Automatic control management system for machining of vehicle forgings

Abstract

The invention relates to the technical field of forge piece machining control, and particularly discloses an automatic control management system for vehicle forge piece machining, and the system comprises an online monitoring module which is disposed in a forging forming operation area of forge pieces and is used for carrying out the multi-dimensional real-time data collection of the forging forming process; the data processing and analyzing module is used for receiving and preprocessing the multi-dimensional real-time data, comprehensively analyzing the preprocessed multi-dimensional data and generating corresponding forging quality evaluation parameters; the state judgment module is used for comparing the quality evaluation parameters with a preset quality evaluation threshold range; real-time monitoring and self-adaptive optimization of the forging forming process are achieved, the quality threshold value and the sampling inspection frequency can be dynamically adjusted, and multi-dimensional sensing of the forging forming process, precise judgment and classification of the forging quality and self-adaptive optimization of the key threshold value are achieved.

Description

Technical Field

[0001] This invention relates to the field of forging processing control technology, specifically to an automated control and management system for automotive forging processing. Background Technology

[0002] The automated processing flow of automotive forgings typically includes multiple consecutive stages such as blanking, heating, forging, heat treatment, and cleaning and inspection. Among these, the forging stage is the key link that determines the internal structure, mechanical properties, and defect generation of the forging. During this stage, the workpiece undergoes plastic deformation under high temperature and pressure. Any fluctuation in process parameters (such as insufficient forging force, improper deformation, or temperature inaccuracy) may lead to irreversible defects such as folds, cracks, and flow line disorder. These defects are often only discovered in subsequent offline inspections, resulting in huge waste of materials, energy, and time.

[0003] In existing technologies, although simple monitoring instruments may be installed on forging machines, their thresholds are mostly fixed values ​​and cannot be adaptively adjusted according to dynamic factors such as batch differences in materials and mold wear. More importantly, current online monitoring is disconnected from authoritative offline testing (such as ultrasonic testing and metallographic analysis), lacking a closed-loop feedback system to continuously optimize the accuracy of online monitoring using the precise values ​​of offline testing. In addition, the efficiency of existing quality inspection processes is generally low, especially when conducting destructive sampling inspections of finished products. The sampling frequency is often set according to a fixed cycle or proportion and cannot be dynamically adjusted according to the real-time production quality status. This rigid sampling mechanism not only leads to high testing costs but also makes it difficult to accurately capture batch-specific quality fluctuations, resulting in an imbalance between cost and quality control effectiveness. Summary of the Invention

[0004] The purpose of this invention is to provide an automated control and management system for automotive forging processing, which solves the following technical problems: How to overcome the problems of fixed monitoring thresholds and the separation of online and offline detection data in existing technologies.

[0005] The objective of this invention can be achieved through the following technical solutions: An automated control and management system for automotive forging processing, the system comprising: The online monitoring module is deployed in the forging and forming operation area of ​​the forging to collect multi-dimensional real-time data on the forging and forming process; The data processing and analysis module is used to receive multi-dimensional real-time data and preprocess it, and then perform comprehensive analysis on the preprocessed multi-dimensional data to generate corresponding forging quality evaluation parameters. The status determination module is used to compare the quality evaluation parameters with the preset quality evaluation threshold range, and mark the processed forgings as qualified, unqualified and pending states according to the comparison results. The non-destructive testing module is used to perform offline precise data acquisition on forgings marked as pending state using multi-dimensional non-destructive testing methods, obtain multi-dimensional non-destructive testing data of the pending forgings, and after quantitative analysis of the multi-dimensional non-destructive testing data, reclassify and mark the pending forgings as good or defective. The destructive sampling module is used to perform destructive sampling inspections on four types of forgings marked as qualified, good, defective and unqualified according to preset sampling rules, and obtain destructive sampling inspection data for each type of forging. The threshold adjustment module is used to analyze the destructive sampling data and the multidimensional non-destructive testing data, and then dynamically and intelligently adjust the upper and lower limits of the preset quality evaluation threshold range. The control and management module is used to classify and screen processed forgings, control their transfer paths, and store them in designated areas based on four marked states: qualified, good, defective, and unqualified.

