A method and system for optimizing process parameters of a transmission shaft forging

Abstract

The application provides a process parameter optimization method and system for a transmission shaft forging, and is applied to the field of industrial data processing; the application can collect multi-dimensional process data such as forging pressure, impact speed, die temperature and equipment running state in real time based on a processing terminal during the forging process of the transmission shaft, dynamically predict and analyze the forging state of different structure regions, timely identify potential quality deviation regions, meanwhile, the application can establish the corresponding relationship between the process parameter change and the forging state evolution process by correlatively mining the historical forging state data of the abnormal change region, and clearly determine the key influencing link and parameter transmission path of the abnormal state, thereby avoiding the problem that the traditional method only relies on the passive adjustment of the final quality detection result.

Description

Technical Field

[0001] This invention relates to the field of industrial data processing, and in particular to a method and system for optimizing process parameters of a transmission shaft forging. Background Technology

[0002] As high-performance mechanical equipment places increasingly higher demands on the load-bearing capacity and reliability of transmission systems, drive shaft forgings are gradually developing towards higher strength, higher precision, and more complex structures. During the drive shaft forging process, the evolution of the internal microstructure, material flow state, and residual stress distribution of the forging are influenced by multiple process parameters, including heating temperature, forging pressure, deformation rate, die condition, and cooling process. Because there is a continuous influence between different forging stages, the temperature distribution, plastic deformation state, and internal stress state formed in the previous stage directly affect the forming effect of the subsequent stages. Therefore, it is necessary to coordinately optimize the process parameters of multiple stages.

[0003] However, existing methods for optimizing the process parameters of drive shaft forgings typically rely on preset process curves or adjustments based on historical experience, targeting only a single processing stage or a single quality indicator. For example, if the heating temperature and forging pressure are preset during the forging process and kept constant throughout the entire production process, it is impossible to adjust subsequent process parameters in a timely manner when raw material properties fluctuate, equipment response changes, or abnormal deformation occurs in localized areas of the forging. Summary of the Invention

[0004] This invention aims to solve the problem of how to obtain information on the changes in the state of forgings in real time during the forging process of drive shafts, establish a dynamic correlation between process parameters and the evolution of the state of forgings, and thereby coordinately optimize and adjust the process parameters of different forging stages and different regions. It provides a method and system for optimizing the process parameters of drive shaft forgings.

[0005] The present invention employs the following technical means to solve the technical problem: This invention provides a method for optimizing process parameters of a drive shaft forging, comprising: Based on the forging change state data pre-acquired by the processing terminal for the drive shaft, the structural characteristics of the drive shaft are identified. According to the structural characteristics, the drive shaft is divided into several state regions, and the predicted forging state of the drive shaft after a preset time period is constructed. The forging change state data specifically includes the forging pressure, impact speed, die temperature, equipment response time and equipment operating parameters generated by the forging equipment. The state regions specifically include the journal region, the shoulder transition region and the main bearing region. Determine whether the predicted forging condition meets the preset forging quality requirements; If not, then during the processing, identify the abnormal areas that have changed, extract the change features corresponding to the abnormal areas, match the change features with the preset forming rules of each area, dynamically divide the abnormal change areas of the drive shaft, extract the historical forging state data associated with the abnormal change areas, and construct the corresponding regional state change correlation relationship. The change features specifically include the location of the abnormality, the duration of the abnormality, the magnitude of the change, and the direction of the change. Determine whether the historical forging status data meets the preset status association conditions; If the conditions are met, the parameter change nodes that caused the current abnormal state are identified. Based on the parameter change nodes, the transmission relationship between the changed parameters in the historical forging process is analyzed to generate the abnormal state evolution path. The state change process caused by the change of process parameters in the abnormal change area is obtained. According to the abnormal state evolution path, the influencing factors of the current abnormal state are collected. Based on the influencing factors, the corresponding key process parameter set and parameter influence weight are generated. The process parameter adjustment constraint relationship of the abnormal change area is established. The changed parameters specifically include temperature change, pressure change, deformation degree change and material flow state. The influencing factors specifically include main process factors and multi-parameter coupling factors.

[0006] Furthermore, the step of identifying the changed state regions during processing, extracting the change features corresponding to the state regions, matching the change features with the preset forming rules of each region, and dynamically dividing the abnormal change regions of the transmission shaft further includes: Based on the change characteristics corresponding to the abnormal change region, the characteristic change sequence of the abnormal change region in the continuous processing stage is obtained; Determine whether the trend of change in the feature change sequence meets the preset forming deviation condition; If so, then based on the direction of change of each processing stage in the feature change sequence, the deviation development process of the abnormal change region is obtained. According to the order of processing stages, the change characteristics of each stage are correlated to identify the key change stage that causes the abnormal change region to continuously deviate from the forming law. Through the regional state change information corresponding to the key change stage, the process change factors affecting the forming state of the transmission shaft in the processing stage are extracted, and the coupling relationship between the abnormal change region and the process parameter change is established.

[0007] Furthermore, before the step of extracting historical forging state data associated with the abnormal change area and constructing the corresponding regional state change correlation, the method further includes: Based on the processing response information corresponding to the abnormal change region, the forming deviation information of the abnormal change region in the continuous forging stage is extracted, wherein the forming deviation information specifically includes the heat transfer trajectory, material flow trajectory and strain accumulation characteristics. Determine whether the forming deviation process corresponding to the forming deviation information matches the preset historical traceability conditions; If so, based on the local concentration trend in the forming deviation information, the initial formation stage of the deviation phenomenon in the abnormal change area is determined. Based on the processing node information corresponding to the initial formation stage, the forging forming response characteristics in the subsequent forging stage are analyzed in reverse. Historical processing segments that correspond to the current abnormal change area formation process are selected. Specifically, the local concentration trend includes the temperature gradient change law and the plastic migration direction. The forging forming response characteristics specifically include the load response process, the degree of regional deformation coordination, and the microstructure evolution trend.

[0008] Furthermore, the step of identifying the parameter change nodes that cause the current abnormal state, and based on the parameter change nodes, analyzing the transmission relationship between the changed parameters during the historical forging process to generate the abnormal state evolution path, also includes: Based on the forging process information corresponding to the parameter change nodes, the temporal sequence relationship and the scope of influence between each parameter change node are obtained; Based on the time sequence and the scope of influence, determine whether the preset influence transmission conditions are met between the parameter change nodes; If so, the order of influence of different parameter change nodes on the forging forming result is collected, the direction of influence transmission between parameter change nodes is identified, and based on the direction of influence transmission, the coordinated change process between control parameters in the historical forging process is analyzed, the key response links passed through by the parameter change from the initial disturbance to the regional forming anomaly are generated, and the mapping relationship between the parameter change nodes and the forging state response characteristics is established. The control parameters specifically include temperature control parameters, loading parameters, deformation control parameters and cooling adjustment parameters.

[0009] Furthermore, the step of determining whether the predicted forging condition meets the preset forging quality requirements also includes: Based on the state description information corresponding to the predicted forging state, the quality risk distribution of the predicted forging state in different regions is extracted, wherein the quality risk distribution specifically includes quality risk indicators, performance deviation degree and defect formation tendency; Determine whether the quality risk distribution meets the preset risk control conditions; If so, identify the risk clustering area corresponding to the quality risk distribution, analyze the performance deviation direction corresponding to the risk clustering area, and based on the performance deviation direction, track the forging characteristics corresponding to the risk clustering area in the predicted forging process to determine the key influencing links of quality risk. The forging characteristics specifically include material flow state, heat exchange process and force transmission.

[0010] Furthermore, the step of determining whether the historical forging state data meets the preset state association conditions also includes: Obtain the degree of process behavior reproducibility corresponding to the historical forging state data; Determine whether the degree of reproducibility of the process behavior meets the preset reproducibility requirements; If so, based on the processing action sequence of the degree of reproducibility of the process behavior, the execution connection mode between different processes is identified from the historical forging process, the main processing links in which the forging shape of the drive shaft changes are divided, the equipment control behavior of the processing terminal in the forging process is obtained, the forging forming effect of the equipment control behavior on the drive shaft is collected, and based on the forging forming result, historical forming cases corresponding to the current abnormal change area are selected.

[0011] Furthermore, the step of identifying the structural characteristics of the drive shaft based on the forging change state data pre-acquired by the processing terminal also includes: Extract the structural forming feature information corresponding to the structural parts, wherein the structural forming feature information specifically includes cross-sectional transition relationship, force transmission path, material filling requirements and local forming constraint information; Determine whether the structural forming feature information meets the preset forming sensitivity conditions; If so, based on the cross-sectional transition relationship, identify the degree of geometric change of the transmission shaft between different connection areas, divide the structural transition positions that cause material flow obstruction, collect the transmission mode of external loading during forging between different structural areas according to the force transmission path, and determine the key load-bearing area that bears the concentrated load.

[0012] This invention also provides a process parameter optimization system for drive shaft forgings, including: The construction module is used to identify the structural characteristics of the drive shaft based on the forging change state data pre-acquired by the processing terminal, divide the drive shaft into several state regions according to the structural characteristics, and construct the predicted forging state of the drive shaft after a preset time period. The forging change state data specifically includes the forging pressure, impact velocity, die temperature, equipment response time and equipment operating parameters generated by the forging equipment, and the state regions specifically include the journal region, the shoulder transition region and the main bearing region. The judgment module is used to determine whether the predicted forging state can meet the preset forging quality requirements; The execution module is used to identify abnormal areas that have changed during the processing if not, extract the change features corresponding to the abnormal areas, match the change features with the preset forming rules of each area, dynamically divide the abnormal change areas of the transmission shaft, extract the historical forging state data associated with the abnormal change areas, and construct the corresponding regional state change correlation relationship. The change features specifically include the location of the abnormality, the duration of the abnormality, the magnitude of the change, and the direction of the change. The second judgment module is used to determine whether the historical forging state data meets the preset state association conditions; The second execution module is used to identify the parameter change nodes that cause the current abnormal state if the condition is met. Based on the parameter change nodes, it analyzes the transmission relationship between the changed parameters in the historical forging process, generates an abnormal state evolution path, obtains the state change process of the abnormal change area caused by the change of process parameters, collects the influencing factors of the current abnormal state according to the abnormal state evolution path, generates the corresponding key process parameter set and parameter influence weights according to the influencing factors, and establishes the process parameter adjustment constraint relationship of the abnormal change area. The changed parameters specifically include temperature change, pressure change, deformation degree change and material flow state, and the influencing factors specifically include main process factors and multi-parameter coupling factors.

[0013] Furthermore, the execution module also includes: The acquisition unit is used to acquire the feature change sequence of the abnormal change region during the continuous processing stage based on the change characteristics corresponding to the abnormal change region. The judgment unit is used to determine whether the change trend in the feature change sequence meets the preset forming deviation condition; The execution unit is configured to, if so, obtain the deviation development process of the abnormal change region according to the change amplitude and direction corresponding to each processing stage in the feature change sequence, associate the change features of each stage according to the order of processing stages, identify the key change stage that causes the abnormal change region to continuously deviate from the forming law, extract the process change factors affecting the forming state of the transmission shaft in the processing stage through the regional state change information corresponding to the key change stage, and establish the coupling relationship between the abnormal change region and the process parameter change.

[0014] Furthermore, it also includes: The extraction module is used to extract the forming deviation information of the abnormal change region in the continuous forging stage based on the processing response information corresponding to the abnormal change region. The forming deviation information specifically includes the heat transfer trajectory, material flow trajectory and strain accumulation characteristics. The third judgment module is used to determine whether the forming deviation process corresponding to the forming deviation information matches the preset historical traceability conditions. The third execution module is used to, if so, determine the initial formation stage of the deviation phenomenon in the abnormal change area based on the local concentration trend in the forming deviation information, and reverse analyze the forging forming response characteristics in the subsequent forging stage based on the processing node information corresponding to the initial formation stage, and screen historical processing segments that correspond to the current abnormal change area formation process. The local concentration trend specifically includes the temperature gradient change law and the plastic migration direction, and the forging forming response characteristics specifically include the load response process, the degree of regional deformation coordination, and the microstructure evolution trend.

