An aluminum wheel product weight intelligent management and control method, system, device and medium

Abstract

The present application belongs to the technical field of aluminum wheel product manufacturing, and specifically relates to an aluminum wheel product weight intelligent management and control method, system, device and medium. The present application acquires multi-dimensional production data through real-time acquisition of a weighing system, improves resource utilization efficiency; determines weight grades and realizes real-time statistics and visual display, providing data support for decision-making; core index calculation and fluctuation chart display, combined with weight and multi-dimensional size data to assess star ratings and prioritize, improving problem processing efficiency; establishing a mold yield and cost early warning mechanism, completely recording the problem processing, parameter adjustment whole process and feedback results and used for model training, and subsequently generating a processing scheme for similar problems of similar products, greatly improving problem processing efficiency and accuracy. The present application realizes the full-process intelligent management and control of aluminum wheel product weight from data acquisition, analysis, determination, early warning to problem processing, effectively reduces production cost, and improves product quality stability and production efficiency.

Description

Technical Field

[0001] This invention belongs to the field of aluminum wheel product manufacturing technology, specifically relating to an intelligent weight control method, system, equipment, and medium for aluminum wheel products. Background Technology

[0002] Currently, weight control in aluminum wheel production relies heavily on manual recording and experience-based judgment, leading to several pain points: First, it's impossible to accurately monitor deviations from standard weights in real time, easily resulting in exceeding weight standards, low metal utilization, and increased production costs. Second, metal utilization analysis is limited to a single dimension, failing to categorize and calculate across multiple product types, making it difficult to accurately pinpoint weight loss issues. Third, product weight grading is not standardized, lacking real-time statistics and visualization, hindering a clear understanding of weight control effectiveness. Fourth, the correlation analysis between weight and dimensional issues is insufficient, with ambiguous priority classification, untimely feedback, and a lack of closed-loop process management. Fifth, during mold use, mold wear increases with production volume, leading to variations in aluminum usage; the absence of a correlation warning mechanism between mold production output and dimensional deviations easily results in cost losses due to overproduction or underproduction. Sixth, the analysis of the impact of machining parameters on weight is unsystematic, failing to provide timely guidance for process adjustments, and the weight adjustment process lacks effective tracking. These problems result in low weight control accuracy, high costs, and poor quality stability in aluminum wheel products, hindering the improvement of production efficiency. Summary of the Invention

[0003] This invention proposes a method, system, equipment, and medium for intelligent weight control of aluminum wheel products, in order to solve the problem that weight control in the prior art relies heavily on manual recording and experience-based judgment.

[0004] To achieve the above objectives, the present invention proposes the following technical solution: A method for intelligent weight control of aluminum wheel products includes the following steps: Step 1: Connect with various equipment on site in real time, establish a data acquisition link, and collect multi-dimensional data during the production process of aluminum wheel products; Step 2: Process the multi-dimensional data, classify and calculate the metal utilization rate of casting wheels and casting wheels, calculate the level of individual pieces and the weight improvement, and determine the product weight grade. Step 3: Based on the multi-dimensional data of the product, sort the priority levels of product issues; Step 4: Record the real-time and cumulative output of the top mold and side mold in the multi-dimensional data, set the cost recovery threshold and cost difference threshold, and trigger an alarm if the corresponding data exceeds the cost recovery threshold and cost difference threshold. Step 5: Obtain the shift weight data for each product and determine whether the shift weight data is qualified. If it is not qualified, trigger an alarm. Step 6: Collect dimensional data from at least 10 adjusted products for verification. If all dimensional data are qualified, the retest is passed; if not all dimensional data are qualified, the retest is failed, and a new process adjustment plan is formulated until the product data is qualified.

[0005] Preferably, the multi-dimensional data includes weighing data, dimensional data, production data, process data, quality data, and mold data.

[0006] Preferably, in step 2, determining the product weight grade specifically involves: The determination is based on a preset product weight standard range, including: The weight is classified into four levels: weight within the standard range and lower than the design value, weight within the standard range but higher than the design value, weight exceeding the lower limit of the standard range, and weight exceeding the upper limit of the standard range.