[0006] Furthermore, the online monitoring module includes: Laser scanning sensors are used to acquire the contour and dimensional data of forgings in real time; Temperature sensors are used to collect temperature distribution data in the forging and forming area; Pressure sensors are used to collect pressure change data on the contact surface between the mold and the forging; Vibration sensors are used to collect vibration amplitude and frequency data of forging equipment; Displacement sensors are used to collect real-time displacement data of forgings during the forming process.

[0007] Furthermore, the process of obtaining the forging quality evaluation parameters includes:

[0008]

[0009] The forging quality evaluation parameter Q is obtained by analysis and calculation using formulas (1) and (2); Where n is the number of dimensions monitored online. , Let be the weight coefficient of the i-th monitoring dimension. For the preprocessed data of the i-th monitoring dimension, The optimal value for the i-th monitoring dimension. Let be the theoretical maximum allowable value for the i-th monitoring dimension. Let be the theoretical minimum allowable value for the i-th monitoring dimension. Let be the data deviation coefficient for the i-th monitoring dimension. is the average of historical qualified data for the i-th monitoring dimension.

[0010] Furthermore, the process of marking the machined forgings as qualified, unqualified, and pending states includes: The forging quality evaluation parameter Q is compared with the preset quality evaluation threshold range. Perform a comparison; like If so, the forging will be marked as qualified; like If so, the forging will be marked as defective; like If so, the forging will be marked as pending.

[0011] Furthermore, the non-destructive detection module includes: A three-dimensional coordinate measuring machine is used to accurately acquire the final contour dimension data of forgings; Ultrasonic flaw detectors are used to detect cracks and porosity defects inside forgings. Eddy current testing equipment is used to detect defects on and near the surface of forgings. Industrial CT scanners are used to acquire three-dimensional data on density distribution and defects inside forgings. Infrared thermal imagers are used to detect residual stress distribution data in forgings.

[0012] Furthermore, the process of reclassifying and marking the forgings in the pending state as either good or defective includes:

[0013] The defect score of the forging in the undetermined state is obtained by analyzing and calculating using the above formula. ; Where m is the number of dimensions for non-destructive testing. , Let be the weight coefficient of the j-th detection dimension. For the j-th detection dimension, The maximum allowed value for the j-th detection dimension. This represents the minimum allowed value for the j-th detection dimension; when If this happens, the corresponding forging in the pending state will be remarked as defective. when If the condition is met, the corresponding forging in the pending state will be remarked as a good product.

[0014] Furthermore, the destructive sampling module includes: The sampling management unit is used to select a corresponding number of forgings as samples from four categories of forgings marked as qualified, good, defective and unqualified, according to preset sampling rules. The preset sampling rules are as follows: qualified forgings are sampled according to the first sampling ratio, good forgings are sampled according to the second sampling ratio, defective forgings are sampled according to the third sampling ratio, and unqualified forgings are sampled according to the fourth sampling ratio. Wherein, the first sampling ratio < the second sampling ratio < the fourth sampling ratio < the third sampling ratio, and each sampling ratio is dynamically adjusted according to the batch quantity and quality evaluation parameters of forgings in various states; The destructive testing unit is used to perform destructive testing on selected sample forgings. The testing items include mechanical property testing, metallographic structure testing, hardness testing, and fatigue life testing of the forgings. The data calibration and transmission unit is used to perform systematic error calibration and filtering noise reduction on the destructive sampling data, and after associating the calibrated sampling data with the corresponding forging status marking information, online monitoring data and non-destructive testing data, it packages and transmits the data to the threshold intelligent adjustment module.

[0015] Furthermore, the process of dynamically and intelligently adjusting the upper and lower limits of the preset quality evaluation threshold range includes:

[0016]

[0017]

[0018]

[0019]

[0020]

[0021] The upper limit adjustment amount of the quality evaluation threshold range is obtained by analyzing and calculating using formulas (3)-(8). and lower limit adjustment amount ; in, The performance qualification rate of forgings in qualified condition. The performance qualification rate of forgings in good condition. The performance pass rate of forgings in defective condition. The performance pass rate of forgings in unqualified condition. This refers to the number of forgings that pass performance inspection in a qualified condition. The number of samples for forgings in qualified condition to be randomly inspected. This refers to the number of forgings that pass performance inspection in good condition. The number of samples for forgings in good condition for random inspection. This refers to the number of forgings that pass performance inspections in defective condition. This refers to the number of samples taken from forgings in defective condition for random inspection. This refers to the number of forgings that passed performance testing out of those that were found to be in substandard condition. This refers to the number of forgings sampled in a non-conforming condition. The target performance pass rate for forgings in qualified condition. The target performance pass rate for forgings in good condition is... For forgings in defective condition to meet target performance standards For forgings in a non-conforming state to achieve the target performance, The average defect score for forgings in the undetermined state. This is the threshold for comparing poor performance scores. , , , , , It is the adjustment coefficient, and , ; Update the upper limit of the quality assessment threshold range to The lower limit has been updated to and ensure .