[0015] This invention provides a method and system for optimizing the process parameters of drive shaft forgings, which has the following beneficial effects: This invention enables dynamic prediction and analysis of the forging state in different structural regions during the forging process of drive shafts, based on multi-dimensional process data such as forging pressure, impact speed, mold temperature, and equipment operating status collected in real time by the processing terminal. It can promptly identify potential quality deviation areas and establish a correspondence between process parameter changes and the evolution of forging state by correlating and mining historical forging state data of abnormal change areas. This clarifies the key influencing links and parameter transmission paths of abnormal states, avoiding the problem of passive adjustment based solely on the final quality inspection results in traditional methods. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an embodiment of the method for optimizing process parameters of the drive shaft forging according to the present invention. Figure 2 This is a structural block diagram of an embodiment of the process parameter optimization system for drive shaft forgings of the present invention. Detailed Implementation

[0017] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. The realization of the purpose, functional features, and advantages of the invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings.

[0018] The technical solutions of the embodiments of the present invention 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 the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Reference Appendix Figure 1The method for optimizing process parameters of a drive shaft forging according to one embodiment of the present invention includes: S1: Based on the forging change state data pre-acquired by the processing terminal for the drive shaft, the structural characteristics of the drive shaft are identified. According to the structural characteristics, the drive shaft is divided into several state regions, and the predicted forging state of the drive shaft after a preset time period is constructed. The forging change state data specifically includes the forging pressure, impact speed, die temperature, equipment response time and equipment operating parameters generated by the forging equipment. The state regions specifically include the journal region, the shoulder transition region and the main bearing region. S2: Determine whether the predicted forging state can meet the preset forging quality requirements; S3: If not, identify the abnormal areas that have changed during the processing, extract the change features corresponding to the abnormal areas, match the change features with the preset forming rules of each area, dynamically divide the abnormal change areas of the transmission shaft, extract the historical forging state data associated with the abnormal change areas, and construct the corresponding regional state change correlation relationship. The change features specifically include the location of the abnormality, the duration of the abnormality, the magnitude of the change, and the direction of the change. S4: Determine whether the historical forging state data meets the preset state association conditions; S5: If the conditions are met, identify the parameter change nodes that caused the current abnormal state. Based on the parameter change nodes, analyze the transmission relationship between the changed parameters in the historical forging process, generate the abnormal state evolution path, obtain the state change process of the abnormal change area caused by the change of process parameters, collect the influencing factors of the current abnormal state according to the abnormal state evolution path, generate the corresponding key process parameter set and parameter influence weight according to the influencing factors, and establish the process parameter adjustment constraint relationship of the abnormal change area. The changed parameters specifically include temperature change, pressure change, deformation degree change and material flow state, and the influencing factors specifically include main process factors and multi-parameter coupling factors.

[0020] In this embodiment, the system uses forging change state data of the drive shaft pre-acquired by the processing terminal. This data specifically includes forging pressure, impact velocity, die temperature, equipment response time, and equipment operating parameters generated by the forging equipment. The system identifies the structural characteristics of the drive shaft and, based on these characteristics, divides it into several state regions, including a journal region, a shoulder transition region, and a main bearing region. It then constructs a predicted forging state of the drive shaft after a pre-set time period. The system then determines whether the predicted forging state meets the pre-set forging quality requirements and executes the corresponding steps. For example, when the system determines that the predicted forging state of the drive shaft after a pre-set time period... When the forging condition meets the preset forging quality requirements, the system assumes that the trend of process parameter changes during the current forging process can maintain stable forming of each state region of the drive shaft. Based on the forming results of each state region corresponding to the predicted forging condition, the system records the effective control range of the current forging pressure, impact velocity, die temperature, equipment response time, and equipment operating parameters, and stores this set of process parameters as a stable processing state sample. For example, if the system determines that the predicted forging condition of the drive shaft after a preset time period cannot meet the preset forging quality requirements, the system assumes that the current combination of forging process parameters does not have the sustainable capability to meet the target quality requirements. If the process requires immediate parameter adjustment, the system will identify abnormal areas during processing, extract the corresponding change characteristics, including the location, duration, magnitude, and direction of the anomaly. These characteristics are then matched with pre-defined forming rules for each area to dynamically divide the abnormal change areas of the drive shaft. Historical forging state data associated with these abnormal change areas is extracted to construct corresponding regional state change relationships. The system also judges the quality requirements of the predicted forging state. When the predicted result fails to meet the target quality requirements, it can promptly determine that the current combination of process parameters is causing quality issues in the forging. This approach mitigates the risk of deviation and avoids the problem of delayed adjustments caused by traditional methods that only detect defects after forging is completed through inspection. It enhances the proactiveness of process optimization. Furthermore, by extracting historical forging state data associated with abnormal change areas, the system establishes correlations between regional state changes. This allows the system to filter data from historical processing that is relevant to the current abnormal state, uncovering the correspondence between the formation process of abnormal areas and historical process conditions. This provides a basis for subsequent analysis of the impact of process parameter changes on the evolution of forging states. The system then determines whether these historical forging state data associated with abnormal change areas meet pre-set state correlation conditions to execute corresponding steps.For example, when the system determines that a portion of historical forging state data associated with an abnormal change area does not meet the pre-set state association conditions, the system will consider that there is insufficient state correspondence between this portion of historical forging state data and the current abnormal change area, and it cannot accurately reflect the formation process of the current abnormal state. The system will mark this portion of historical forging state data as low-correlation data and reduce its reference weight in the abnormal state analysis process. At the same time, based on information such as the location of the abnormality, the direction of change, the duration, and the magnitude of change corresponding to the current abnormal change area, the system will expand the scope of historical data retrieval again, from different processing stages, different structural areas, and different process conditions. The system filters historical forging process data that has a higher degree of matching with the current abnormal state. For example, when the system determines that a certain portion of historical forging state data associated with the abnormal change area meets the pre-set state association conditions, the system will consider that directly using this portion of historical data for abnormal cause analysis will not cause judgment bias due to the influence of process parameters, and subsequent optimization schemes can still address the current forging problem. The system will identify the parameter change nodes that caused the current abnormal state, and based on these parameter change nodes, analyze the transmission relationship between the changing parameters in the historical forging process. The changing parameters specifically include temperature changes, pressure changes, deformation degree changes, and material flow state, generating an abnormal state evolution. The system analyzes the evolution path of abnormal states to obtain the state change process caused by changes in process parameters. Based on each abnormal state evolution path, it collects the influencing factors of the current abnormal state. These influencing factors include main process factors and multi-parameter coupling factors. Based on different influencing factors, it generates corresponding key process parameter sets and parameter influence weights (the key process parameter set is used to characterize the process parameters that need to be adjusted first, and the parameter influence weights are used to characterize the degree of influence of different process parameters on the improvement effect of abnormal states). It establishes the process parameter adjustment constraint relationship for the abnormal change region. The system judges whether historical forging state data meets the state correlation conditions and selects data with effective correlation value for analysis. This can avoid errors in process influence judgment caused by historical data reference bias. At the same time, by identifying the parameter change nodes that cause the current abnormal state and analyzing the transmission relationship between temperature change, pressure change, deformation degree change and material flow state, it can transform the traditional method of only detecting abnormal results into dynamically tracking the abnormal formation process. Furthermore, by extracting main process factors and multi-parameter coupling factors based on the abnormal state evolution path, it further generates key process parameter sets and parameter influence weights, which can identify the degree of influence of different process parameters on the formation of abnormal states and avoid the problem of not being able to determine the main influencing factors when multiple parameters change simultaneously. ;

[0021] It should be noted that during the processing, abnormal areas that have changed are identified, and the change features corresponding to these abnormal areas are extracted. These change features are then matched with the preset forming rules for each area to dynamically divide the abnormal change areas of the drive shaft. Historical forging state data associated with these abnormal change areas are extracted, and corresponding regional state change correlations are constructed. Specifically: Based on the regional response during the forging process, the system detects whether there are any deviations from the normal forming rules at different structural positions of the drive shaft. For example, local dimensional changes may occur in the journal area, insufficient material filling may occur in the shoulder transition area, and uneven deformation may occur in the main load-bearing area. When the system identifies a region with changes, it further extracts the corresponding change characteristics, including the location of the abnormality, the duration of the abnormality, the magnitude of the change, and the direction of the change, to describe the specific manifestation of the abnormal change. For example, if the system detects that the temperature drop rate in the shoulder transition area increases significantly during the continuous forging stage, and the material flow rate in this area decreases, and this trend continues for multiple processing stages, then this area is identified as a key analysis target. Subsequently, the system matches and analyzes the extracted variation features with the pre-established forming rules for each region. Since different structural regions of the drive shaft play different roles during forging, the forming requirements for each region also differ. For example, the journal region primarily focuses on the ability to maintain cross-sectional dimensions, the shoulder transition region primarily focuses on material flow continuity and transition forming quality, and the main load-bearing region primarily focuses on overall deformation coordination. Therefore, based on the forming rules corresponding to different regions, the system determines whether the current change belongs to normal processing fluctuations or an abnormal state deviation caused by changes in process parameters. For example, the journal region is allowed to have a certain range of dimensional changes during normal forging. When the detected change exceeds the allowable range for that region, it is determined that there is an abnormal change in that region, and the analysis scope is further expanded. After identifying abnormal changes, the system dynamically divides the abnormal change areas in the drive shaft based on the degree of change in different regions, the duration of the abnormality, and the influence relationship between regions. This process does not fix a specific location as the abnormal area, but adjusts the range of the abnormal area in real time according to the actual changes during the forging process. For example, if the system initially detects a local material flow abnormality in the shoulder transition area, and analysis reveals that this abnormality will further affect the forming state of the adjacent main load-bearing area, then the shoulder transition area and the main load-bearing area are jointly classified as abnormal change areas. If the abnormality is limited to a local location on the journal, a smaller range of abnormal area is maintained, thereby improving the accuracy of abnormality location. Furthermore, for the identified abnormal change area, the system extracts historical forging state data associated with that area. This historical forging state data may include records of forging pressure changes, die temperature changes, equipment operating status information, and forming result information for the corresponding area during historical processing stages. The system analyzes the state performance of the same area under similar process conditions during historical processing to find data that is valuable for reference in relation to the formation process of the current abnormal area. For example, when insufficient material filling occurs in the current shoulder transition area, the system retrieves forging pressure, impact velocity, and die temperature data when similar forming deviations occur in the same structural area during historical production, and analyzes the correspondence between these historical process states and the current abnormal situation. Finally, based on the historical forging state data obtained through screening, the system constructs the correlation relationship of regional state changes corresponding to the abnormal change areas. This is used to describe the mutual influence process between different processing stages, different process parameters, and different regional states. For example, through historical data analysis, it was found that when the mold temperature drops beyond a certain range, the material flow capacity of the shoulder transition area decreases, which then leads to local insufficiency. The system then establishes a state correlation relationship of "mold temperature change - material flow change - regional forming abnormality", providing a basis for subsequent identification of key process parameters that cause abnormalities and for formulating targeted parameter adjustment strategies.

