild, moderate, and severe deviation states and calculates the precision instability index, cycle time disturbance index, and forming risk index. Therefore, it can transform scattered abnormal signals into a unified feedforward judgment criterion, which is more complete than the method of correcting based on a single detection value.

[0021] This invention further generates graded feedforward judgment results, parameter over-limit results, anomaly aggregation results, and cycle reset requirement results based on the accuracy instability index, cycle time disturbance index, and forming risk index, and determines the parameter fine-tuning amount, linkage compensation amount, and cycle time reset amount respectively. Therefore, it can adopt differentiated feedforward control strategies for different deviation states, instead of using the same compensation mode to handle all abnormal situations, thereby improving the control targeting.

[0022] This invention also unifies the differentiated feedforward control strategy with the calling rules, executes preset control before the next forming cycle, and outputs abnormal classification results, accuracy optimization results and subsequent state switching results. Therefore, it can not only suppress the current abnormal trend in advance, but also form a continuous calling basis for subsequent forming cycles, thereby enhancing the accuracy stability and state switching capabilities in continuous production. Attached Figure Description

[0023] Figure 1 This is a schematic diagram of the overall process of a spring forming accuracy feedforward control method in an embodiment of the present invention; Figure 2 This is a schematic diagram of the process for establishing an accuracy anomaly monitoring dataset in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the logical relationship between identifying mild deviation, moderate deviation, and severe deviation states and calculating the accuracy instability index, cycle time disturbance index, and forming risk index in embodiments of the present invention. Figure 4 This is a schematic diagram illustrating the generation of hierarchical feedforward judgment results, parameter over-limit results, abnormal aggregation results, and cycle reset requirement results in an embodiment of the present invention. Figure 5 This is a schematic diagram of the process for generating differentiated feedforward control strategies and invocation rules in an embodiment of the present invention; Figure 6 This is a schematic diagram illustrating the output of anomaly classification results, accuracy optimization results, and subsequent state switching results in an embodiment of the present invention; Figure 7 This is a schematic diagram of the overall module of a spring forming accuracy feedforward control method in an embodiment of the present invention. Detailed Implementation

[0024] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0025] like Figures 1 to 7 As shown, To make the objectives, technical solutions, and beneficial effects of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings. In some embodiments, the continuous production of compression springs on the same CNC spring machine is taken as a unified implementation scenario. The spring machine includes a wire feeding execution unit, a winding execution unit, a cutting execution unit, and a cycle control unit. The system continuously collects wire feeding abnormality data, tension drift data, winding offset data, cutting deviation data, dimensional fluctuation data, and equipment status data in each forming cycle, and performs preset control on the next forming cycle after the current forming cycle ends. The following steps and embodiments of each dependent claim all revolve around the same equipment, the same batch, and the continuous forming cycle link.

[0026] Reference Figure 1 A method for feedforward control of spring forming accuracy includes the following steps: S1 acquires data on abnormal wire feeding, tension drift, winding offset, cutting deviation, dimensional fluctuation, and equipment status to establish a precision anomaly monitoring dataset.

[0027] Feeding anomaly data is used to characterize deviations during the feeding process; tension drift data is used to characterize changes in the material's stress state; winding offset data is used to characterize deviations of the winding action from the set trajectory; cutting deviation data is used to characterize differences in the position or time of the cutting action; dimensional fluctuation data is used to characterize changes in the forming size during continuous forming cycles; and equipment status data is used to characterize the current operating status of the equipment.

[0028] The process of this step involves incorporating the aforementioned multi-source data into the same monitoring framework. Since different data sources correspond to different workstations and different acquisition cycles, it is difficult to accurately reflect the abnormal state within the same forming cycle if they are not organized in a unified manner first. Therefore, the focus of this step is not to judge any single abnormality, but to establish a high-precision abnormality monitoring dataset that can reflect the overall abnormal state of the current forming cycle.

[0029] The accuracy anomaly monitoring dataset retains the same-cycle correspondence between wire feeding anomaly data, tension drift data, winding offset data, cutting deviation data, dimensional fluctuation data, and equipment status data, and can be used to describe the basis of accuracy anomaly status in the current forming cycle.

[0030] In this embodiment, when the spring machine continuously produces compression springs, the system synchronously collects the following data within a certain forming cycle: abnormal wire feeding data corresponding to the feeding station, tension drift data corresponding to the tension monitoring unit, winding offset data corresponding to the winding station, cutting deviation data corresponding to the cutting station, dimensional fluctuation data corresponding to the online detection unit, and equipment status data corresponding to the whole machine controller. Subsequently, the system categorizes these data into the monitoring records of the same forming cycle to form the accuracy anomaly monitoring dataset corresponding to that forming cycle.