[0022] Furthermore, the control management module includes: The sorting and screening unit is used to control the sorting robot arm or conveyor belt path according to four marking states, guiding forgings in different states to the corresponding storage areas. The qualified product processing unit is used to transfer qualified forgings to the finished product warehouse for packaging and storage. The good product processing unit is used to transfer forgings in good condition to the waiting-for-shipment area or the special marking area; The defective product handling unit is used to transfer defective forgings to the rework area or the degraded use area; The non-conforming product handling unit is used to transfer non-conforming forgings to the scrap recycling area; The status traceability unit is used to record the status marking time, location information, and flow path of each forging, and to establish a complete quality traceability file.

[0023] The beneficial effects of this invention are: (1) This invention integrates online monitoring, data processing and analysis, status determination, non-destructive testing, destructive sampling, threshold adjustment and control management modules to build a comprehensive management platform that integrates real-time monitoring, intelligent diagnosis and closed-loop feedback. The system realizes real-time monitoring and adaptive optimization of the forging process, and can dynamically adjust the quality threshold and sampling frequency. This effectively solves the problems of defect detection lag, resource waste and quality control imbalance caused by fixed thresholds being unable to adapt to dynamic factors, the separation of online and offline detection and the rigidity of the sampling mechanism in the prior art. It realizes multi-dimensional perception of the forging process, accurate judgment and classification of forging quality, and adaptive optimization of key thresholds, and ultimately significantly improves the quality control accuracy and overall production efficiency of automated production of automotive forgings.

[0024] (2) This invention constructs a multi-factor fusion threshold adjustment model by implementing a destructive verification mechanism of dynamic sampling. It incorporates the sampling pass rate of four types of forgings and the group quality of forgings in the pending state into the feedback loop, realizing the continuous self-optimization and accurate calibration of the judgment benchmark of the online monitoring system. It effectively overcomes the threshold drift problem caused by time-varying factors such as material batch fluctuations and mold wear, significantly improves the adaptability and reliability of the system in long-term operation, and achieves the optimal balance between quality control stability and inspection cost economy in automotive forging processing. Attached Figure Description

[0025] The invention will now be further described with reference to the accompanying drawings.

[0026] Figure 1 This is a schematic block diagram of an automated control and management system for automotive forging processing proposed in this invention. Detailed Implementation

[0027] 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 some embodiments of the present invention, and not all embodiments. 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.

[0028] Please see Figure 1 As shown, in one embodiment, an automated control and management system for automotive forging processing is provided, the system comprising: The online monitoring module is deployed in the forging and forming operation area of ​​the forging to collect multi-dimensional real-time data on the forging and forming process. The multi-dimensional real-time data includes, but is not limited to, forging temperature, forging pressure, deformation rate, die displacement and vibration signal. High-frequency data acquisition is achieved through an integrated sensor network to capture instantaneous fluctuations in process parameters. The data processing and analysis module is used to receive multi-dimensional real-time data and perform preprocessing. The preprocessing process includes data cleaning, noise filtering, normalization, and feature extraction. The preprocessed multi-dimensional data is then comprehensively analyzed to generate corresponding forging quality evaluation parameters. The status determination module is used to compare the quality evaluation parameters with the preset quality evaluation threshold range, and mark the processed forgings as qualified, unqualified and pending states according to the comparison results. The preset threshold range is initially set based on historical production data and is dynamically updated by subsequent modules. The non-destructive testing module is used to perform offline and precise data acquisition on forgings marked as pending state using multi-dimensional non-destructive testing methods (such as ultrasonic testing, X-ray imaging, eddy current testing, or infrared thermography analysis) to obtain multi-dimensional non-destructive testing data of the pending forgings. After quantitative analysis of the multi-dimensional non-destructive testing data, the forgings in the pending state are reclassified and marked as good or defective. The destructive sampling module is used to perform destructive sampling inspections on four types of forgings marked as qualified, good, defective and unqualified according to preset sampling rules. The destructive sampling inspection data of each type of forging is obtained through metallographic analysis, tensile test or hardness test to verify the accuracy of online and non-destructive testing. The threshold adjustment module is used to analyze the destructive sampling data and the multidimensional non-destructive testing data, and then dynamically and intelligently adjust the upper and lower limits of the preset quality evaluation threshold range to compensate for the effects of material batch differences, mold wear and environmental changes, and realize threshold self-optimization. The control and management module is used to classify and screen the processed forgings according to four marked states: qualified, good, defective, and unqualified. This is done through visual recognition or RFID tracking, transfer path control (automatic guidance based on PLC or industrial robots), and zoned storage and placement (such as warehousing of qualified products and rework or scrapping of defective products). At the same time, it generates quality reports and feeds them back to the production execution system to achieve closed-loop control of the production process.