[0022] It should be added that the parameter change nodes that cause the current abnormal state are identified. Based on these parameter change nodes, the transmission relationship between the changed parameters in the historical forging process is analyzed to generate an abnormal state evolution path. This yields the state change process of the abnormal change region caused by the change in process parameters. According to the abnormal state evolution path, the influencing factors of the current abnormal state are collected. Based on these influencing factors, a corresponding set of key process parameters and parameter influence weights are generated. Finally, the process parameter adjustment constraint relationship of the abnormal change region is established. Specifically: Based on historical forging status data and the changes in the current abnormal area, the system identifies parameter change nodes that lead to the current abnormal state. These parameter change nodes refer to key time points or processing stages during the forging process where a certain process parameter changes significantly, further causing a shift in the forging state. The system does not simply acquire all changed parameters, but rather combines the abnormal area formation process to screen parameter change nodes that have a real impact on the abnormal state. For example, when insufficient material filling occurs in the shoulder transition area, the system analyzes historical processing data and finds that there was a continuous drop in die temperature and insufficient increase in forging pressure during a certain processing stage before the abnormality occurred. The corresponding die temperature change node and pressure change node are then identified as key parameter change nodes that led to the formation of the abnormal state. Subsequently, based on the determined parameter change nodes, the system analyzes the transmission relationship between different changing parameters during the historical forging process. Since the process parameters in drive shaft forging do not act independently but rather have interrelationships—for example, changes in mold temperature affect material plasticity, changes in material plasticity further affect material flow state, and material flow state in turn affects the final forming effect—the system analyzes the subsequent state response after different parameters change based on information such as temperature changes, pressure changes, deformation degree changes, and material flow state in historical forging data, determining the order of action between parameters. For example, if the system finds that during a certain batch of forging, after the mold temperature decreases, the material flow rate gradually decreases, and subsequently, insufficient filling occurs in the shoulder area, then a transmission relationship of "decreased mold temperature—decreased material flow capacity—abnormal regional forming" is established. Based on the aforementioned parameter change nodes and the transmission relationships between the changing parameters, the system further generates an abnormal state evolution path to describe the entire process of an abnormal change region gradually developing from a normal state to an abnormal state. This abnormal state evolution path can include an abnormal triggering stage, a state change stage, and an abnormal formation stage. For example, for an abnormal situation in the shoulder transition region, the evolution path generated by the system may be: abnormal drop in mold temperature → local reduction in material plasticity → obstruction of metal flow → insufficient filling of the shoulder region → local quality deviation of the forging. By establishing this evolution path, the system can clearly identify that the abnormality is not directly caused by a single parameter, but is the result of the gradual transmission of multiple process factors. Furthermore, the system collects and analyzes the influencing factors corresponding to the current abnormal state based on the abnormal state evolution path. These influencing factors include key process factors and multi-parameter coupling factors. Key process factors describe the direct impact of a single key parameter on the abnormal state, such as insufficient forging pressure leading to a decrease in material filling capacity. Multi-parameter coupling factors describe the comprehensive impact caused by the combined changes of multiple parameters, such as a simultaneous decrease in mold temperature and impact velocity, further reducing material flowability. The system analyzes each influencing link in the abnormal state evolution path to determine the degree of influence of different influencing factors on the formation of the abnormality. Finally, based on the identified influencing factors, the system generates a set of key process parameters and their influence weights. The set of key process parameters is used to determine the process parameters that need to be adjusted in the future. For example, for the problem of insufficient filling in the shoulder area, the mold temperature, forging pressure, and impact speed are determined as key adjustment parameters. The influence weights of the parameters are used to represent the importance of different parameters to the formation of abnormal states. For example, the influence weight of mold temperature is determined to be 0.5, the influence weight of forging pressure is 0.3, and the influence weight of impact speed is 0.2.

[0023] In this embodiment, step S3, which involves identifying changed state regions during processing, extracting the change features corresponding to the state regions, and matching the change features with preset forming rules for each region to dynamically divide the abnormal change regions of the transmission shaft, further includes: S31: Based on the change characteristics corresponding to the abnormal change region, obtain the characteristic change sequence of the abnormal change region during the continuous processing stage; S32: Determine whether the trend of change in the feature change sequence meets the preset forming deviation condition; S33: If so, then according to the change amplitude and direction corresponding to each processing stage in the feature change sequence, obtain the deviation development process of the abnormal change region, associate the change characteristics of each stage according to the order of processing stages, identify the key change stage that causes the abnormal change region to continuously deviate from the forming law, extract the process change factors that affect the forming state of the transmission shaft in the processing stage through the regional state change information corresponding to the key change stage, and establish the coupling relationship between the abnormal change region and the process parameter change.

[0024] In this embodiment, the system acquires the characteristic change sequence of the abnormal change region during the continuous processing stage based on the change characteristics corresponding to these abnormal change regions. Then, the system determines whether the change trend in these characteristic change sequences meets the pre-set forming deviation conditions, and executes the corresponding steps accordingly. For example, when the system determines that the characteristic change sequence of the abnormal change region during the continuous processing stage cannot meet the pre-set forming deviation conditions, the system considers the currently detected change to be a short-term deviation caused by equipment operation disturbances, instantaneous material fluctuations, or changes in the processing environment during the forging process, which will not continuously affect the subsequent forming quality of the drive shaft. The system will temporarily mark the abnormal change region, retain the corresponding characteristic change sequence as process monitoring data, and reduce the risk level of the current abnormal state in the region. It will continuously track the region based on information such as the location of the abnormality, the magnitude of the change, the recovery status, and the forming performance of the region, which are continuously collected during the subsequent processing stages. For example, when the system determines that the characteristic change sequence of the abnormal change region during the continuous processing stage can meet the pre-set forming deviation conditions, the system considers that the current abnormal change region has a certain degree of state fluctuation, and the fluctuation has formed a continuous forming deviation. The system analyzes trends and, based on the magnitude and direction of changes in each processing stage within these characteristic change sequences, identifies the deviation development process of abnormal change areas. It then correlates the change characteristics of each stage according to the sequence of processing stages, identifying the key change stages that cause the abnormal change areas to continuously deviate from the forming pattern. By analyzing the regional state change information corresponding to different key change stages, the system extracts the process change factors affecting the forming state of the drive shaft within each processing stage, establishing a coupling relationship between abnormal change areas and process parameter changes. By analyzing the change trends of abnormal change characteristics within continuous processing stages, the system can distinguish between short-term processing fluctuations and continuous forming deviation risks. When a continuously developing abnormal trend is detected, potential quality problems can be identified in advance, avoiding the situation in traditional forging processes where relying solely on finished product inspection to discover defects leads to delayed adjustments. Furthermore, by acquiring the deviation development process of abnormal change areas based on the change magnitude and direction corresponding to different processing stages, and performing correlation analysis on the change characteristics of each stage according to the processing stage sequence, the system can accurately locate the key change stages leading to the continuous expansion of abnormalities, clarifying the starting point and development path of the abnormal state, and avoiding the problem of being unable to determine the main stage of action due to numerous influencing factors.

[0025] It should be noted that, based on the direction of change corresponding to each processing stage in the characteristic change sequence, the deviation development process of the abnormal change region is obtained. According to the chronological order of the processing stages, the change characteristics of each stage are correlated to identify the key change stages that cause the abnormal change region to continuously deviate from the forming law. Through the regional state change information corresponding to the key change stages, the process change factors affecting the forming state of the drive shaft within the processing stages are extracted, and the coupling relationship between the abnormal change region and process parameter changes is established. Specifically: The system acquires information on the changes in abnormal areas during continuous processing stages, records the degree and trend of change at each stage according to the processing time sequence, and determines whether the abnormal state is gradually expanding, remaining stable, or recovering. For example, during the forging process of the transition area of ​​the drive shaft shoulder, the system continuously collects data on the dimensional offset, material flow state, and temperature changes of this area in multiple processing stages. It finds that the dimensional offset is 0.2mm in the first stage, increases to 0.5mm in the second stage, and increases to 0.8mm in the third stage, indicating that the abnormal area has a continuously expanding deviation trend. Based on this, the system can obtain the process of the area gradually developing from a slight deviation to a significant forming deviation. Subsequently, the system performs correlation analysis on the change characteristics corresponding to each stage according to the sequence of processing stages. Since abnormal states in the drive shaft forging process usually do not occur suddenly at a certain moment, but are formed by the gradual accumulation of parameter changes in multiple processing stages, the system needs to analyze the continuous influence relationship between different stages. For example, if the system detects that the mold temperature drops in the early processing stage of the shoulder transition area, the material flow rate decreases in the middle processing stage, and the local filling is insufficient in the later processing stage, then the system correlates the change characteristics corresponding to these stages to determine that the early temperature change may be an important reason for the subsequent abnormal material flow and the final forming deviation, rather than taking the final filling insufficiency as the cause of the abnormality alone. After completing the stage correlation, the system further identifies the key change stages that cause the abnormal change area to continuously deviate from the forming law. Specifically, the system selects the stages that play a decisive role in the development of the anomaly based on the degree of change, the scope of influence, and the subsequent state response of each processing stage. For example, during the analysis, it was found that although the shoulder area showed obvious forming deviation in the third processing stage, the forging pressure had already decreased and the material flow was restricted in the second processing stage. Furthermore, the abnormal state in the third stage was formed by the accumulation of the second stage. Therefore, the system identified the second processing stage as the key change stage. By identifying the key change stage, it is possible to avoid adjusting only the final abnormal manifestation and instead locate the source of the anomaly. Furthermore, the system extracts process change factors affecting the forming state of the drive shaft during the processing stage by using the regional state change information corresponding to the key change stages. The regional state change information can include temperature changes, material flow state, local deformation degree, and changes in forming results in abnormal areas. The system combines this information to analyze the influence of corresponding process parameters. For example, during the key change stage, if the system finds that the temperature drop rate in the shoulder area is accelerating and the material flow range is decreasing, it determines that the mold temperature change may be an important process factor affecting the forming state of this area. If a decrease in forging pressure is also detected, it further determines that the combined effect of mold temperature and forging pressure leads to a decrease in material filling capacity. Finally, based on the extracted process variation factors, the system establishes a coupling relationship between abnormal variation areas and process parameter changes to describe how different process parameter changes jointly affect the forming state of abnormal areas. For example, the system establishes a coupling relationship of "decreased mold temperature → decreased material plasticity → obstructed material flow → insufficient shoulder filling", or a coupling relationship of "insufficient forging pressure + abnormal deformation speed → uneven local deformation → performance deviation of the main body load-bearing area".

[0026] In this embodiment, before step S3, which involves extracting historical forging state data associated with the abnormal change area and constructing the corresponding regional state change correlation, the method further includes: S301: Based on the processing response information corresponding to the abnormal change region, extract the forming deviation information of the abnormal change region in the continuous forging stage, wherein the forming deviation information specifically includes heat transfer trajectory, material flow trajectory and strain accumulation characteristics; S302: Determine whether the forming deviation process corresponding to the forming deviation information matches the preset historical traceability conditions; S303: If so, then based on the local concentration trend in the forming deviation information, determine the initial formation stage of the deviation phenomenon in the abnormal change area, and based on the processing node information corresponding to the initial formation stage, reverse analyze the forging forming response characteristics in the subsequent forging stage, and screen historical processing segments that correspond to the current abnormal change area formation process. The local concentration trend specifically includes the temperature gradient change law and the plastic migration direction, and the forging forming response characteristics specifically include the load response process, the degree of regional deformation coordination, and the microstructure evolution trend.