[0031] Reference Figure 2 Specifically, S1 includes the following steps: The abnormal feed data, tension drift data, winding offset data, cutting deviation data, dimensional fluctuation data, and equipment status data are divided into cycles and time-marked according to the forming cycle. Based on the acquisition time corresponding to each forming cycle, the abnormal data of wire feeding, tension drift data, winding offset data, cutting deviation data, dimensional fluctuation data and equipment status data are time-series aligned and workstation bound; Extract abnormal amplitude, fluctuation frequency, duration and associated working condition identifiers from the time-aligned abnormal wire feeding data, tension drift data, winding offset data, cutting deviation data, size fluctuation data and equipment status data; By uniformly associating abnormal amplitude, fluctuation frequency, duration, and related operating condition identifiers, a precision anomaly monitoring dataset is established.

[0032] By dividing the data into cycles and using time stamping, it is possible to ensure that data from different sources correspond at the forming cycle level. By aligning the time sequence and binding the workstations, it is possible to ensure that data on abnormal wire feeding, tension drift, winding offset, cutting deviation, dimensional fluctuation, and equipment status are consistent at the same acquisition time. By extracting abnormal amplitude, fluctuation frequency, duration, and associated working condition identifiers, the original abnormal signals can be further transformed into monitoring features that can be used for analysis. Finally, a precision anomaly monitoring dataset is established through unified association. For example, the system first divides the data on abnormal wire feeding, tension drift, winding offset, cutting deviation, dimensional fluctuation, and equipment status into cycles and uses time stamping according to the forming cycle. Then, it completes time sequence alignment and workstation binding based on the acquisition time of each forming cycle. After that, the system extracts abnormal amplitude, fluctuation frequency, duration, and associated working condition identifiers from the aligned data and associates these features to form a more complete precision anomaly monitoring dataset.

[0033] S2 identifies mild, moderate, and severe deviation states based on the accuracy anomaly monitoring dataset, and calculates the accuracy instability index, cycle time disturbance index, and forming risk index.

[0034] This step, based on the accuracy anomaly monitoring dataset, identifies the degree of deviation and disturbance corresponding to the current forming cycle, and calculates the accuracy instability index, cycle time disturbance index, and forming risk index accordingly.

[0035] Reference Figure 3 Specifically, S2 includes the following steps: The degree of accuracy deviation in the current forming cycle is identified by the deviation relationship between the wire feeding anomaly data, winding offset data and dimensional fluctuation data in the accuracy anomaly monitoring dataset. Identify the degree of cycle disturbance corresponding to the current forming cycle based on the disturbance relationship between tension drift data, cutting deviation data, and equipment status data; The accuracy instability index, the cycle time disturbance index, and the forming risk index are calculated based on the degree of accuracy deviation and the degree of cycle time disturbance, respectively. The slight deviation, moderate deviation, and severe deviation states are identified based on the correspondence between the accuracy instability index, the cycle disturbance index, the forming risk index, and the preset level range.

[0036] The system identifies the degree of accuracy deviation corresponding to the current forming cycle based on the deviation relationship between the wire feeding anomaly data, winding offset data, and dimensional fluctuation data in the accuracy anomaly monitoring dataset. This degree of accuracy deviation is used to characterize the anomaly level of the current forming cycle in terms of forming dimensional stability. Then, the system identifies the degree of cycle time disturbance corresponding to the current forming cycle based on the disturbance relationship between tension drift data, cutting deviation data, and equipment status data. This degree of cycle time disturbance is used to characterize the disturbance level of the current forming cycle in terms of cycle time execution and mechanism coordination.

[0037] After obtaining the accuracy deviation level and cycle time disturbance level, the system further calculates the accuracy instability index, cycle time disturbance index, and forming risk index. The accuracy instability index is used to reflect the accuracy instability level of the current forming cycle, the cycle time disturbance index is used to reflect the cycle time abnormality level of the current forming cycle, and the forming risk index is used to reflect the forming risk level under the comprehensive abnormal state of the current forming cycle. Subsequently, the system identifies the mild deviation state, moderate deviation state, and severe deviation state based on the correspondence between the accuracy instability index, cycle time disturbance index, and forming risk index and the preset level range.

[0038] The identification of accuracy deviation mainly relies on the deviation relationship between abnormal wire feeding data, winding offset data, and dimensional fluctuation data, while the identification of cycle time disturbance mainly relies on the disturbance relationship between tension drift data, cutting deviation data, and equipment status data.