[0029] Through the above technical solution, this embodiment provides an automated control and management system for automotive forgings. The system integrates modules for online monitoring, data processing and analysis, status determination, non-destructive testing, destructive sampling, threshold adjustment, and control management. This constructs a comprehensive management platform integrating real-time monitoring, intelligent diagnosis, and closed-loop feedback. The system achieves real-time monitoring and adaptive optimization of the forging process, dynamically adjusting quality thresholds and sampling frequency. This effectively solves problems in existing technologies, such as the inability of fixed thresholds to adapt to dynamic factors, the disconnect between online and offline testing, and the rigidity of the sampling mechanism, leading to delayed defect detection, resource waste, and quality control imbalance. It achieves multi-dimensional perception of the forging process, accurate determination and classification of forging quality, and adaptive optimization of key thresholds, ultimately significantly improving the quality control accuracy and overall production efficiency of automated automotive forging production.

[0030] In one embodiment, the online monitoring module includes: Laser scanning sensors are used to collect the contour dimension data of forgings in real time and detect geometric defects such as dimensional deviations and warping by comparing them with preset CAD models. Temperature sensors are used to collect temperature distribution data in the forging zone, monitor heating uniformity and temperature drop during forging, and ensure that the material is in the optimal plastic deformation temperature range. Pressure sensors are used to collect pressure change data on the contact surface between the die and the forging to reflect whether the forging force is sufficient and uniform, and to avoid insufficient filling or over-forging due to improper pressure. Vibration sensors are used to collect vibration amplitude and frequency data of forging equipment. By analyzing abnormal vibration patterns, the condition of the equipment and potential impact cracks can be indirectly determined. Displacement sensors are used to collect real-time displacement data of forgings during the forming process. Combined with time parameters, the deformation rate can be calculated, which is a key parameter for evaluating material flowability and deformation.

[0031] The process for obtaining the forging quality evaluation parameters includes:

[0032]

[0033] The forging quality evaluation parameter Q is obtained by analysis and calculation using formulas (1) and (2); Where n is the number of dimensions monitored online, i.e., the total number of data types collected, which is directly set during system initialization based on the number of sensor types enabled. , The weight coefficient for the i-th monitoring dimension is typically determined based on historical production data, the experience of process experts, or through feature importance analysis of a large number of samples using a machine learning model. The preprocessed real-time data value for the i-th monitoring dimension is directly collected by the corresponding sensor, preprocessed by the data processing and analysis module, and then input into the calculation unit. The optimal value for the i-th monitoring dimension is the target value under ideal process parameters, obtained in advance based on material properties, mold design, and forging process specifications. Let be the theoretical maximum allowable value for the i-th monitoring dimension. Let be the theoretical minimum allowable value for the i-th monitoring dimension. , All of these were obtained in advance through pre-defined process standards, materials science knowledge, and historical qualified data ranges. Let be the data deviation coefficient for the i-th monitoring dimension. The mean of historical qualified data for the i-th monitoring dimension represents the average level of qualified data for this dimension during past stable production cycles. It provides a dynamic benchmark based on actual production levels for quality evaluation by continuously extracting and updating this mean from forging data marked as qualified.

[0034] The process of marking the shaped forgings into qualified, unqualified, and pending states includes: The forging quality evaluation parameter Q is compared with the preset quality evaluation threshold range. Perform a comparison; like If the forging is marked as qualified, it indicates that the forging has excellent overall performance and low deviation across all key monitoring dimensions. like If the forging is marked as non-conforming, it indicates that the forging has serious deviations in one or more key dimensions and has a high quality risk. like If the quality of the forging is not determined, it will be marked as pending. This indicates that the quality of the forging is in a critical range and cannot be clearly determined by the online model. It needs to be transferred to the non-destructive testing module for precise re-inspection.