[0027] In this embodiment, the system extracts forming deviation information of the abnormal change area during the continuous forging stage based on the processing response information corresponding to the abnormal change area. The forming deviation information specifically includes the heat transfer trajectory, material flow trajectory, and strain accumulation characteristics. The system then determines whether the forming deviation process corresponding to this forming deviation information matches pre-set historical tracing conditions to execute the corresponding steps. For example, if the system determines that the forming deviation process corresponding to the forming deviation information of a certain abnormal change area during the continuous forging stage cannot match the pre-set historical tracing conditions, the system will consider that the current formation process of the abnormal change area differs significantly from the existing abnormal formation patterns in the historical forging process. The existing historical forging state data cannot accurately explain the cause of the abnormal area's formation. The system will then re-acquire real-time information such as heat input, material flow state, local deformation, and equipment execution status within the processing stage corresponding to the abnormal area, and independently analyze the current abnormal formation process. Conversely, if the system determines that the forming deviation process corresponding to the forming deviation information of a certain abnormal change area during the continuous forging stage can match the pre-set historical tracing conditions, the system will consider that the abnormality is caused solely by previously common factors such as temperature changes, pressure fluctuations, and abnormal material flow. The system will then execute the corresponding steps based on these forming deviation information... The system analyzes local concentration trends in the forming deviation information, specifically including temperature gradient variation patterns and plastic migration directions, to determine the initial formation stage of the deviation phenomenon in the abnormal change area. Based on the processing node information corresponding to the initial formation stage, it reverse-analyzes the forging response characteristics in subsequent forging stages. These forging response characteristics specifically include load response process, regional deformation coordination degree, and microstructure evolution trend. Historical processing segments corresponding to the current abnormal change area formation process are selected. By determining whether the forming deviation process meets historical tracing conditions, the system calls up valuable historical processing segments when a match is successful, quickly determining the correspondence between the abnormal state and historical processing processes, improving the accuracy and reliability of abnormal cause analysis. Furthermore, by analyzing the local concentration trends in the forming deviation information, combined with temperature gradient variation patterns and plastic migration directions, the system can locate the initial formation stage of the abnormal change area transitioning from a normal forming state to a deviation state, clarifying the starting processing node of the abnormality. Moreover, by reverse-analyzing the load response process, regional deformation coordination degree, and microstructure evolution trend in subsequent forging stages based on the processing node information corresponding to the initial formation stage, the system can establish a dynamic connection between the abnormal formation stage and subsequent forming results, identifying the impact of process changes on the development of the forging state.

[0028] It should be noted that, based on the local concentration trend in the forming deviation information, the initial formation stage of the deviation phenomenon in the abnormal change area is determined. Based on the processing node information corresponding to the initial formation stage, the forging forming response characteristics in subsequent forging stages are analyzed in reverse, and historical processing segments corresponding to the current abnormal change area formation process are selected. Specifically: The system acquires the thermal transfer trajectory and plastic migration direction of the abnormal change area during the continuous forging stage, analyzes whether the abnormal changes are concentrated in a certain local location in different processing stages, and determines the processing stage in which the abnormal state first shows a significant deviation. Among them, the thermal transfer trajectory is used to describe the transfer and change of heat between different areas of the drive shaft during the forging process, and the plastic migration direction is used to reflect the flow trend of the material under the action of force. For example, during the processing of the transition area of ​​the drive shaft shoulder, the system found that the temperature distribution in this area remained stable in the first two processing stages, but after entering the third processing stage, a significant increase in temperature gradient appeared in this area, and at the same time, the material flow direction gradually shifted from the normal filling direction. The system then determined that the third processing stage was the initial formation stage of the deviation phenomenon of the abnormal change area. After determining the initial forming stage, the system performs reverse analysis on the forging forming response characteristics in subsequent forging stages based on the processing node information corresponding to that stage. Since abnormal states typically have a continuous impact, small deviations generated in the initial stage may gradually amplify in subsequent processing. Therefore, the system does not only analyze the state at the time of the abnormality but also tracks the forming changes in each processing stage after that stage. The forging forming response characteristics include the load response process, the degree of regional deformation coordination, and the microstructure evolution trend. The load response process is used to analyze the stress changes in different regions after the forging pressure is applied; the degree of regional deformation coordination is used to determine whether the deformation between different structural regions of the drive shaft remains consistent; and the microstructure evolution trend reflects the changes in the internal microstructure of the material as the processing progresses. For example, once the system determines that the shoulder transition area has an initial deviation in the third processing stage, it further analyzes the load changes in the corresponding area in the fourth and fifth processing stages. If it finds that the forging pressure continues to increase in subsequent stages, but the deformation growth rate of the shoulder area is lower than that of the main area, and the internal structure of the material changes abnormally, the system believes that the deviation generated in the initial stage has affected the subsequent forming process, and records the complete response process corresponding to the abnormality. Through this reverse analysis method, the system can clearly understand the development process of the abnormality from its generation to its expansion, rather than just obtaining the final quality problem. Furthermore, based on the forging forming response characteristics obtained from the analysis, the system filters historical processing segments from the historical forging database that correspond to the formation process of the current abnormal change area. Specifically, the system uses the initial formation stage, thermal change process, material migration trend, and subsequent forming response process of the current abnormal area as the filtering criteria, and matches them with forging records of different batches and different stages in the historical processing process to find data segments with the same or similar abnormal formation processes. For example, if there is insufficient material filling in the shoulder area of ​​the current drive shaft, the system analysis shows that the formation process is "increased mold temperature gradient → decreased material plasticity → local flow restriction → insufficient filling". At this time, the system will look for processing segments with the same temperature gradient change, restricted material flow and abnormal shoulder filling from the historical processing records, rather than simply screening data with the same final defect result.

[0029] In this embodiment, step S5, which identifies the parameter change nodes that cause the current abnormal state, analyzes the transmission relationship between the changed parameters during the historical forging process based on the parameter change nodes, and generates the abnormal state evolution path, further includes: S51: Based on the forging process information corresponding to the parameter change nodes, obtain the temporal sequence relationship and the range of influence between each parameter change node; S52: Based on the time sequence relationship and the range of influence, determine whether the preset influence transmission conditions are met between the parameter change nodes; S53: If so, collect the sequential relationship of the influence of different parameter change nodes on the forging forming result, identify the influence transmission direction between each parameter change node, analyze the coordinated change process between control parameters in the historical forging process based on the influence transmission direction, generate the key response links that the parameter change passes through in the process of transmission from the initial disturbance to the regional forming anomaly, and establish the mapping relationship between the parameter change node and the forging state response characteristics. The control parameters specifically include temperature control parameters, loading parameters, deformation control parameters and cooling adjustment parameters.

[0030] In this embodiment, the system obtains the temporal sequence relationship and influence range between parameter change nodes based on the forging process information corresponding to the parameter change nodes. Then, the system determines whether the parameter change nodes meet the pre-set influence transmission conditions based on the temporal sequence relationship and influence range, and executes the corresponding steps accordingly. For example, when the system determines that the pre-set influence transmission conditions cannot be met between these parameter change nodes, the system will consider that although the currently identified parameter change nodes have a certain temporal change relationship in the forging process, there is no clear continuous influence process between different parameter changes, and it cannot be proven that the previous parameter change will directly or indirectly cause the forging state deviation corresponding to the subsequent parameter change. The system will reduce the reference weight of the parameter change nodes that do not meet the influence transmission conditions in the abnormal cause analysis, and obtain other relevant forging process data in that stage based on the processing stage information corresponding to the abnormal change area, including equipment execution status, material deformation status, mold working status, and area forming performance. For example, when the system determines that the pre-set influence transmission conditions can be met between these parameter change nodes, the system will consider that the current abnormal change area is caused by the identified The parameter change nodes gradually form according to the expected transmission path. The system collects the sequential relationship of the impact of different parameter change nodes on the forging forming result, identifies the direction of influence transmission between each parameter change node, and analyzes the coordinated change process between control parameters in the historical forging process based on these influence transmission directions. The control parameters specifically include temperature control parameters, loading parameters, deformation control parameters, and cooling adjustment parameters. The system generates key response links in the process of parameter change from initial disturbance to regional forming anomaly, and establishes a mapping relationship between parameter change nodes and forging state response characteristics. By analyzing the temporal sequence relationship and the scope of influence between parameter change nodes, the system clarifies the transmission direction of different process parameter changes in the forging forming process, avoiding the problem that traditional methods cannot explain the problem of multiple stages and multiple parameters working together by judging the cause of anomalies based on a single parameter change. At the same time, by identifying the influence transmission relationship between each parameter change node and analyzing the coordinated change process between temperature control parameters, loading parameters, deformation control parameters, and cooling adjustment parameters, the system can determine the key response links in the process from initial process disturbance to regional forming anomaly, and clarify the main influence path in the formation process of the anomaly.

[0031] It should be noted that the sequential relationship between the impact of different parameter change nodes on the forging forming result is collected, the direction of influence transmission between the parameter change nodes is identified, and based on the direction of influence transmission, the coordinated change process between control parameters in the historical forging process is analyzed. Key response links are generated during the transmission of parameter changes from initial disturbance to regional forming anomalies, and a mapping relationship is established between the parameter change nodes and the forging state response characteristics. Specifically: The system first collects the sequence of influence of different parameter change nodes on the forging result based on the occurrence time, duration, and corresponding changes in the forging state during the forging process. Specifically, the system does not simply record whether a parameter has changed, but analyzes whether the forging state changes accordingly in subsequent stages after a parameter change occurs, thereby determining the order of influence of the parameter change on the forming result. For example, during the forging process in the transition area of ​​the drive shaft shoulder, the system detects that the die temperature begins to decrease in the third processing stage, followed by a decrease in material flow rate in the fourth processing stage, and localized insufficient filling in the fifth processing stage. Based on the time sequence of these changes, the system determines that the die temperature change precedes the material flow change, and the material flow change further affects the final forming result. After obtaining the sequential influence relationship between the nodes of parameter change, the system further identifies the direction of influence transmission between different parameter change nodes. Specifically, the system determines whether a change in a certain parameter will become a triggering factor for another parameter change or an abnormal state in a region, based on the time sequence of parameter changes, the range of the affected area, and the corresponding changes in forging state. For example, if the system finds that the die temperature decrease occurs before the abnormal material flow in the shoulder area, and the range of the temperature decrease highly overlaps with the area of ​​abnormal material flow, then the die temperature change is determined to be the preceding influencing node, and the change in material flow state is the subsequent response node, forming the influence transmission direction of "die temperature change → abnormal material flow → local forming deviation". Subsequently, based on the direction of influence transmission, the system analyzes the coordinated changes of control parameters during the historical forging process. Since temperature control parameters, loading parameters, deformation control parameters, and cooling adjustment parameters typically interact during drive shaft forging, the system extracts the changes of different control parameters during the anomaly formation process from historical processing data, analyzing whether multiple parameters change simultaneously and the synergistic effects of these changes. For example, the system analysis of historical forging data reveals that when the mold temperature decreases, the equipment automatically increases the loading pressure. However, due to the decrease in material plasticity, the pressure increase cannot effectively improve material flow, ultimately leading to insufficient filling in the shoulder area. In this case, the system records the coordinated changes between temperature control parameters and loading parameters and determines the combined effect of both on anomaly formation. Furthermore, based on the coordinated changes in control parameters during historical forging processes, the system generates key response stages as parameter changes propagate from initial disturbances to regional forming anomalies. These key response stages describe the state transition processes at different stages of anomaly formation. For example, for the journal region dimensional deviation problem, the key response stages analyzed by the system could be: "abnormal cooling regulation → change in local temperature distribution → increased material shrinkage → regional dimensional shift"; for the shoulder region insufficient filling problem, it could be: "decreased die temperature → decreased material flowability → decreased local deformation coordination → insufficient filling". By generating key response stages, the system can clearly identify that the anomaly is not directly caused by a single parameter, but rather gradually formed after multiple control parameters act continuously. Finally, based on the key response links, the system establishes a mapping relationship between parameter change nodes and forging state response characteristics. This mapping relationship is used to describe which state characteristics of the forging will change due to different parameter changes, such as establishing corresponding relationships like "decrease in forging pressure - decrease in local deformation", "increase in temperature gradient - shift in material flow direction", and "change in cooling rate - change in microstructure evolution trend".

[0032] In this embodiment, step S2, which determines whether the predicted forging state can meet the preset forging quality requirements, further includes: S21: Based on the state description information corresponding to the predicted forging state, extract the quality risk distribution of the predicted forging state in different regions, wherein the quality risk distribution specifically includes quality risk indicators, performance deviation degree and defect formation tendency; S22: Determine whether the quality risk distribution meets the preset risk control conditions; S23: If so, identify the risk clustering area corresponding to the quality risk distribution, analyze the performance offset direction corresponding to the risk clustering area, and based on the performance offset direction, track the forging characteristics corresponding to the risk clustering area in the predicted forging process to determine the key influencing links of quality risk. The forging characteristics specifically include material flow state, heat exchange process and force transmission.