[0039] The precision instability index, cycle time disturbance index, and forming risk index are derived from the two levels mentioned above, and are ultimately mapped to preset level ranges to identify mild, moderate, and severe deviation states. This process provides a clear input path and grading basis for deviation state identification. For example, the system identifies the degree of precision deviation based on the deviation relationship between abnormal wire feeding data, winding offset data, and dimensional fluctuation data within the current forming cycle; then, it identifies the degree of cycle time disturbance based on the disturbance relationship between tension drift data, cutting deviation data, and equipment status data; subsequently, it calculates the precision instability index, cycle time disturbance index, and forming risk index, and identifies the cycle as a moderate deviation state based on the preset level range.

[0040] For a specific example, in the aforementioned precision anomaly monitoring dataset, the system detected that both the wire feeding anomaly data and the winding offset data increased simultaneously, and the dimensional fluctuation data deviated at multiple consecutive sampling points, thus identifying that the precision deviation of the current forming cycle was at a high level. At the same time, the tension drift data and the cutting deviation data increased synchronously, and the equipment status data indicated a decrease in cycle execution stability. Therefore, the system identified that the cycle disturbance of this forming cycle was at a medium-high level. Based on this, the system calculated the precision instability index, the cycle disturbance index, and the forming risk index, and identified the forming cycle as a medium deviation state according to the preset level range.

[0041] Furthermore, the calculation of the accuracy instability index, cycle time disturbance index, and forming risk index includes the following steps: The abnormal contribution value of the feed line side is determined based on the abnormal feed line data and the dimensional fluctuation data. The abnormal contribution value on the winding side is determined based on tension drift data and winding offset data; The abnormal contribution values ​​on the cutting side and the abnormal contribution values ​​on the cycle time side are determined based on the cutting deviation data and equipment status data. The abnormal contribution values ​​from the wire feeding side, winding side, cutting side, and cycle time side are fused together to generate the accuracy instability index and the cycle time disturbance index. A forming risk index is generated based on the continuous changes in the precision instability index, the cycle time disturbance index, and the dimensional fluctuation data.

[0042] The abnormal contribution values ​​from the wire feeding side, winding side, cutting side, and cycle time side originate from abnormal combinations in different directions. By fusing the abnormal contribution values ​​from multiple sides, a precision instability index and a cycle time disturbance index can be generated respectively. Then, based on the continuous change relationship between the precision instability index, the cycle time disturbance index, and the dimensional fluctuation data, a forming risk index is generated, giving the forming risk index a clearer basis for abnormal accumulation.

[0043] For example, the system first determines the abnormal contribution value on the feeding side based on abnormal feeding data and dimensional fluctuation data, then determines the abnormal contribution value on the winding side based on tension drift data and winding offset data, and determines the abnormal contribution value on the cutting side and the abnormal contribution value on the cycle time side based on cutting deviation data and equipment status data. After that, the system merges these contribution values ​​to generate the accuracy instability index and the cycle time disturbance index, and then generates the forming risk index by combining the continuous change relationship of dimensional fluctuation data.

[0044] S3 generates graded feedforward judgment results, parameter over-limit results, abnormal clustering results, and cycle time reset requirement results based on the accuracy instability index, cycle time disturbance index, and forming risk index.

[0045] The key point of this step is to generate a judgment result for the next forming cycle based on the combination relationship between the three types of indices.

[0046] Specifically, S3 includes the following steps: Based on the combined relationship between the precision instability index, the cycle time disturbance index, and the forming risk index, a graded feedforward judgment result corresponding to the current forming cycle is generated. The parameter over-limit results are generated based on the comparison between the accuracy instability index and the preset parameter boundary, and the abnormal aggregation results are generated based on the aggregation distribution of the cycle disturbance index in the continuous forming cycle. The cycle time reset requirement result is generated based on the coupling relationship between the forming risk index, parameter over-limit results, and abnormal aggregation results.

[0047] The system first generates a tiered feedforward judgment result corresponding to the current forming cycle based on the combined relationship between the precision instability index, the cycle time disturbance index, and the forming risk index. The tiered feedforward judgment result is used to characterize which feedforward level should be used for preset control in the current forming cycle. Then, the system generates parameter over-limit results based on the comparison relationship between the precision instability index and the preset parameter boundary, and generates anomaly clustering results based on the clustering distribution of the cycle time disturbance index in continuous forming cycles. The parameter over-limit results are used to characterize whether the current anomaly level has exceeded the normal parameter call boundary, and the anomaly clustering results are used to characterize whether the anomaly has formed a clustering trend in multiple continuous forming cycles.