[0007] Preferably, in step 3, prioritizing product issues specifically involves: The semi-finished product weight is qualified compared with the design value, earning 1 star; the measured value of the rim thickness is within the standard range, earning 1 star; the measured value of the flange thickness is within the standard range, earning 2 stars; the measured value of the spoke thickness is within the standard range, earning 1 star. Products rated 4-5 stars are considered high priority, products rated 2-3 stars are considered medium priority, and products rated 1 star are considered low priority.

[0008] Preferably, in step 4, the preset production of the top mold and side mold exceeding 20,000 pieces is the mold cost recovery threshold.

[0009] Preferably, step 5 specifically includes: Obtain the average weight of each product from the previous production shift and the average weight of the current shift over the last 4 hours. Compare the current shift's average weight with the average weight of the previous shift and the product's design value. If the current shift's average weight is greater than the average weight of the previous shift and exceeds the design value, issue a warning.

[0010] Preferably, step 6 further includes: Data is archived throughout the entire process to form a knowledge base for reference in similar issues later; simultaneously, the data is input into the big data training model for continuous training and optimization based on historical data.

[0011] A weight intelligent control system for aluminum wheel products includes the following modules: The data acquisition module is used to connect various on-site devices in real time, establish a data acquisition link, and collect multi-dimensional data during the production process of aluminum wheel products. The processing module is used to process multi-dimensional data, classify and calculate the metal utilization rate of casting wheels and casting wheels, calculate the level of individual pieces and improve weight, and determine the weight grade of products. The analysis module is used to prioritize product issues based on multi-dimensional product data; record the real-time and cumulative production of top and side dies in the multi-dimensional data; set cost recovery thresholds and cost difference thresholds; if the corresponding data exceeds the cost recovery threshold and cost difference threshold, an alert is triggered; acquire shift weight data for each product and determine whether the shift weight data is qualified; if it is not qualified, an alert is triggered; collect dimensional data of at least 10 adjusted products for verification; if all dimensional data are qualified, the retest passes; if not all dimensional data are qualified, the retest fails, and a new process adjustment plan is formulated until the product data is qualified. The AI ​​module is used for end-to-end data archiving, forming a knowledge base for reference in similar issues later; simultaneously, data is input into the big data training model, and continuous training and optimization are carried out based on historical data.

[0012] An electronic device, comprising a memory and a processor; Memory, used to store computer programs; A processor is used to execute the computer program, which, when executed by the processor, implements the steps of an intelligent weight control method for aluminum wheel products.

[0013] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a method for intelligent weight control of aluminum wheel products.

[0014] The advantages of this invention are: This invention acquires multi-dimensional production data through a real-time weighing system, classifies and calculates the metal utilization rate of cast wheels and spinning wheels, accurately grasps weight loss, and improves resource utilization efficiency. It determines weight levels and provides real-time statistics and visualization, intuitively presenting the control effect and providing data support for decision-making. Through core indicator calculation and fluctuation chart display, it clearly understands the actual situation of weight control and the trend of A-level product proportion. Combining weight and multi-dimensional dimensional data to assess star ratings and prioritize tasks, it achieves precise delivery of problem-solving opinions and closed-loop process cases, improving problem-solving efficiency. It establishes a mold production and cost early warning mechanism to avoid cost losses in advance. It fully records the entire process of problem handling, parameter adjustment, and feedback results for model training. For similar problems with similar products, it can automatically generate handling solutions, which can be issued with one click after confirmation by technical personnel, adapting to complex parameter adjustment environments, significantly improving problem-solving efficiency and accuracy, and achieving continuous iterative upgrades in control capabilities. This invention realizes intelligent control of aluminum wheel product weight from data collection, analysis, judgment, early warning to problem handling, effectively reducing production costs and improving product quality stability and production efficiency. Attached Figure Description

[0015] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 A schematic diagram of an intelligent weight control method for aluminum wheel products; Figure 2 A schematic diagram showing the details of an intelligent weight control method for aluminum wheel products; Figure 3 A schematic diagram of an intelligent weight control system for aluminum wheel products; Figure 4 This is a schematic diagram of an electronic device. Detailed Implementation

[0016] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0017] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. Example

[0018] Please see Figure 1 , Figure 2 As shown, this invention provides an intelligent weight control method for aluminum wheel products, specifically including the following steps: Step 1: Real-time data acquisition, specifically: Real-time connection with various equipment on site establishes a data acquisition link, enabling real-time collection of multi-dimensional data during the production process of aluminum wheel products, including: weighing data, dimensional data, production data, process data, quality data, and mold data.