[0035] Through the above technical solution, this embodiment provides an online quality evaluation and preliminary screening method for automotive forgings based on multi-sensor data fusion. The method integrates multiple types of sensors such as laser, temperature, pressure, vibration, and displacement to construct a comprehensive data acquisition system. It also utilizes a quantitative evaluation model that integrates weighting coefficients, deviation normalization, and historical benchmarks to fuse complex multi-dimensional process data into an intuitive quality evaluation parameter. This enables rapid, objective, and online preliminary judgment of forging forming quality, significantly improving the efficiency and intelligence level of quality control in automated production lines for automotive forgings.

[0036] In one embodiment, the non-destructive testing module includes: A three-dimensional coordinate measuring machine is used to accurately collect the final contour dimension data of forgings. By comparing it with the design model, its geometric accuracy (such as position and concentricity) is quantified, and it is assessed whether the deviation is caused by springback or die wear. Ultrasonic flaw detectors utilize the propagation characteristics of high-frequency sound waves in materials to detect whether there are volumetric defects such as cracks, inclusions, and pores inside forgings, and can assess the burial depth and approximate size of the defects. Eddy current testing equipment uses the principle of electromagnetic induction to detect minute cracks, folds and other defects on and near the surface of forgings, and to conduct a rigorous evaluation of surface quality. Industrial CT scanners use X-ray computed tomography technology to acquire three-dimensional data of density distribution and defects inside forgings, including density distribution, shrinkage cavities and porosity, and can accurately reconstruct the three-dimensional morphology of internal defects. Infrared thermal imagers capture the temperature field distribution on the surface of forgings to indirectly detect residual stress concentration areas caused by uneven plastic deformation or differences in cooling rates, providing a basis for fatigue performance evaluation.

[0037] The process of reclassifying and marking forgings in the pending state as either good or defective includes:

[0038] The defect score of the forging in the undetermined state is obtained by analyzing and calculating using the above formula. ; Where m represents the number of dimensions for non-destructive testing, i.e., the number of types of testing equipment used, which is determined by the system based on the actual configuration and testing procedures. , The weight coefficient for the j-th inspection dimension is obtained by allocating factors based on the impact of various defects on product functional safety, combined with industry standards (such as automotive forging acceptance specifications) and expert experience. The actual detected value for the j-th detection dimension is obtained by direct measurement or analysis by the corresponding non-destructive testing equipment. For example, an ultrasonic flaw detector might output the equivalent defect size, while an industrial CT scanner might output the percentage of internal porosity. Let j be the maximum allowed values ​​for the detection dimensions. Let be the minimum allowed value for the j-th detection dimension. , All are obtained in advance based on product drawings, technical specifications, and customer standards; when If the forging is in a pending state, it will be remarked as a defective product, indicating that the forging has a clear quality defect in at least one non-destructive testing dimension, with the actual test value being greater than or equal to the maximum allowable value or less than or equal to the minimum allowable value. when If the forging is in an undetermined state, it will be remarked as a good product. This indicates that although the online monitoring data of the forging is critical, after more accurate offline verification, its performance in all non-destructive testing dimensions is within acceptable range and meets the factory quality requirements.

[0039] Through the above technical solution, this embodiment provides a method for accurate re-judgment of forging quality based on multi-dimensional non-destructive testing data fusion. The method integrates multiple non-destructive testing technologies such as coordinate measurement, ultrasonic, eddy current, CT, and infrared to construct a multi-level, high-precision offline testing system. It integrates defect data of different dimensions and properties into a unified quantitative index, realizing a rapid, objective, and accurate final judgment on forgings in a pending state during online monitoring. This effectively avoids misjudging critical products as qualified or scrapped products, significantly improves the accuracy of quality grading, and ensures the reliability of products leaving the factory.