[0033] In this embodiment, the system extracts the quality risk distribution of the predicted forging state in different regions based on the state description information corresponding to the predicted forging state. The quality risk distribution specifically includes quality risk indicators, performance deviation degree, and defect formation tendency. The system then determines whether these quality risk distributions meet pre-set risk control conditions to execute corresponding steps. For example, if the system determines that the quality risk distribution of the predicted forging state in a certain region does not meet the pre-set risk control conditions, the system considers that this region has a high quality deviation risk in the subsequent forging process, and the currently predicted forging state cannot guarantee that this region will continue to meet the target forming requirements. The system will analyze the main influencing factors leading to the increased risk in this region based on the quality risk indicators, performance deviation degree, and defect formation tendency, such as determining whether the risk is caused by insufficient forging pressure, abnormal mold temperature, obstructed material flow, or unreasonable cooling process. Conversely, if the system determines that the quality risk distribution of the predicted forging state in a certain region meets the pre-set risk control conditions, the system... If the system determines that the quality risk indicators for a given region exceed the preset allowable range, or that the performance deviation is gradually increasing, indicating a significant tendency to form defects, it suggests that the current combination of process parameters may not be able to effectively control the forming process in that region. The system will identify the risk clusters corresponding to these quality risk distributions, analyze the performance deviation direction corresponding to each risk cluster, and track the forging characteristics of each risk cluster during the predicted forging process based on different performance deviation directions. These forging characteristics specifically include the material flow state, heat exchange process, and force transmission, thus identifying the key influencing factors of quality risk. By identifying the risk clusters corresponding to the quality risk distribution and analyzing the performance deviation direction corresponding to different risk regions, the system can clarify the development trend of quality risks in different regions. At the same time, by tracking the material flow state, heat exchange process, and force transmission of each risk cluster during the predicted forging process, the system can further identify the key factors affecting the formation of quality risks, clarifying whether the quality deviation is caused by abnormal heat distribution, restricted material flow, or uneven force transmission.

[0034] It should be noted that identifying the risk clustering regions corresponding to the quality risk distribution, analyzing the performance offset direction corresponding to the risk clustering regions, and based on the performance offset direction, tracking the forging characteristics corresponding to the risk clustering regions in the predicted forging process to determine the key influencing factors of quality risk, specifically: The system acquires quality risk indicators, performance deviations, and defect formation tendencies corresponding to each state region, compares and analyzes the risk levels of different regions, and determines whether quality risks are concentrated in a certain structural location. Since different regions of the drive shaft undertake different forming tasks during forging—for example, the journal region mainly affects dimensional accuracy, the shoulder transition region mainly affects material continuity and stress transfer, and the main load-bearing region mainly affects overall load-bearing performance—the system identifies regions with higher risks and a continuous impact trend as risk clustering regions based on the risk performance of each region. For instance, if the performance deviation of the shoulder transition region is found to be significantly higher than other regions during the prediction process, and this region also shows a tendency for insufficient material filling, the system identifies the shoulder transition region as a quality risk clustering region and uses it as a key focus for subsequent analysis. After identifying the risk cluster area, the system further analyzes the corresponding performance deviation direction to determine the development trend of the quality state of the area deviating from the normal forming state. Specifically, the system combines the predicted changes in the regional state during the forging process to analyze whether the performance change in the area is developing in the direction of dimensional deviation, deterioration of microstructure and properties, abnormal local deformation, or abnormal material flow. For example, if the system detects that the local deformation of the shoulder transition area gradually increases during the continuous forging stage, while the material flow range gradually shrinks, then the performance deviation direction of the area is determined to be "deterioration of deformation coordination and restricted material flow." If the temperature drops too quickly in the main load-bearing area, accompanied by abnormal microstructure changes, then the performance deviation direction of the area is determined to be "microstructure and property deviation." Subsequently, based on the performance deviation direction corresponding to different risk accumulation areas, the system reversely tracks the changes in forging characteristics of those areas during the predicted forging process. These forging characteristics include material flow state, heat exchange process, and force transmission. Specifically, the system analyzes the material migration in the risk area at different processing stages to determine if there are issues such as insufficient material flow or flow direction deviation. Simultaneously, it analyzes the heat input, heat diffusion, and temperature gradient changes during forging to determine if heat changes affect the forming state of the area. Furthermore, the system analyzes the load transmission process between different structural areas to determine if there are issues of force concentration or localized insufficient force. For example, regarding the filling risk in the shoulder transition area, the system tracking revealed that during the predicted forging process, the heat distribution in this area was normal in the early stages. However, as processing continued, the die temperature decreased, leading to a decrease in material plasticity. Subsequently, the material flow rate decreased, and the force transmission concentrated in the adjacent area, preventing the shoulder from receiving sufficient material replenishment. At this point, the system can determine that the risk is not simply caused by insufficient final filling, but by abnormal heat exchange affecting material flow and further altering the continuous impact of the force transmission process. Finally, based on the tracked material flow state, heat exchange process, and force transmission, the system identifies the key influencing factors leading to quality risks. Specifically, the system analyzes the correlation between changes in different forging characteristics and risk formation, and filters out the factors that have the main impact on quality risks. For example, if the analysis finds that the quality risk in the shoulder area mainly stems from the decrease in material flowability due to the reduction in mold temperature, rather than insufficient forging pressure, then the system identifies "abnormal heat exchange leading to restricted material flow" as the key influencing factor. If the risk in the main load-bearing area is found to be mainly caused by uneven force distribution during loading, then "abnormal force transmission" is identified as the key influencing factor.

[0035] In this embodiment, step S4, which determines whether the historical forging state data meets the preset state association conditions, further includes: S41: Obtain the degree of process behavior reproducibility corresponding to the historical forging state data; S42: Determine whether the degree of reproduction of the process behavior meets the preset reproduction requirements; S43: If so, based on the processing action sequence of the degree of reproducibility of the process behavior, identify the execution connection mode between different processes in the historical forging process, divide the main processing links in which the forging shape of the transmission shaft changes, obtain the equipment control behavior of the processing terminal in the forging process, collect the forging forming effect of the equipment control behavior on the transmission shaft, and based on the forging forming result, screen the historical forming cases corresponding to the current abnormal change area.

[0036] In this embodiment, the system acquires the degree of process behavior reproducibility corresponding to historical forging state data, and then determines whether the degree of process behavior reproducibility meets the preset reproduction requirements in order to execute the corresponding steps. For example, when the system determines that the degree of process behavior reproducibility corresponding to a certain segment of historical forging state data cannot meet the preset reproduction requirements, the system will consider that the historical data may only be consistent in terms of the final forming result, local state performance, or changes in certain process parameters, but there are differences in the development process of key process behaviors, the order of parameter changes, and the regional response process. The system will analyze the differences in the forging process between the current abnormal change area and the historical data, including the order of process parameter changes, the duration of parameter changes, the regional state response process, and the abnormal formation stage. For example, when the system determines that the degree of process behavior reproducibility corresponding to a certain segment of historical forging state data can meet the preset reproduction requirements, the system will consider that the segment of historical forging state data is close to the state characteristics of the current abnormal change area, and its corresponding forging process can truly reproduce the current abnormal formation process, and can be used as the cause of the current abnormal state. As a valid reference for analysis, the system identifies the execution connection methods between different processes in the historical forging process based on the processing action sequence with the degree of reproducibility of the process behavior. It divides the main processing links in which the forging shape of the drive shaft changes, obtains the equipment control behavior of the processing terminal in the forging process, collects the effect of these equipment control behaviors on the forging formation of the drive shaft, and selects historical forming cases corresponding to the current abnormal change area based on different forging formation results. By identifying the execution connection methods between different processes in the historical forging process based on the processing action sequence corresponding to the degree of reproducibility of the process behavior, and dividing the main processing links in which the forging shape of the drive shaft changes, the system can reconstruct the influence relationship between each processing stage in the formation of the abnormal state, and clarify under which process, processing node, and equipment control behavior the abnormality gradually forms, thereby improving the ability to analyze the abnormality formation process. At the same time, by obtaining the equipment control behavior of the processing terminal in the forging process and analyzing the impact of these control behaviors on the forging formation effect of the drive shaft, the system can establish the correspondence between the equipment control process and the forging formation result.

[0037] It should be noted that, based on the processing action sequence of the reproducibility of the aforementioned process behavior, the execution connection mode between different processes is identified from the historical forging process, the main processing links in which the forging shape of the drive shaft changes are divided, the equipment control behavior of the processing terminal in the forging process is obtained, the effect of the equipment control behavior on the forging forming effect of the drive shaft is collected, and based on the forging forming result, historical forming cases corresponding to the current abnormal change area are selected, specifically: The system first performs process analysis on historical forging state data that meets the reproduction requirements based on the processing action sequence corresponding to the degree of process behavior reproducibility. The processing action sequence is used to describe the sequence, duration, and action changes of various operations performed by the processing terminal during the historical forging process, such as loading actions, pressure holding actions, die adjustment actions, and cooling control actions of the forging equipment. By analyzing the execution relationship of these processing actions at different time stages, the system identifies the connection mode between different processes in the historical forging process and clarifies the continuous influence relationship between each processing link. For example, when an abnormality of insufficient material filling occurs in the shoulder area of ​​the drive shaft, the system acquires a piece of historical processing data. The processing action sequence shows that: first, the mold preheating operation is performed, followed by rapid loading forging, and after the initial deformation is completed, a second loading is performed, and finally, cooling control is performed. The system analysis found that in this historical processing, the rapid loading stage was entered immediately after the mold preheating was completed, which caused a correlation between the material temperature change and the deformation process. Therefore, the process connection mode of "preheating stage → initial loading stage → secondary forming stage → cooling stage" was determined to be "preheating stage → initial loading stage → secondary forming stage → cooling stage". Subsequently, the system divides the main processing steps that cause changes in the forging shape of the drive shaft according to the execution connection relationship between different processes. Since the shape changes of different structural areas of the drive shaft usually occur in specific processing stages, the system determines the key links affecting the formation of the forging shape based on the dimensional changes, material flow changes, and local deformation conditions corresponding to each stage. For example, during the processing of the journal area, the system found that the dimensional changes mainly occur in the initial loading stage, while the material filling changes in the shoulder transition area mainly occur in the secondary forming stage. Therefore, the initial loading stage is classified as the journal shape forming stage, and the secondary forming stage is classified as the shoulder shape adjustment stage. After completing the division of the main processing stages, the system further acquires the equipment control behavior of the processing terminal in each forging stage. The equipment control behavior is used to describe the actual execution state of the processing equipment in the forging process, including the loading speed change, pressure application process, mold temperature adjustment process, equipment response process, and cooling control process. For example, the system analyzes historical processing data and finds that in the forming process of the shoulder area, the processing equipment first applies the load at a low speed, then quickly increases the loading pressure, and maintains it for a certain period of time after reaching the target pressure. This loading process is then regarded as an important equipment control behavior affecting the forming of the shoulder. Furthermore, the system collects the impact of these equipment control behaviors on the forming effect of the drive shaft forging, and performs correlation analysis between the equipment control process and the final forging state. Specifically, the system analyzes the size changes, morphological changes, material filling conditions, and performance of the corresponding areas after the execution of different control behaviors, and determines the correspondence between equipment control behaviors and forging results. For example, the system finds that when the loading speed increases too quickly, the material flow in the shoulder area is insufficient, which easily leads to local filling defects; while when the mold temperature is kept within a reasonable range, the material flow is more uniform, and the forming quality of the shoulder area is better. Thus, a correspondence is established between equipment loading behavior, temperature control behavior, and forging results. Finally, the system filters historical forming cases based on different historical forging formation results, selecting cases that correspond to the current abnormal change area formation process as reference objects. During the screening process, the system not only compares the defect types of the final forgings, but also combines the processing links, equipment control behavior, and regional state change process for comprehensive matching. For example, when the current drive shaft shoulder area has a local filling insufficiency anomaly, the system will not directly filter all historical cases with filling insufficiency, but will give priority to historical forming cases that have also experienced "abnormal temperature change → restricted material flow → insufficient shoulder filling".