[0019] Step 2: Data processing and core indicator calculation, specifically: The multi-dimensional data is preprocessed, cleaned, and integrated, and the following analysis is performed: For both cast wheels and cast spinning wheels, the ratio of the weight after machining to the weight of the blank is calculated using computer-aided methods to determine the metal utilization rate; only weight data before and after machining are used for calculation. The weight of the designed semi-finished product is verified for compliance to prevent unreasonable design values.

[0020] Based on the customer's preset product weight standard range, the system automatically determines the weight class of each product: Grade A: Weight within the standard range and below the design value; Grade B: Weight within the standard range but above the design value; Grade C: Weight exceeds the lower limit of the standard range (suspected of cutting corners); Grade D: Weight exceeds the upper limit of the standard range.

[0021] The system provides real-time statistics on the quantity and proportion of products at each weight level, generating charts showing the proportion of Grade A products and the actual level of individual units. It also displays the target weight, actual weight, comparison results, number of days to meet the target, and finished product output for each product. Furthermore, it shows real-time fluctuations in the proportion and level of Grade A products per unit, generating bar charts for the Grade A proportion and line charts for the Grade A output of individual products.

[0022] Calculate the core weight index: Unit weight = (Design semi-finished product weight × Production volume - Actual semi-finished product weight × Production volume) / Production volume. A positive unit weight indicates profitability in weight control. Improved weight = (Initial actual weight - Improved actual weight) × Production volume.

[0023] Step 3: Problem star rating and intelligent processing, specifically: Star Rating: Based on multi-dimensional testing data for each product, a star rating is automatically assigned using a 5-star system. The semi-finished product weight is qualified compared with the design value, and gets 1 star; the measured value of the rim thickness is within the standard range, and gets 1 star; the measured value of the flange thickness is within the standard range, and gets 2 stars; the measured value of the spoke thickness is within the standard range, and gets 1 star.

[0024] Prioritization: Issues are prioritized based on the total number of star ratings. Products rated 4-5 stars are high-priority and will be immediately forwarded to the responsible technical department for management and technical personnel; products rated 2-3 stars are medium-priority and will be forwarded to the corresponding responsible technical department for technical personnel; products rated 1 star are low-priority and will be included in routine inspection and processing.

[0025] Based on the test results, targeted handling suggestions are automatically generated, such as: "Flange rim thickness out of tolerance + weight grade D, it is recommended to check the wear of the top mold and adjust the casting pressure parameters" or "It is recommended to fine-tune the Z-axis compensation value parameter of the machined rim to the XX range." The handling suggestions are pushed to the corresponding technical department; for intuitive on-site process adjustment issues such as production parameter fine-tuning and material feeding calibration, the handling suggestions can be directly issued to the on-site production machine with one click after manual review by the technical department.

[0026] After receiving instructions, on-site operators will adjust the process and provide feedback in the system. The system automatically records the time of issuance of the adjustment instruction, the receiver number, the operator, the adjustment implementation steps, and the adjustment completion time; Completely record the stage parameters and final results of the parameter tuning process to form a complete process adjustment traceability archive. Archive the details of each problem handled, corresponding handling opinions, complete parameter tuning records, and parameter tuning feedback results in a unified manner and store them in the big data training model; as data accumulates, the model is continuously optimized.

[0027] Step 4: Mold Management and Cost Early Warning Mold production monitoring: The system records the real-time and cumulative production of the top mold and side molds.

[0028] Set cost recovery threshold: The preset cost recovery threshold is when the production volume of the top mold and side mold exceeds 20,000 pieces. This threshold can be set.

[0029] Key monitoring trigger: When the output reaches a certain percentage of the threshold (e.g., 90%, i.e., 18,000 pieces), the key monitoring mode is activated to strengthen the monitoring of size and weight.

[0030] Correlation analysis and early warning: Statistical analysis of changes in the correlation between production output and dimensional deviations. If it is found that the increase in aluminum usage due to mold wear leads to a cost difference exceeding the design value (e.g., 5%), a red warning will be triggered. Warning methods include pop-up reminders on the system interface and sending SMS notifications to relevant personnel.