[0048] Specifically, the graded feedforward judgment results come from the combination relationship of three types of indices; the parameter over-limit results come from the comparison relationship between the accuracy instability index and the preset parameter boundary; the anomaly clustering results come from the clustering distribution of the cycle disturbance index in the continuous forming cycle; and the cycle reset requirement results come from the coupled analysis of the forming risk index, parameter over-limit results, and anomaly clustering results.

[0049] After obtaining the graded feedforward judgment results, parameter over-limit results, and abnormal aggregation results, the system generates cycle time reset requirement results based on the coupling relationship between the forming risk index, parameter over-limit results, and abnormal aggregation results. The cycle time reset requirement results are used to characterize whether the cycle time control needs to be reset before the next forming cycle in the current forming cycle.

[0050] For example, in the forming cycle corresponding to the aforementioned moderate deviation state, the system generates a medium-level graded feedforward judgment result based on the combination relationship of the precision instability index, the cycle time disturbance index, and the forming risk index. Subsequently, the system compares the precision instability index with the preset parameter boundary and finds that some control directions are close to the upper limit, thus generating a parameter over-limit result. Then, based on the distribution of the cycle time disturbance index in the last five forming cycles, it finds that anomalies are continuously clustered, thus generating an anomaly clustering result. Finally, the system combines the forming risk index, the parameter over-limit result, and the anomaly clustering result to determine that cycle time reset is not required for the time being, and only retains the cycle time reset requirement result as a pending trigger state.

[0051] S4. Based on the graded feedforward judgment results, determine the parameter fine-tuning amount corresponding to the mild deviation state, the linkage compensation amount corresponding to the moderate deviation state, and the cycle reset amount corresponding to the severe deviation state.

[0052] The key point of this step is to map different deviation states to different levels of control variables based on the results of the hierarchical feedforward judgment.

[0053] Reference Figure 4 Specifically, S4 includes the following steps: When the graded feedforward judgment result is a slight deviation state, the parameter fine-tuning amount corresponding to the slight deviation state is determined based on the accuracy instability index and parameter over-limit result; When the graded feedforward judgment result is a moderate deviation state, the linkage compensation amount corresponding to the moderate deviation state is determined based on the accuracy instability index, the cycle disturbance index, and the abnormal aggregation result. When the graded feedforward judgment result is a severe deviation state, the cycle time reset amount corresponding to the severe deviation state is determined based on the forming risk index and cycle time reset requirement result. Boundary and priority checks are performed on parameter fine-tuning amounts, linkage compensation amounts, and cycle reset amounts; The parameter fine-tuning amount, linkage compensation amount, and cycle reset amount after boundary verification and priority verification are bound to the mild deviation state, moderate deviation state, and severe deviation state, respectively.

[0054] Furthermore, determining the beat reset amount includes the following steps: The basic reset range of the cycle time reset is determined based on the forming risk index corresponding to the severe deviation state. Determine the additional correction range for the cycle reset amount based on the cycle reset requirement results and equipment status data; The basic reset amplitude and the additional correction amplitude are superimposed to generate the beat reset amount corresponding to the severe deviation state.

[0055] When the hierarchical feedforward judgment result is a slight deviation state, the system determines the parameter fine-tuning amount corresponding to the slight deviation state based on the accuracy instability index and parameter over-limit results; the parameter fine-tuning amount is used to make small adjustments to local anomalies. When the hierarchical feedforward judgment result is a moderate deviation state, the system determines the linkage compensation amount corresponding to the moderate deviation state based on the accuracy instability index, cycle time disturbance index, and anomaly aggregation results; the linkage compensation amount is used to perform coordinated compensation for multiple related control directions. When the hierarchical feedforward judgment result is a severe deviation state, the system determines the linkage compensation amount based on the forming risk index and cycle time reset requirement results. The cycle time reset amount corresponds to the severe deviation state; the cycle time reset amount is used to perform reset intervention on the forming cycle time execution; then, the system performs boundary verification and priority verification on the parameter fine-tuning amount, linkage compensation amount and cycle time reset amount, and binds the verified parameter fine-tuning amount, linkage compensation amount and cycle time reset amount to the mild deviation state, moderate deviation state and severe deviation state respectively; for example, after verification, the system binds the parameter fine-tuning amount to the mild deviation state, binds the linkage compensation amount to the moderate deviation state, and binds the cycle time reset amount to the severe deviation state to form a callable state control relationship.