[0038] In this embodiment, step S1, which identifies the structural characteristics of the drive shaft based on the forging change state data pre-acquired by the processing terminal, further includes: S11: Extract the structural forming feature information corresponding to the structural part, wherein the structural forming feature information specifically includes cross-sectional transition relationship, force transmission path, material filling requirements and local forming constraint information; S12: Determine whether the structural forming feature information meets the preset forming sensitivity conditions; S13: If so, then based on the cross-sectional transition relationship, identify the degree of geometric change of the transmission shaft between different connection areas, divide the structural transition positions that cause material flow obstruction, collect the transmission mode of external loading during forging between different structural areas according to the force transmission path, and determine the key load-bearing area that bears the concentrated load.

[0039] In this embodiment, the system extracts structural forming feature information corresponding to the structural parts. This structural forming feature information specifically includes cross-sectional transition relationships, force transmission paths, material filling requirements, and local forming constraint information. Then, it determines whether these structural forming feature information meets pre-set forming sensitivity conditions to execute corresponding steps. For example, when the system determines that the structural forming feature information corresponding to a certain structural part of the drive shaft cannot meet the pre-set forming sensitivity conditions, the system considers that the cross-sectional transition relationships, force transmission paths, material filling requirements, and local forming constraint information of that area do not exhibit obvious forming risk characteristics. Even if there are temperature fluctuations, pressure changes, or material flow differences within a certain range during forging, it will not directly lead to significant morphological deviations or quality defects in that area. The system will perform routine state monitoring on this structural part, continuously collecting information on dimensional changes, deformation states, and forming results during the processing process to avoid ignoring potential state changes in subsequent processing stages due to low current sensitivity. For example, when the system determines that the structural forming feature information corresponding to a certain structural part of the drive shaft can meet the pre-set forming sensitivity conditions, the system... The system identifies the structural component as a critical sensitive area prone to significant forming deviations due to morphological changes during the current forging process. Based on cross-sectional transitions, the system determines the degree of geometric change between different connection areas of the drive shaft, identifies structural transition locations causing material flow obstruction, and, according to the force transmission path, collects data on the transmission methods of external loading during forging between different structural areas to determine the critical load-bearing areas subjected to concentrated loads. By judging whether the structural forming characteristics meet the forming sensitivity conditions, the system further analyzes structural components that meet these conditions. This avoids the problem of applying a uniform control strategy to all areas in traditional forging processes, allowing the system to prioritize areas with complex structural changes and high quality risks, thus improving the targeting of process optimization. Furthermore, by combining the material flow obstruction locations and concentrated load areas for comprehensive analysis, the system can more accurately determine the main causes of quality deviations in structurally sensitive areas, establish the correlation between structural features and forging forming response, and provide reliable data support for subsequent adjustments to key process parameters, local area optimization, and forging quality control, thereby improving the stability and forming accuracy of the drive shaft forging process.

[0040] It should be noted that, based on the aforementioned cross-sectional transition relationship, the degree of geometric change of the drive shaft between different connection areas is identified, structural transition positions that cause material flow obstruction are delineated, and based on the aforementioned force transmission path, the transmission mode of external loading during forging is collected between different structural areas to determine the key load-bearing areas subjected to concentrated loads, specifically: The system acquires the cross-sectional dimension changes, transition continuity, and local shape changes between various structural parts of the drive shaft, and analyzes whether there are obvious abrupt changes or transition changes between adjacent areas. When the cross-sectional change of a certain connection area is large, the system believes that the area needs to complete a large material redistribution during the forging process, and the material flow path is easily restricted by the structure, resulting in problems such as insufficient local filling and flow direction deviation. For example, at the connection between the journal and shoulder of the drive shaft, the diameter of the journal area is small, while the cross-section of the shoulder area suddenly increases. During the forging process, the material needs to migrate from the main body area to the shoulder. If the cross-sectional change at this transition position is too concentrated, the material is prone to flow stagnation when constrained by the mold, making it impossible for the shoulder area to be fully filled. Therefore, based on the cross-sectional transition relationship, the system identifies this connection position as a potential material flow stagnation area and records its corresponding degree of geometric change, providing a basis for subsequent analysis of forming risks. Furthermore, based on the identified degree of geometric change, the system divides different structural transition locations and identifies key structural transition regions that may affect material flow. Specifically, the system divides the drive shaft into regions based on the cross-sectional change range of each region, material flow requirements, and local forming space, marking locations with higher material flow restriction as key analysis areas. For example, the main load-bearing area of ​​the drive shaft has a relatively stable material flow path due to its small continuous cross-sectional change, and the system can classify it as a normal forming area; while the shoulder transition area has a significant cross-sectional change, requiring the material to quickly change its flow direction, and is therefore classified as a material flow sensitive area. Subsequently, based on the force transmission path, the system analyzes the transmission mode of external loading between different structural regions during the forging process. Specifically, the system acquires information on the pressure, impact, and deformation load applied by the forging equipment, analyzes the process of these external forces being transmitted from the loading position to different structural regions of the drive shaft, and determines the load-bearing situation in different regions. Since the drive shaft needs to withstand torsional loads and axial loads during use, the force distribution formed during the forging stage will directly affect subsequent performance. For example, when forging the main body area of ​​the drive shaft, the pressure applied by the equipment first acts on the mold contact area, and then is transmitted through the material to the shoulder and journal connection position. If the system detects that the shoulder transition area bears a large load transmission, and there is a significant cross-sectional change in this area, it is considered that this area not only has material flow constraints, but also has a high risk of stress concentration, and therefore it is identified as a critical load-bearing area. Finally, based on the force transmission in different structural regions, the system determines the key load-bearing areas subjected to concentrated loads. Specifically, the system comprehensively analyzes the intensity of external loading, load transmission paths, and regional structural characteristics to identify the locations that bear the main load conversion and transmission during the forging process. For example, the area connecting the shoulder and the main body needs to complete the cross-sectional transition and bear the load transmitted from the main body area. Therefore, the system identifies this location as a key load-bearing area and focuses on the pressure changes, deformation coordination, and microstructure changes in this area during subsequent process optimization.

[0041] Reference Appendix Figure 2 A process parameter optimization system for a drive shaft forging, as described in one embodiment of the present invention, includes: The construction module 10 is used to identify the structural characteristics of the transmission shaft based on the forging change state data pre-acquired by the processing terminal, divide the transmission shaft into several state regions according to the structural characteristics, and construct the predicted forging state of the transmission shaft after a preset time period. The forging change state data specifically includes the forging pressure, impact speed, die temperature, equipment response time and equipment operating parameters generated by the forging equipment, and the state regions specifically include the journal region, the shoulder transition region and the main body bearing region. The judgment module 20 is used to determine whether the predicted forging state can meet the preset forging quality requirements; The execution module 30 is used to identify abnormal areas that have changed during the processing if not, extract the change features corresponding to the abnormal areas, match the change features with the preset forming rules of each area, dynamically divide the abnormal change areas of the transmission shaft, extract the historical forging state data associated with the abnormal change areas, and construct the corresponding regional state change correlation relationship. The change features specifically include the location of the abnormality, the duration of the abnormality, the magnitude of the change, and the direction of the change. The second judgment module 40 is used to determine whether the historical forging state data meets the preset state association conditions; The second execution module 50 is used to identify, if a condition is met, the parameter change node that caused the current abnormal state, and based on the parameter change node, analyze the transmission relationship between the changed parameters in the historical forging process, generate an abnormal state evolution path, obtain the state change process of the abnormal change area caused by the change of process parameters, collect the influencing factors of the current abnormal state according to the abnormal state evolution path, generate the corresponding key process parameter set and parameter influence weight according to the influencing factors, and establish the process parameter adjustment constraint relationship of the abnormal change area. The changed parameters specifically include temperature change, pressure change, deformation degree change and material flow state, and the influencing factors specifically include main process factors and multi-parameter coupling factors.

[0042] In this embodiment, the construction module 10, based on the forging change state data of the drive shaft pre-acquired by the processing terminal, specifically including the forging pressure, impact velocity, die temperature, equipment response time, and equipment operating parameters generated by the forging equipment, identifies the structural characteristics of the drive shaft. Based on these structural characteristics, the drive shaft is divided into several state regions, specifically including the journal region, shoulder transition region, and main bearing region. The system constructs a predicted forging state of the drive shaft after a pre-set time period. Then, the judgment module 20 determines whether the predicted forging state meets the pre-set forging quality requirements to execute the corresponding steps. For example, when the system determines that the predicted forging state of the drive shaft after a pre-set time period... When the state meets the preset forging quality requirements, the system assumes that the trend of process parameter changes during the current forging process can keep the transmission shaft in stable formation across its various state regions. Based on the forming results of each state region corresponding to the predicted forging state, the system records the effective control range of the current forging pressure, impact velocity, die temperature, equipment response time, and equipment operating parameters, storing this set of process parameters as a stable processing state sample. For example, if the system determines that the predicted forging state of the transmission shaft after a preset time period cannot meet the preset forging quality requirements, the execution module 30 will consider that the current combination of forging process parameters lacks the sustainability to meet the target quality requirements and needs to execute immediately. During parameter adjustment operations, the system identifies abnormal areas that have changed during processing, extracts the corresponding change characteristics of these abnormal areas, including the location of the abnormality, duration of the abnormality, amplitude of the change, and direction of the change. Different change characteristics are matched with pre-set forming rules for each area to dynamically divide the abnormal change areas of the drive shaft. Historical forging state data associated with these abnormal change areas is extracted to construct corresponding regional state change correlations. Then, the second judgment module 40 judges whether the historical forging state data associated with these abnormal change areas meets the pre-set state correlation conditions to execute the corresponding steps. For example, when the system determines that a certain part is associated with an abnormal change area… If the associated historical forging state data does not meet the pre-set state association conditions, the system will consider that there is not enough state correspondence between the historical forging state data and the current abnormal change area, and it cannot accurately reflect the formation process of the current abnormal state. The system will mark the historical forging state data as low correlation data and reduce its reference weight in the abnormal state analysis process. At the same time, based on the abnormal occurrence location, change direction, duration and change magnitude of the current abnormal change area, the system will expand the historical data retrieval scope again and screen historical forging process data with a higher degree of matching with the current abnormal state from different processing stages, different structural areas and different process conditions.For example, when the system determines that a portion of historical forging state data associated with an abnormal change area meets pre-set state association conditions, the second execution module 50 will consider that directly using this portion of historical data for anomaly cause analysis will not cause deviations in the judgment of process parameter influence, and subsequent optimization schemes can still address the current forging problem. The system will identify the parameter change nodes that cause the current abnormal state, and based on these parameter change nodes, analyze the transmission relationship between the changing parameters during the historical forging process. The changing parameters specifically include temperature changes, pressure changes, deformation degree changes, and material flow state, generating an abnormal state evolution path to obtain the state change process of the abnormal change area caused by process parameter changes. According to each abnormal state evolution path, the influencing factors of the current abnormal state are collected. The influencing factors specifically include main process factors and multi-parameter coupling factors. Based on different influencing factors, a corresponding set of key process parameters and parameter influence weights are generated (the set of key process parameters is used to characterize the process parameters that need to be adjusted first, and the parameter influence weights are used to characterize the degree of influence of different process parameters on the improvement effect of the abnormal state), establishing the process parameter adjustment constraint relationship for the abnormal change area.