[0040] In one embodiment, the destructive sampling module includes: The sampling management unit is used to select a corresponding number of forgings as samples from four categories of forgings marked as qualified, good, defective and unqualified, according to preset sampling rules. The preset sampling rules are as follows: qualified forgings are sampled according to the first sampling ratio, good forgings are sampled according to the second sampling ratio, defective forgings are sampled according to the third sampling ratio, and unqualified forgings are sampled according to the fourth sampling ratio. Among them, the first sampling ratio < the second sampling ratio < the fourth sampling ratio < the third sampling ratio. This ratio relationship reflects the principle that the higher the risk, the stricter the sampling. In particular, the highest sampling intensity is applied to the defective product status (the critical product where the online and non-destructive testing results contradict each other) to accurately reveal potential quality risks. Moreover, each sampling ratio is dynamically adjusted according to the batch quantity and quality evaluation parameters of forgings in various statuses. The destructive testing unit is used to perform destructive testing on selected sample forgings. The testing items include, but are not limited to, mechanical property testing, metallographic structure testing, hardness testing, and fatigue life testing of the forgings. The data calibration and transmission unit is used to perform systematic error calibration and filtering noise reduction on the destructive sampling data, and after associating the calibrated sampling data with the corresponding forging status marking information, online monitoring data and non-destructive testing data, it packages and transmits the data to the threshold intelligent adjustment module.

[0041] The process of dynamically and intelligently adjusting the upper and lower limits of the preset quality evaluation threshold range includes:

[0042]

[0043]

[0044]

[0045]

[0046]

[0047] The upper limit adjustment amount of the quality evaluation threshold range is obtained by analyzing and calculating using formulas (3)-(8). and lower limit adjustment amount ; in, The performance qualification rate of forgings in qualified condition. The performance qualification rate of forgings in good condition. The performance pass rate of forgings in defective condition. The performance pass rate of forgings in unqualified condition. This refers to the number of forgings that pass performance inspection in a qualified condition. The number of samples for forgings in qualified condition to be randomly inspected. This refers to the number of forgings that pass performance inspection in good condition. The number of samples for forgings in good condition for random inspection. This refers to the number of forgings that pass performance inspections in defective condition. This refers to the number of samples taken from forgings in defective condition for random inspection. This refers to the number of forgings that passed performance testing out of those that were found to be in substandard condition. This refers to the number of forgings sampled in a non-conforming condition. , , , All results were determined and statistically obtained by the destructive testing unit according to testing standards. , , , This data was obtained to statistically analyze the actual sampling quantity of forgings in various conditions. The target performance pass rate for forgings in qualified condition. The target performance pass rate for forgings in good condition is... For forgings in defective condition to meet target performance standards For forgings in a non-conforming state to achieve the target performance, , , , All of these can be obtained by pre-setting quality objectives, customer requirements, and historical best performance. The average defect score for forgings in the undetermined state is obtained by averaging the defect scores P calculated from all forgings in the undetermined state during the same period after non-destructive testing. The threshold for defect scoring is pre-set based on historical data and the accuracy of non-destructive testing, serving as a benchmark for determining whether the quality of a group of forgings in a pending state is normal. , , , , , The adjustment coefficients can all be obtained through training with historical data, regression analysis, or by setting them based on the experience of process experts. , ; Update the upper limit of the quality assessment threshold range to The lower limit has been updated to and ensure .

[0048] Through the above technical solution, this embodiment provides a dynamic self-adjustment method for quality evaluation threshold based on destructive sampling feedback. The method constructs a multi-factor fusion threshold adjustment model by implementing a destructive verification mechanism of dynamic sampling. It simultaneously incorporates the sampling pass rate of forgings in four states and the group quality of forgings in undetermined states into the feedback loop, realizing continuous self-optimization and accurate calibration of the judgment benchmark of the online monitoring system. It effectively overcomes the threshold drift problem caused by time-varying factors such as material batch fluctuations and mold wear, significantly improves the adaptability and reliability of the system in long-term operation, and finally achieves the optimal balance between quality control stability and inspection cost economy in automotive forging processing.