[0056] Slight deviation states correspond to parameter fine-tuning amounts, moderate deviation states correspond to linkage compensation amounts, and severe deviation states correspond to cycle reset amounts; boundary checks and priority checks are used to ensure that the three types of control quantities do not exceed the allowable range and satisfy the execution sequence relationship when used; finally, a clear mapping from deviation states to control quantities can be formed through binding.

[0057] The basic reset range is determined by the forming risk index, while the additional correction range is jointly determined by the cycle time reset requirement and equipment status data. By superimposing the basic reset range and the additional correction range, the cycle time reset amount corresponding to the severe deviation state can simultaneously possess risk correspondence and equipment adaptability. For example, when the system identifies that the current forming cycle has entered a severe deviation state, it first determines the basic reset range of the cycle time reset amount based on the forming risk index, then determines the additional correction range by combining the cycle time reset requirement and equipment status data, and finally superimposes the two to form the cycle time reset amount, which is used for subsequent cycle time control.

[0058] For example, in this embodiment, the system determines the linkage compensation amount to be invoked in the current forming cycle based on the intermediate-level graded feedforward judgment result generated by S3 and the current abnormal aggregation result. Subsequently, the system calculates the linkage compensation amount based on the accuracy instability index, the cycle disturbance index and the abnormal aggregation result, and performs boundary verification and priority verification on it. After the verification is completed, the system binds the linkage compensation amount to the intermediate deviation state for use in the next step to generate a differentiated feedforward control strategy.

[0059] S5 generates differentiated feedforward control strategies based on parameter fine-tuning, linkage compensation, and cycle reset, and establishes calling rules corresponding to mild, moderate, and severe deviation states.

[0060] The process in this step involves mapping the three types of control variables to different control directions and different calling conditions, thereby generating a differentiated feedforward control strategy that can be executed before the next forming cycle.

[0061] Reference Figure 5 Specifically, S5 includes the following steps: The parameter fine-tuning amount, linkage compensation amount, and cycle reset amount are respectively mapped to wire feeding control, winding control, cutting control, and cycle control to generate differentiated feedforward control strategies; Establish calling rules based on the execution priority, calling boundary, and exit boundary corresponding to the mild deviation state, moderate deviation state, and severe deviation state, respectively; The differentiated feedforward control strategy is uniformly associated with the invocation rules to generate a feedforward control invocation set; Write the set of feedforward control calls into the preset control call table for the next forming cycle.

[0062] The system first maps the parameter fine-tuning amount, linkage compensation amount, and cycle reset amount to the wire feeding control, winding control, cutting control, and cycle control, respectively, to generate differentiated feedforward control strategies. Then, the system establishes calling rules based on the execution priority, calling boundary, and exit boundary corresponding to the mild deviation state, moderate deviation state, and severe deviation state, respectively. The calling rules are used to explain when the feedforward strategy enters and exits under different deviation states, and which control direction is executed first.

[0063] Furthermore, the system further associates the differentiated feedforward control strategy with the calling rules to generate a feedforward control call set, and writes the feedforward control call set into the preset control call table for the next forming cycle; in this way, the system can complete the feedforward control preparation according to the preset control call table before the start of the next forming cycle.

[0064] For example, in this embodiment, the system maps the linkage compensation amount corresponding to the moderate deviation state to the wire feeding control, winding control, and cutting control respectively, and retains the cycle control as the observation state, generating the corresponding differentiated feedforward control strategy; then, the system establishes the execution priority, call boundary, and exit boundary for the moderate deviation state, forming a call rule, and uniformly associates the differentiated feedforward control strategy with the call rule to generate a feedforward control call set; finally, the system writes the feedforward control call set into the preset control call table of the next forming cycle.

[0065] S6 executes preset control before the next forming cycle according to the differentiated feedforward control strategy, and outputs the abnormal classification result, accuracy optimization result and subsequent state switching result.

[0066] The process of this step involves executing preset control before the next forming cycle and comparing and analyzing the state before and after execution, thereby outputting the abnormal classification result, the accuracy optimization result, and the subsequent state switching result.