[0043] In this embodiment, the execution module further includes: The acquisition unit is used to acquire the feature change sequence of the abnormal change region during the continuous processing stage based on the change characteristics corresponding to the abnormal change region. The judgment unit is used to determine whether the change trend in the feature change sequence meets the preset forming deviation condition; The execution unit is configured to, if so, obtain the deviation development process of the abnormal change region according to the change amplitude and direction corresponding to each processing stage in the feature change sequence, associate the change features of each stage according to the order of processing stages, identify the key change stage that causes the abnormal change region to continuously deviate from the forming law, extract the process change factors affecting the forming state of the transmission shaft in the processing stage through the regional state change information corresponding to the key change stage, and establish the coupling relationship between the abnormal change region and the process parameter change.

[0044] In this embodiment, the system acquires the characteristic change sequence of the abnormal change region within the continuous processing stage based on the change characteristics corresponding to these abnormal change regions. The system then determines whether the change trend in these characteristic change sequences meets the pre-set forming deviation conditions, and executes the corresponding steps accordingly. For example, when the system determines that the characteristic change sequence of the abnormal change region within the continuous processing stage does not meet the pre-set forming deviation conditions, the system considers the currently detected change to be a short-term deviation caused by equipment operation disturbances, instantaneous material fluctuations, or changes in the processing environment during the forging process. This deviation is not expected to continuously affect the subsequent forming quality of the drive shaft. The system temporarily marks the abnormal change region, retains the corresponding characteristic change sequence as process monitoring data, and reduces the risk level of the current abnormal state in that region. Based on the continuous collection of abnormal occurrence data in the subsequent processing stages of that region... Information such as location, magnitude of change, recovery status, and regional forming performance is continuously tracked. For example, when the system determines that the characteristic change sequence of abnormal change features in the continuous processing stage can meet the pre-set forming deviation conditions, the system will consider that there is a certain degree of state fluctuation in the current abnormal change area, and the fluctuation has formed a continuous forming deviation trend. The system will obtain the deviation development process of the abnormal change area according to the change magnitude and direction corresponding to each processing stage in these characteristic change sequences. According to the order of processing stages, the change features of each stage are correlated to identify the key change stages that cause the abnormal change area to continuously deviate from the forming law. Through the regional state change information corresponding to different key change stages, the process change factors affecting the forming state of the drive shaft in the processing stage are extracted, and the coupling relationship between the abnormal change area and the process parameter changes is established.

[0045] In this embodiment, it also includes: The extraction module is used to extract the forming deviation information of the abnormal change region in the continuous forging stage based on the processing response information corresponding to the abnormal change region. The forming deviation information specifically includes the heat transfer trajectory, material flow trajectory and strain accumulation characteristics. The third judgment module is used to determine whether the forming deviation process corresponding to the forming deviation information matches the preset historical traceability conditions. The third execution module is used to, if so, determine the initial formation stage of the deviation phenomenon in the abnormal change area based on the local concentration trend in the forming deviation information, and reverse analyze the forging forming response characteristics in the subsequent forging stage based on the processing node information corresponding to the initial formation stage, and screen historical processing segments that correspond to the current abnormal change area formation process. The local concentration trend specifically includes the temperature gradient change law and the plastic migration direction, and the forging forming response characteristics specifically include the load response process, the degree of regional deformation coordination, and the microstructure evolution trend.

[0046] In this embodiment, the system extracts forming deviation information of the abnormal change region during the continuous forging stage based on the processing response information corresponding to the abnormal change region. The forming deviation information specifically includes the heat transfer trajectory, material flow trajectory, and strain accumulation characteristics. The system then determines whether the forming deviation process corresponding to this forming deviation information matches pre-set historical tracing conditions to execute the corresponding steps. For example, if the system determines that the forming deviation process corresponding to the forming deviation information of a certain abnormal change region during the continuous forging stage cannot match the pre-set historical tracing conditions, the system will consider that the current formation process of the abnormal change region differs significantly from the existing abnormal formation patterns in the historical forging process. The existing historical forging state data cannot accurately explain the cause of the abnormal region's formation. The system will then re-acquire the heat input, material flow state, local deformation, and other relevant data within the processing stage corresponding to the abnormal region. Real-time information such as equipment execution status is used to independently analyze the current anomaly formation process. For example, when the system determines that the forming deviation process corresponding to the forming deviation information of a certain abnormal change area in the continuous forging stage can match the pre-set historical traceability conditions, the system will consider that the anomaly is caused by factors such as temperature changes, pressure fluctuations, and abnormal material flow. Based on the local concentration trend in these forming deviation information, which specifically includes the temperature gradient change law and the direction of plastic migration, the system will determine the initial formation stage of the deviation phenomenon in the abnormal change area. Based on the processing node information corresponding to the initial formation stage, the system will reverse analyze the forging forming response characteristics in the subsequent forging stages. The forging forming response characteristics specifically include the load response process, the degree of regional deformation coordination, and the microstructure evolution trend. The system will then select historical processing segments that correspond to the current abnormal change area formation process.

[0047] In this embodiment, the second execution module further includes: The second acquisition unit is used to acquire the temporal sequence relationship and the range of influence between the parameter change nodes based on the forging process information corresponding to the parameter change nodes; The second judgment unit is used to determine whether the preset influence transmission conditions are met between the parameter change nodes based on the time sequence relationship and the range of influence. The second execution unit is used to, if so, collect the sequential relationship of the influence of different parameter change nodes on the forging forming result, identify the influence transmission direction between each parameter change node, analyze the coordinated change process between control parameters in the historical forging process based on the influence transmission direction, generate the key response links passed through by the parameter change from the initial disturbance to the regional forming anomaly, and establish the mapping relationship between the parameter change nodes and the forging state response characteristics. The control parameters specifically include temperature control parameters, loading parameters, deformation control parameters and cooling adjustment parameters.

[0048] In this embodiment, the system obtains the temporal sequence relationship and influence range between parameter change nodes based on the forging process information corresponding to the parameter change nodes. Then, based on this temporal sequence relationship and influence range, the system determines whether the parameter change nodes meet the pre-set influence transmission conditions and executes the corresponding steps. For example, when the system determines that these parameter change nodes cannot meet the pre-set influence transmission conditions, the system considers that although the currently identified parameter change nodes have a certain temporal relationship during the forging process, there is no clear continuous influence process between different parameter changes. It cannot be proven that the previous parameter change will directly or indirectly cause the forging state shift corresponding to the subsequent parameter change. The system will reduce the reference weight of the parameter change nodes that do not meet the influence transmission conditions in the anomaly cause analysis and obtain the relevant information based on the processing stage information corresponding to the anomaly change area. Other relevant forging process data during the stage include information such as equipment execution status, material deformation status, mold working status, and regional forming performance. For example, when the system determines that the influence transmission conditions between these parameter change nodes can be met, the system will consider that the current abnormal change area is gradually formed by the identified parameter change nodes according to the expected transmission path. The system will collect the sequential relationship of the influence of different parameter change nodes on the forging forming result, identify the influence transmission direction between each parameter change node, and analyze the coordinated change process between control parameters in the historical forging process based on these influence transmission directions. The control parameters specifically include temperature control parameters, loading parameters, deformation control parameters, and cooling adjustment parameters. The system will generate the key response links that the parameter changes pass through in the process of transmission from the initial disturbance to the regional forming anomaly, and establish the mapping relationship between parameter change nodes and forging state response characteristics.

[0049] In this embodiment, the determination module further includes: The extraction unit is used to extract the quality risk distribution of the predicted forging state in different regions based on the state description information corresponding to the predicted forging state. The quality risk distribution specifically includes quality risk indicators, performance deviation degree and defect formation tendency. The third judgment unit is used to determine whether the quality risk distribution meets the preset risk control conditions; The third execution unit is used to identify the risk clustering area corresponding to the quality risk distribution if the condition is met, analyze the performance offset direction corresponding to the risk clustering area, track the forging characteristics corresponding to the risk clustering area in the predicted forging process based on the performance offset direction, and determine the key influencing links of quality risk. The forging characteristics specifically include the material flow state, heat exchange process and force transmission.

[0050] In this embodiment, the system extracts the quality risk distribution of the predicted forging state in different regions based on the state description information corresponding to the predicted forging state. The quality risk distribution specifically includes quality risk indicators, performance deviation degree, and defect formation tendency. Then, the system determines whether these quality risk distributions meet the pre-set risk control conditions to execute the corresponding steps. For example, when the system determines that the quality risk distribution of the predicted forging state in a certain region does not meet the pre-set risk control conditions, the system will consider that there is a high quality deviation risk in the subsequent forging process in that region. The currently predicted forging state cannot guarantee that the region will continue to meet the target forming requirements. The system will analyze the main influencing factors that lead to the increased risk in that region based on the quality risk indicators, performance deviation degree, and defect formation tendency, such as determining whether the risk is caused by forging pressure. Factors such as insufficient force, abnormal mold temperature, obstructed material flow, or unreasonable cooling process can cause quality risks. For example, when the system determines that the quality risk distribution in a certain area of ​​the predicted forging state meets the pre-set risk control conditions, the system will consider that the quality risk index corresponding to that area exceeds the preset allowable range, or the performance deviation gradually expands, indicating a significant tendency to form defects. This suggests that the current combination of process parameters may not be able to effectively control the forming process in that area. The system will identify the risk cluster areas corresponding to these quality risk distributions, analyze the performance deviation direction corresponding to each risk cluster area, and track the forging characteristics corresponding to each risk cluster area in the predicted forging process based on different performance deviation directions. The forging characteristics specifically include the material flow state, heat exchange process, and force transmission, thus determining the key influencing factors of quality risk.

[0051] In this embodiment, the second determination module further includes: The third acquisition unit is used to acquire the degree of process behavior reproducibility corresponding to the historical forging state data; The fourth judgment unit is used to judge whether the degree of reproducibility of the process behavior meets the preset reproducibility requirements; The fourth execution unit is used to, if so, identify the execution connection mode between different processes from the historical forging process based on the processing action sequence of the degree of reproducibility of the process behavior, divide the main processing links in which the forging shape of the transmission shaft changes, obtain the equipment control behavior of the processing terminal in the forging process, collect the forging forming effect of the equipment control behavior on the transmission shaft, and, based on the forging forming result, filter historical forming cases corresponding to the current abnormal change area.

[0052] In this embodiment, the system acquires the degree of process behavior reproducibility corresponding to historical forging state data, and then determines whether the degree of process behavior reproducibility meets the preset reproduction requirements in order to execute the corresponding steps. For example, when the system determines that the degree of process behavior reproducibility corresponding to a certain segment of historical forging state data cannot meet the preset reproduction requirements, the system will consider that the historical data may only be consistent in terms of the final forming result, local state performance, or changes in certain process parameters, but there are differences in the development process of key process behaviors, the order of parameter changes, and the regional response process. The system will analyze the forging process differences between the current abnormal change area and the historical data, including information such as the order of process parameter changes, the duration of parameter changes, the regional state response process, and the abnormal formation stage. For example, when the system determines that the reproducibility of the process behavior corresponding to a certain segment of historical forging state data meets the pre-set reproducibility requirements, the system will consider that the historical forging state data is close to the state characteristics of the current abnormal change area, and the corresponding forging process can truly reproduce the current abnormal formation process, which can serve as an effective reference for the analysis of the cause of the current abnormal state. Based on the processing action sequence of the reproducibility of the process behavior, the system will identify the execution connection mode between different processes in the historical forging process, divide the main processing links in which the forging shape of the drive shaft changes, obtain the equipment control behavior of the processing terminal in the forging process, collect the forging forming effect of these equipment control behaviors on the drive shaft, and select historical forming cases corresponding to the current abnormal change area based on different forging forming results.

[0053] In this embodiment, the construction module further includes: The second extraction unit is used to extract the structural forming feature information corresponding to the structural part, wherein the structural forming feature information specifically includes cross-sectional transition relationship, force transmission path, material filling requirements and local forming constraint information; The fifth judgment unit is used to determine whether the structural forming feature information meets the preset forming sensitivity conditions; The fifth execution unit is used to, if so, identify the degree of geometric change of the transmission shaft between different connection areas according to the cross-sectional transition relationship, divide the structural transition position that causes material flow obstruction, collect the transmission mode of external loading during forging between different structural areas according to the force transmission path, and determine the key bearing area that bears the concentrated load.