[0049] In one embodiment, the control management module, as the system's execution terminal, receives final status marker instructions from upstream modules and thereby realizes automated logistics management and information traceability of forgings, specifically including: The sorting and screening unit is used to send control commands to the sorting robot arm, baffle mechanism or cross belt sorter and other actuators through the programmable logic controller (PLC) according to the four final marked states: qualified state, good state, defective state and unqualified state. This dynamically controls the path switching of the conveyor belt, so as to accurately and efficiently guide the forgings in different states to the corresponding subsequent processing channels and storage areas. The qualified product processing unit is used to transfer qualified forgings to the finished product warehouse for packaging and storage. The good product processing unit is used to transfer forgings in good condition to the shipping area or special marking area. Such products may require special quality instructions or limited use when leaving the warehouse to ensure that customers are informed and to meet traceability requirements. The defective product handling unit is used to transfer defective forgings to the rework area (such as for welding repair, grinding, etc.) or the downgraded use area (to be treated as secondary or substandard products) in order to maximize material value and reduce overall waste. The non-conforming product handling unit is used to transfer non-conforming forgings to the scrap recycling area and initiate the scrap recycling process, putting them into the smelting furnace or handing them over to professional recyclers for processing, so as to realize the recycling of resources. The status traceability unit is used to establish and maintain a unique quality traceability file for each forging. This unit records the complete lifecycle data of each forging in the entire system, including but not limited to: the timestamp of the status mark, the physical location information on the sorting line, the complete flow path it has experienced, and associates and binds it with the corresponding online monitoring data, non-destructive testing reports and destructive sampling results.

[0050] Through the above technical solution, this embodiment provides an automated sorting and traceability management method for forgings based on multi-state determination. The method integrates classification and screening, partitioning and state traceability functions to transform upstream quality determination conclusions into precise physical execution instructions, thereby realizing automated and refined sorting and warehousing management of qualified, good, defective and unqualified products.

[0051] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. An automated control management system for processing of automotive forgings, characterized in that, The system comprises: An online monitoring module arranged in a forging forming area of a forging piece for collecting multi-dimensional real-time data of the forging forming process; A data processing and analysis module for receiving and preprocessing the multi-dimensional real-time data, comprehensively analyzing the preprocessed multi-dimensional data, and generating corresponding forging quality evaluation parameters; A state determination module for comparing the quality evaluation parameters with preset quality evaluation threshold ranges, and marking the processed and formed forging pieces as qualified, unqualified, and pending according to comparison results; A non-destructive testing module for collecting off-line accurate data of the forging pieces marked as pending by using multi-dimensional non-destructive testing methods, obtaining multi-dimensional non-destructive testing data of the pending forging pieces, and reclassifying the pending forging pieces as good or bad products after quantitative analysis of the multi-dimensional non-destructive testing data; A destructive sampling module for destructively sampling the four types of forging pieces marked as qualified, good, bad, and unqualified according to preset sampling rules, and obtaining destructive sampling data of the forging pieces; A threshold adjustment module for analyzing the destructive sampling data and the multi-dimensional non-destructive testing data, and dynamically and intelligently adjusting upper and lower limits of the preset quality evaluation threshold ranges; A control and management module for classifying, screening, controlling transfer paths, and storing the processed and formed forging pieces according to the four marking states of qualified, good, bad, and unqualified.

2. The automatic control management system for processing of a wrought product for a vehicle according to claim 1, characterized by, The online monitoring module comprises: A laser scanning sensor for collecting real-time profile size data of the forging piece; A temperature sensor for collecting temperature distribution data of the forging forming area; A pressure sensor for collecting pressure change data on a contact surface between a die and the forging piece; A vibration sensor for collecting vibration amplitude and frequency data of the forging equipment; A displacement sensor for collecting real-time displacement data of the forging piece in the forming process.

3. The system for automated control and management of processing of forgings for automotive vehicles according to claim 2, characterized in that, The forging quality evaluation parameter obtaining process comprises: Obtaining the forging quality evaluation parameter Q by formula (1)-(2) analysis and calculation; wherein n is the number of dimensions of online monitoring, , is the weight coefficient of the i-th monitoring dimension, is the pre-processed data of the i-th monitoring dimension, is the optimal value of the i-th monitoring dimension, is the theoretical maximum allowable value of the i-th monitoring dimension, is the theoretical minimum allowable value of the i-th monitoring dimension, is the data deviation coefficient of the i-th monitoring dimension, is the historical qualified data mean value of the i-th monitoring dimension.

4. The automatic control management system for processing of a forged piece for a vehicle according to claim 3, characterized by, The process of marking the processed and formed forging pieces as qualified, unqualified, and pending comprises: The forging quality evaluation parameter Q is compared with a preset quality evaluation threshold range Comparison is made; If then the forging is marked as being in a pass state; If then the forging is marked as not conforming; If then the forging is marked as pending.