[0067] Reference Figure 6 Specifically, step S6 includes the following steps: Based on the differentiated feedforward control strategy, the preset control is executed before the next forming cycle, and the abnormal data of wire feeding, tension drift data, winding offset data, cutting deviation data, size fluctuation data and equipment status data after the preset control is called are recorded. Based on the changes in the accuracy anomaly monitoring dataset before and after the preset control call, anomaly classification results and accuracy optimization results are generated; The subsequent state switching results are generated based on the changing trends among the anomaly classification results, parameter over-limit results, anomaly aggregation results, and cycle time reset requirement results. The anomaly classification results, accuracy optimization results, and subsequent state switching results are bound to the current forming cycle; The results of the bound anomaly classification, precision optimization, and subsequent state switching will be used as the basis for subsequent preset control calls.

[0068] The system executes preset control before the next forming cycle based on a differentiated feedforward control strategy, and records the abnormal wire feeding data, tension drift data, winding offset data, cutting deviation data, dimensional fluctuation data, and equipment status data after the preset control is invoked. Then, based on the changes in the accuracy anomaly monitoring dataset before and after the preset control is invoked, it generates anomaly classification results and accuracy optimization results. The anomaly classification results are used to characterize whether the current deviation state has improved, and the accuracy optimization results are used to characterize whether the current feedforward control has effectively reduced accuracy anomalies.

[0069] The abnormal data and status data after the preset control call are compared with the accuracy anomaly monitoring dataset before the call to generate anomaly classification results and accuracy optimization results. Then, based on the changing trends between the anomaly classification results, parameter over-limit results, anomaly aggregation results and cycle reset requirement results, subsequent state switching results are generated. Finally, the three types of results after binding are used for subsequent preset control calls, thus forming a continuous call closed loop.

[0070] Furthermore, the system generates subsequent state switching results based on the changing trends among the anomaly classification results, parameter over-limit results, anomaly aggregation results, and cycle time reset requirement results, and binds the anomaly classification results, accuracy optimization results, and subsequent state switching results with the current forming cycle; finally, the bound results are used as the basis for subsequent preset control calls.

[0071] Before the next forming cycle, the system completes the preset control writing based on the feedforward control call set, and after forming, it re-collects data on abnormal wire feeding, tension drift, winding offset, cutting deviation, dimensional fluctuation, and equipment status. By comparing the accuracy anomaly monitoring datasets of the two forming cycles, the system finds that the degree of accuracy instability has decreased, and the anomaly has decreased from a moderate deviation state to a slight deviation state. Therefore, it generates anomaly classification results and accuracy optimization results. Subsequently, the system combines the changing trends of parameter over-limit results, anomaly aggregation results, and cycle time reset requirement results to generate subsequent state switching results, and binds the above results to the current forming cycle as the basis for the next round of preset control calls.

[0072] In summary, in this embodiment, the present invention, focusing on the same spring machine, the same type of compression spring, and a continuous forming cycle scenario, sequentially completes the following steps: establishing a precision anomaly monitoring dataset; identifying mild, moderate, and severe deviation states; calculating the precision instability index, cycle time disturbance index, and forming risk index; generating graded feedforward judgment results, parameter over-limit results, anomaly aggregation results, and cycle time reset requirements; determining parameter fine-tuning amounts, linkage compensation amounts, and cycle time reset amounts; establishing differentiated feedforward control strategies and calling rules; executing preset control; and outputting anomaly grading results, precision optimization results, and subsequent state switching results. This forms a complete implementation chain for a spring machine forming precision feedforward control method, encompassing anomaly monitoring, graded identification, feedforward judgment, differentiated control, execution evaluation, and subsequent switching.

[0073] Reference Figure 7 A spring forming accuracy feedforward control system, comprising: The data acquisition module is used to acquire data on abnormal wire feeding, tension drift, winding offset, cutting deviation, dimensional fluctuation, and equipment status, and to establish a dataset for monitoring accuracy anomalies.

[0074] The calculation module is used to identify mild, moderate and severe deviation states based on the accuracy anomaly monitoring dataset, and to calculate the accuracy instability index, cycle time disturbance index and forming risk index.

[0075] The feedforward judgment module is used to generate graded feedforward judgment results, parameter over-limit results, abnormal clustering results, and cycle time reset requirement results based on the accuracy instability index, cycle time disturbance index, and forming risk index.

[0076] The control judgment module is used to determine the parameter fine-tuning amount corresponding to the mild deviation state, the linkage compensation amount corresponding to the moderate deviation state, and the cycle reset amount corresponding to the severe deviation state based on the graded feedforward judgment results.

[0077] The module is used to generate differentiated feedforward control strategies based on parameter fine-tuning, linkage compensation, and cycle reset, and to establish calling rules corresponding to mild, moderate, and severe deviation states.

[0078] The results output module is used to execute preset control before the next forming cycle according to the differentiated feedforward control strategy, and output the abnormal classification results, accuracy optimization results and subsequent state switching results.