[0054] In this embodiment, the system extracts structural forming feature information corresponding to the structural parts. This structural forming feature information specifically includes cross-sectional transition relationships, force transmission paths, material filling requirements, and local forming constraint information. Then, it determines whether these structural forming feature information meets pre-set forming sensitivity conditions to execute corresponding steps. For example, when the system determines that the structural forming feature information corresponding to a certain structural part of the drive shaft cannot meet the pre-set forming sensitivity conditions, the system will consider that the cross-sectional transition relationships, force transmission paths, material filling requirements, and local forming constraint information of that area do not exhibit obvious forming risk characteristics. Even if there are certain temperature fluctuations, pressure changes, or material flow differences during forging, it will not directly lead to significant shape deviations or quality defects in that area. The system will then... Routine condition monitoring is performed on the parts, continuously collecting information on dimensional changes, deformation states, and forming results during the processing. This avoids ignoring potential state changes in subsequent processing stages due to low current sensitivity. For example, when the system determines that the structural forming characteristics of a certain structural part of the drive shaft meet the pre-set forming sensitivity conditions, the system considers that the structural part has a morphological change requirement during the current forging process and belongs to a key sensitive area that is prone to significant forming deviations. Based on the cross-sectional transition relationship, the system identifies the degree of geometric change of the drive shaft between different connection areas, delineates the structural transition positions that cause material flow obstruction, and collects the transmission mode of external loading between different structural areas during forging based on the force transmission path, thus determining the key load-bearing areas that bear concentrated loads.

[0055] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method of optimizing process parameters for a drive shaft forging, characterized by, Includes the following steps: Based on the forging change state data pre-acquired by the processing terminal for the drive shaft, the structural characteristics of the drive shaft are identified. According to the structural characteristics, the drive shaft is divided into several state regions, and the predicted forging state of the drive shaft after a preset time period is constructed. The forging change state data specifically includes the forging pressure, impact speed, die temperature, equipment response time and equipment operating parameters generated by the forging equipment. The state regions specifically include the journal region, the shoulder transition region and the main bearing region. Determine whether the predicted forging condition meets the preset forging quality requirements; If not, then during the processing, identify the abnormal areas that have changed, extract the change features corresponding to the abnormal areas, match the change features with the preset forming rules of each area, dynamically divide the abnormal change areas of the drive shaft, extract the historical forging state data associated with the abnormal change areas, and construct the corresponding regional state change correlation relationship. The change features specifically include the location of the abnormality, the duration of the abnormality, the magnitude of the change, and the direction of the change. Determine whether the historical forging status data meets the preset status association conditions; If the conditions are met, the parameter change nodes that caused the current abnormal state are identified. Based on the parameter change nodes, the transmission relationship between the changed parameters in the historical forging process is analyzed to generate the abnormal state evolution path. The state change process caused by the change of process parameters in the abnormal change area is obtained. According to the abnormal state evolution path, the influencing factors of the current abnormal state are collected. Based on the influencing factors, the corresponding key process parameter set and parameter influence weight are generated. The process parameter adjustment constraint relationship of the abnormal change area is established. The changed parameters specifically include temperature change, pressure change, deformation degree change and material flow state. The influencing factors specifically include main process factors and multi-parameter coupling factors.

2. The process parameter optimization method of a propeller shaft forging according to claim 1, wherein, The step of identifying the state regions that have changed during the processing, extracting the change features corresponding to the state regions, matching the change features with the preset forming rules of each region, and dynamically dividing the abnormal change regions of the transmission shaft further includes: Based on the change characteristics corresponding to the abnormal change region, the characteristic change sequence of the abnormal change region in the continuous processing stage is obtained; Determine whether the trend of change in the feature change sequence meets the preset forming deviation condition; If so, then based on the direction of change of each processing stage in the feature change sequence, the deviation development process of the abnormal change region is obtained. According to the order of processing stages, the change characteristics of each stage are correlated to identify the key change stage that causes the abnormal change region to continuously deviate from the forming law. Through the regional state change information corresponding to the key change stage, the process change factors affecting the forming state of the transmission shaft in the processing stage are extracted, and the coupling relationship between the abnormal change region and the process parameter change is established.

3. The process parameter optimization method of a propeller shaft forging as claimed in claim 1 wherein, Before the step of extracting historical forging state data associated with the abnormal change area and constructing the corresponding regional state change correlation, the method further includes: Based on the processing response information corresponding to the abnormal change region, the forming deviation information of the abnormal change region in the continuous forging stage is extracted, wherein the forming deviation information specifically includes the heat transfer trajectory, material flow trajectory and strain accumulation characteristics. Determine whether the forming deviation process corresponding to the forming deviation information matches the preset historical traceability conditions; If so, based on the local concentration trend in the forming deviation information, the initial formation stage of the deviation phenomenon in the abnormal change area is determined. Based on the processing node information corresponding to the initial formation stage, the forging forming response characteristics in the subsequent forging stage are analyzed in reverse. Historical processing segments that correspond to the current abnormal change area formation process are selected. Specifically, the local concentration trend includes the temperature gradient change law and the plastic migration direction. The forging forming response characteristics specifically include the load response process, the degree of regional deformation coordination, and the microstructure evolution trend.

4. The process parameter optimization method of a propeller shaft forging according to claim 1, wherein, The step of identifying the parameter change nodes that cause the current abnormal state, and based on the parameter change nodes, analyzing the transmission relationship between the changed parameters during the historical forging process to generate the abnormal state evolution path, further includes: Based on the forging process information corresponding to the parameter change nodes, the temporal sequence relationship and the scope of influence between each parameter change node are obtained; Based on the time sequence and the scope of influence, determine whether the preset influence transmission conditions are met between the parameter change nodes; If so, the order of influence of different parameter change nodes on the forging forming result is collected, the direction of influence transmission between parameter change nodes is identified, and based on the direction of influence transmission, the coordinated change process between control parameters in the historical forging process is analyzed, the key response links passed through by the parameter change from the initial disturbance to the regional forming anomaly are generated, and the mapping relationship between the parameter change nodes and the forging state response characteristics is established. The control parameters specifically include temperature control parameters, loading parameters, deformation control parameters and cooling adjustment parameters.

5. The method of process parameter optimization for a propeller shaft forging as claimed in claim 1, wherein, The step of determining whether the predicted forging state can meet the preset forging quality requirements further includes: Based on the state description information corresponding to the predicted forging state, the quality risk distribution of the predicted forging state in different regions is extracted, wherein the quality risk distribution specifically includes quality risk indicators, performance deviation degree and defect formation tendency; Determine whether the quality risk distribution meets the preset risk control conditions; If so, identify the risk clustering area corresponding to the quality risk distribution, analyze the performance deviation direction corresponding to the risk clustering area, and based on the performance deviation direction, track the forging characteristics corresponding to the risk clustering area in the predicted forging process to determine the key influencing links of quality risk. The forging characteristics specifically include material flow state, heat exchange process and force transmission.

6. The process parameter optimization method of a propeller shaft forging according to claim 1, wherein, The step of determining whether the historical forging state data meets the preset state association conditions further includes: Obtain the degree of process behavior reproducibility corresponding to the historical forging state data; Determine whether the degree of reproducibility of the process behavior meets the preset reproducibility requirements; If so, based on the processing action sequence of the degree of reproducibility of the process behavior, the execution connection mode between different processes is identified from the historical forging process, the main processing links in which the forging shape of the drive shaft changes are divided, the equipment control behavior of the processing terminal in the forging process is obtained, the forging forming effect of the equipment control behavior on the drive shaft is collected, and based on the forging forming result, historical forming cases corresponding to the current abnormal change area are selected.

7. The method of process parameter optimization for a propeller shaft forging as claimed in claim 1, wherein, The step of identifying the structural characteristics of the drive shaft based on the forging change state data pre-acquired by the processing terminal further includes: Extract the structural forming feature information corresponding to the structural parts, wherein the structural forming feature information specifically includes cross-sectional transition relationship, force transmission path, material filling requirements and local forming constraint information; Determine whether the structural forming feature information meets the preset forming sensitivity conditions; If so, based on the cross-sectional transition relationship, identify the degree of geometric change of the transmission shaft between different connection areas, divide the structural transition positions that cause material flow obstruction, collect the transmission mode of external loading during forging between different structural areas according to the force transmission path, and determine the key load-bearing area that bears the concentrated load.

8. A process parameter optimization system for a drive shaft forging, characterized in that, include: The construction module is used to identify the structural characteristics of the drive shaft based on the forging change state data pre-acquired by the processing terminal, divide the drive shaft into several state regions according to the structural characteristics, and construct the predicted forging state of the drive shaft after a preset time period. The forging change state data specifically includes the forging pressure, impact velocity, die temperature, equipment response time and equipment operating parameters generated by the forging equipment, and the state regions specifically include the journal region, the shoulder transition region and the main bearing region. The judgment module is used to determine whether the predicted forging state can meet the preset forging quality requirements; The execution module is used to identify abnormal areas that have changed during the processing if not, extract the change features corresponding to the abnormal areas, match the change features with the preset forming rules of each area, dynamically divide the abnormal change areas of the transmission shaft, extract the historical forging state data associated with the abnormal change areas, and construct the corresponding regional state change correlation relationship. The change features specifically include the location of the abnormality, the duration of the abnormality, the magnitude of the change, and the direction of the change. The second judgment module is used to determine whether the historical forging state data meets the preset state association conditions; The second execution module is used to identify the parameter change nodes that cause the current abnormal state if the condition is met. Based on the parameter change nodes, it analyzes the transmission relationship between the changed parameters in the historical forging process, generates an abnormal state evolution path, obtains the state change process of the abnormal change area caused by the change of process parameters, collects the influencing factors of the current abnormal state according to the abnormal state evolution path, generates the corresponding key process parameter set and parameter influence weights according to the influencing factors, and establishes the process parameter adjustment constraint relationship of the abnormal change area. The changed parameters specifically include temperature change, pressure change, deformation degree change and material flow state, and the influencing factors specifically include main process factors and multi-parameter coupling factors.

9. The process parameter optimization system for transmission shaft forgings according to claim 8, characterized in that, The execution module further includes: The acquisition unit is used to acquire the feature change sequence of the abnormal change region during the continuous processing stage based on the change characteristics corresponding to the abnormal change region. The judgment unit is used to determine whether the change trend in the feature change sequence meets the preset forming deviation condition; The execution unit is configured to, if so, obtain the deviation development process of the abnormal change region according to the change amplitude and direction corresponding to each processing stage in the feature change sequence, associate the change features of each stage according to the order of processing stages, identify the key change stage that causes the abnormal change region to continuously deviate from the forming law, extract the process change factors affecting the forming state of the transmission shaft in the processing stage through the regional state change information corresponding to the key change stage, and establish the coupling relationship between the abnormal change region and the process parameter change.

10. The process parameter optimization system for transmission shaft forgings according to claim 8, characterized in that, Also includes: The extraction module is used to extract the forming deviation information of the abnormal change region in the continuous forging stage based on the processing response information corresponding to the abnormal change region. The forming deviation information specifically includes the heat transfer trajectory, material flow trajectory and strain accumulation characteristics. The third judgment module is used to determine whether the forming deviation process corresponding to the forming deviation information matches the preset historical traceability conditions. The third execution module is used to, if so, determine the initial formation stage of the deviation phenomenon in the abnormal change area based on the local concentration trend in the forming deviation information, and reverse analyze the forging forming response characteristics in the subsequent forging stage based on the processing node information corresponding to the initial formation stage, and screen historical processing segments that correspond to the current abnormal change area formation process. The local concentration trend specifically includes the temperature gradient change law and the plastic migration direction, and the forging forming response characteristics specifically include the load response process, the degree of regional deformation coordination, and the microstructure evolution trend.

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