5. The system for automated control and management of processing of a wrought product for automotive applications according to claim 4, wherein The non-destructive testing module comprises: A three-dimensional coordinate measuring machine for accurately collecting final profile size data of the forging piece; An ultrasonic flaw detector for detecting internal crack and porosity defect data of the forging piece; An eddy current detector for detecting surface and near-surface defect data of the forging piece; An industrial CT machine for obtaining internal density distribution and defect three-dimensional data of the forging piece; An infrared thermal imager for detecting residual stress distribution data of the forging piece.

6. The automatic control management system for processing of a wrought product for a vehicle according to claim 5, characterized by, The process of reclassifying the pending forging pieces as good or bad products comprises: The bad score of the undetermined state forging is obtained by analyzing and calculating the above formula ; wherein m is the number of dimensions of non-destructive testing, , is a weight coefficient of the jth detection dimension, is an actual detection value of the jth detection dimension, is a maximum allowable value of the jth detection dimension, is a minimum allowable value of the jth detection dimension; When the corresponding pending status forging is re-labeled as a reject status; When the corresponding pending status forging is re-labeled as a good product status.

7. The automated control management system for processing of automotive forgings as claimed in claim 6 wherein, The destructive sampling module comprises: A sampling management unit for selecting corresponding numbers of forging pieces as samples from the four types of forging pieces marked as qualified, good, bad, and unqualified according to preset sampling rules. The preset sampling rule is that the qualified state forgings are sampled according to a first sampling ratio, the good product state forgings are sampled according to a second sampling ratio, the substandard product state forgings are sampled according to a third sampling ratio, and the unqualified state forgings are sampled according to a fourth sampling ratio; Wherein, the first sampling ratio < the second sampling ratio < the fourth sampling ratio < the third sampling ratio, and each sampling ratio is dynamically adjusted according to the batch quantity and quality evaluation parameters of each type of state forging; The destructive detection unit is used for destructive detection of the selected sample forgings, and the detection items include mechanical property detection, metallographic structure detection, hardness detection and fatigue life detection of the forgings; The data calibration and transmission unit is used for system error calibration and filtering denoising processing of the destructive sampling data, and after the calibrated sampling data is associated with the state marking information, online monitoring data and non-destructive detection data of the corresponding forgings, it is packaged and transmitted to the threshold intelligent adjustment module.

8. The automated control management system for processing of automotive forgings as claimed in claim 7 wherein, The process of dynamically and intelligently adjusting the upper limit value and the lower limit value of the preset quality evaluation threshold range includes: The upper limit adjustment amount of the quality evaluation threshold range is obtained by analyzing and calculating by formulas (3)-(8) and the lower limit adjustment amount ; wherein, is the performance pass rate for the acceptable status forgings, is the performance pass rate for the good status forgings, is the performance pass rate for the bad status forgings, is the performance pass rate for the unacceptable status forgings, is the number of performance passes in the acceptable status forging sampling, is the number of samples in the acceptable status forging sampling, is the number of performance passes in the good status forging sampling, is the number of samples in the good status forging sampling, is the number of performance passes in the bad status forging sampling, is the number of samples in the bad status forging sampling, is the number of performance passes in the unacceptable status forging sampling, is the number of samples in the unacceptable status forging sampling, is the target performance pass rate for the acceptable status forgings, is the target performance pass rate for the good status forgings, is the target performance pass for the bad status forgings, is the target performance pass for the unacceptable status forgings, is the average badness score for the pending status forgings, is the badness score comparison threshold, , , , , , is the adjustment factor, and , ; updating the upper limit of the quality evaluation threshold range to , the lower limit to and ensuring that .

9. The automated control management system for processing of automotive forgings as claimed in claim 8 wherein, The control management module includes: The classification and screening unit is used for controlling the sorting mechanical arm or the conveying belt path according to the four marking states, and guiding the forgings of different states to the corresponding storage areas; The qualified product processing unit is used for transferring the qualified state forgings to the finished product warehouse for packaging and storage; The good product processing unit is used for transferring the good product state forgings to the waiting for shipment area or the special identification area; The substandard product processing unit is used for transferring the substandard product state forgings to the repair area or the degraded use area; The unqualified product processing unit is used for transferring the unqualified state forgings to the waste product recycling area; The state tracing unit is used for recording the state marking time, position information and flow path of each forging, and establishing a complete quality tracing file.

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