[0079] It should be noted that all directional indications in the embodiments of the present invention, such as up, down, left, right, front, back, etc., are only used to explain the relative positional relationship and movement of the components in a specific posture, as shown in the attached figure. If the specific posture changes, the directional indication will also change accordingly.

[0080] Furthermore, the use of terms such as "first" and "second" in this invention is for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Meanwhile, the word "and / or" throughout the text means including three solutions; for example, "A and / or B" includes solution A, solution B, or a solution that simultaneously satisfies A and B. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0081] All of the above components are general standard parts or components known to those skilled in the art. Their structure and principles can be learned by those skilled in the art through technical manuals or conventional experimental methods.

[0082] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0083] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0084] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0085] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0086] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0087] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0088] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A spring machine forming accuracy feedforward control method, characterized by, Includes the following steps: S1, acquires abnormal wire feeding data, tension drift data, winding offset data, cutting deviation data, size fluctuation data and equipment status data, and establishes an accuracy anomaly monitoring dataset; S2, based on the accuracy anomaly monitoring dataset, identify mild deviation, moderate deviation and severe deviation states, and calculate the accuracy instability index, cycle time disturbance index and forming risk index; S3 generates graded feedforward judgment results, parameter over-limit results, abnormal clustering results, and cycle time reset requirement results based on the accuracy instability index, cycle time disturbance index, and forming risk index. S4. Based on the graded feedforward judgment results, determine the parameter fine-tuning amount corresponding to the mild deviation state, the linkage compensation amount corresponding to the moderate deviation state, and the cycle reset amount corresponding to the severe deviation state. S5 generates differentiated feedforward control strategies based on parameter fine-tuning, linkage compensation, and cycle reset, and establishes calling rules corresponding to mild, moderate, and severe deviation states. S6 executes preset control before the next forming cycle according to the differentiated feedforward control strategy, and outputs the abnormal classification result, accuracy optimization result and subsequent state switching result.

2. The spring machine forming accuracy feedforward control method according to claim 1, characterized in that, S1 includes the following steps: The abnormal feed data, tension drift data, winding offset data, cutting deviation data, dimensional fluctuation data, and equipment status data are divided into cycles and time-marked according to the forming cycle. Based on the acquisition time corresponding to each forming cycle, the abnormal data of wire feeding, tension drift data, winding offset data, cutting deviation data, dimensional fluctuation data and equipment status data are time-series aligned and workstation bound; Extract abnormal amplitude, fluctuation frequency, duration and associated working condition identifiers from the time-aligned abnormal wire feeding data, tension drift data, winding offset data, cutting deviation data, size fluctuation data and equipment status data; By uniformly associating abnormal amplitude, fluctuation frequency, duration, and related operating condition identifiers, a precision anomaly monitoring dataset is established.

3. The forming accuracy feedforward control method of a spring machine according to claim 1, characterized in that, S2 includes the following steps: The degree of accuracy deviation in the current forming cycle is identified by the deviation relationship between the wire feeding anomaly data, winding offset data and dimensional fluctuation data in the accuracy anomaly monitoring dataset. Identify the degree of cycle disturbance corresponding to the current forming cycle based on the disturbance relationship between tension drift data, cutting deviation data, and equipment status data; The accuracy instability index, the cycle time disturbance index, and the forming risk index are calculated based on the degree of accuracy deviation and the degree of cycle time disturbance, respectively. The slight deviation, moderate deviation, and severe deviation states are identified based on the correspondence between the accuracy instability index, the cycle disturbance index, the forming risk index, and the preset level range.

4. The spring forming accuracy feedforward control method according to claim 3, characterized in that, The calculation of the accuracy instability index, cycle time disturbance index, and forming risk index includes the following steps: The abnormal contribution value of the feed line side is determined based on the abnormal feed line data and the dimensional fluctuation data. The abnormal contribution value on the winding side is determined based on tension drift data and winding offset data; The abnormal contribution values ​​on the cutting side and the abnormal contribution values ​​on the cycle time side are determined based on the cutting deviation data and equipment status data. The abnormal contribution values ​​from the wire feeding side, winding side, cutting side, and cycle time side are fused together to generate the accuracy instability index and the cycle time disturbance index. A forming risk index is generated based on the continuous changes in the precision instability index, the cycle time disturbance index, and the dimensional fluctuation data.

5. The spring forming accuracy feedforward control method according to claim 1, characterized in that, S3 includes the following steps: Based on the combined relationship between the precision instability index, the cycle time disturbance index, and the forming risk index, a graded feedforward judgment result corresponding to the current forming cycle is generated. The parameter over-limit results are generated based on the comparison between the accuracy instability index and the preset parameter boundary, and the abnormal aggregation results are generated based on the aggregation distribution of the cycle disturbance index in the continuous forming cycle. The cycle time reset requirement result is generated based on the coupling relationship between the forming risk index, parameter over-limit results, and abnormal aggregation results.

6. The spring forming accuracy feedforward control method according to claim 1, characterized in that, S4 includes the following steps: When the graded feedforward judgment result is a slight deviation state, the parameter fine-tuning amount corresponding to the slight deviation state is determined based on the accuracy instability index and parameter over-limit result; When the graded feedforward judgment result is a moderate deviation state, the linkage compensation amount corresponding to the moderate deviation state is determined based on the accuracy instability index, the cycle disturbance index, and the abnormal aggregation result. When the graded feedforward judgment result is a severe deviation state, the cycle time reset amount corresponding to the severe deviation state is determined based on the forming risk index and cycle time reset requirement result. Boundary and priority checks are performed on parameter fine-tuning amounts, linkage compensation amounts, and cycle reset amounts; The parameter fine-tuning amount, linkage compensation amount, and cycle reset amount after boundary verification and priority verification are bound to the mild deviation state, moderate deviation state, and severe deviation state, respectively.

7. The spring forming accuracy feedforward control method according to claim 6, characterized in that, Determining the beat reset amount includes the following steps: The basic reset range of the cycle time reset is determined based on the forming risk index corresponding to the severe deviation state. Determine the additional correction range for the cycle reset amount based on the cycle reset requirement results and equipment status data; The basic reset amplitude and the additional correction amplitude are superimposed to generate the beat reset amount corresponding to the severe deviation state.

8. The spring forming accuracy feedforward control method according to claim 1, characterized in that, S5 includes the following steps: The parameter fine-tuning amount, linkage compensation amount, and cycle reset amount are respectively mapped to wire feeding control, winding control, cutting control, and cycle control to generate differentiated feedforward control strategies; Establish calling rules based on the execution priority, calling boundary, and exit boundary corresponding to the mild deviation state, moderate deviation state, and severe deviation state, respectively; The differentiated feedforward control strategy is uniformly associated with the invocation rules to generate a feedforward control invocation set; Write the set of feedforward control calls into the preset control call table for the next forming cycle.

9. The spring forming accuracy feedforward control method according to claim 1, characterized in that, S6 includes the following steps: Based on the differentiated feedforward control strategy, the preset control is executed before the next forming cycle, and the abnormal data of wire feeding, tension drift data, winding offset data, cutting deviation data, size fluctuation data and equipment status data after the preset control is called are recorded. Based on the changes in the accuracy anomaly monitoring dataset before and after the preset control call, anomaly classification results and accuracy optimization results are generated; The subsequent state switching results are generated based on the changing trends among the anomaly classification results, parameter over-limit results, anomaly aggregation results, and cycle time reset requirement results. The anomaly classification results, accuracy optimization results, and subsequent state switching results are bound to the current forming cycle; The results of the bound anomaly classification, precision optimization, and subsequent state switching will be used as the basis for subsequent preset control calls.

10. A spring forming accuracy feedforward control system, characterized in that, include: The data acquisition module is used to acquire data on abnormal wire feeding, tension drift, winding offset, cutting deviation, dimensional fluctuation, and equipment status, and to establish a dataset for monitoring accuracy anomalies. The calculation module is used to identify mild, moderate and severe deviation states based on the accuracy anomaly monitoring dataset, and to calculate the accuracy instability index, cycle time disturbance index and forming risk index. The feedforward judgment module is used to generate graded feedforward judgment results, parameter over-limit results, anomaly clustering results, and cycle time reset requirement results based on the accuracy instability index, cycle time disturbance index, and forming risk index. The control judgment module is used to determine the parameter fine-tuning amount corresponding to the mild deviation state, the linkage compensation amount corresponding to the moderate deviation state, and the cycle reset amount corresponding to the severe deviation state based on the graded feedforward judgment results. The module is used to generate differentiated feedforward control strategies based on parameter fine-tuning, linkage compensation, and cycle reset, and to establish calling rules corresponding to mild, moderate, and severe deviation states. The results output module is used to execute preset control before the next forming cycle according to the differentiated feedforward control strategy, and output the abnormal classification results, accuracy optimization results and subsequent state switching results.

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