[0031] Mold repair tracking: For issues requiring mold repair, the system uses Gantt charts to track the progress of repair projects; Mold technicians can use the system to filter key areas of concern, which will help them carry out targeted repairs. The system automatically initiates mold modification or process optimization processes, and mold modification and process optimization tracking orders are automatically transmitted with the system.

[0032] Step 5: Machining Improvement Analysis Shift weight comparison: Obtain the average weight of each product from the previous production shift and the average weight of the current shift over the last 4 hours.

[0033] Dual comparative analysis: The average value of this class is compared with the average value of the previous class and the product design value.

[0034] Visual status indicators: If the average weight of this shift is lower than the average weight of the previous shift but is within the weight standard range, the system interface will display green, indicating that the weight has decreased and the control is in compliance. If the average weight of this class is greater than the average weight of the previous class and exceeds the design value, the system interface will display it in red, indicating that the weight has increased and needs attention.

[0035] Initiate adjustment process: Supports initiating a weight indicator adjustment process based on changes in product weight.

[0036] Process tracking: The system tracks the four stages of the adjustment process in real time: "application-approval-adjustment-verification", and records the adjustment parameters and verification results.

[0037] Step 6: Retesting, verification, and closed-loop management, specifically: After the technical department completes the problem handling or on-site process adjustment, it submits a processing completion application in the system, which triggers the retesting process.

[0038] Collect and verify the weight and key dimensions (such as rim, flange, spoke thickness, and center vent) of at least 10 adjusted products. Focus on verifying whether the product weight meets the standard range and whether the quality inspection data meets the standards.

[0039] If the retest is successful (all products meet the standards), the system will remind the initiating department to confirm and complete the closed-loop management. If the retest fails (there are substandard products), the system will return to the technical department to formulate a new process adjustment plan and repeat the "opinion generation - review and issuance - process adjustment - process traceability - retest verification" process until the product data meets the standards.

[0040] Regardless of whether the retest meets the standards, the system fully records the entire process information of this problem handling, including: problem description; initial handling opinions; stage parameters during the parameter tuning process; final parameter tuning results; retest data; and closed-loop conclusions.

[0041] Data is automatically archived throughout the entire process to form a knowledge base for reference in similar issues later; data is simultaneously input into big data training models for continuous training and optimization based on historical data; complex parameter tuning cases that fail to meet the standards in retesting are marked as key training samples to help the model improve its adaptability to complex scenarios.

[0042] When the optimized model encounters similar problems with similar products in the future, it can directly output the corresponding problem handling results and process parameter adjustment suggestions. After confirmation by technical personnel, it can be deployed and executed with one click. For complex parameter adjustment environments, it can generate multi-stage adaptation parameter adjustment solutions based on historical records.

[0043] In one specific implementation, step 3 allows technicians to manually fill in or supplement processing opinions.

[0044] This invention achieves precise control and intelligent analysis of the weight of aluminum products by collecting real-time weighing system data and multi-dimensional production data. First, it classifies and calculates the metal utilization rate of casting wheels and casting spinning wheels to accurately grasp the weight loss situation and improve resource utilization efficiency. Second, it automatically determines the weight level and displays statistics and visualizations in real time, intuitively presenting the control effect and providing data support for decision-making. Third, through the calculation of core indicators and the display of fluctuation charts, it clearly grasps the actual situation of weight control and the trend of the proportion of A-level products. Fourth, it combines weight and multi-dimensional dimensional data to evaluate star ratings and classify priorities, enabling precise delivery of problem handling opinions and closed-loop case studies, improving problem handling efficiency. Fifth, it establishes a mold production and cost early warning mechanism, triggering early warnings in advance based on the degree of mold wear and current production data, helping to carry out mold repair, spare parts application and other related work as early as possible, and avoiding cost losses in advance. Sixth, it conducts improvement analysis and tracks the progress of process adjustment to continuously optimize the weight control level. Seventh, it adds a big data training module to fully record the entire process of problem handling, parameter adjustment and feedback results for model training. In the future, it can automatically generate handling solutions for similar problems of similar products, and after confirmation by technical personnel, it can be issued for adjustment with one click, adapting to complex parameter adjustment environments, greatly improving the efficiency and accuracy of problem handling, and realizing continuous iterative upgrades of control capabilities. This invention enables intelligent control of the entire process of aluminum wheel product weight management, from data collection, analysis, judgment, early warning to problem handling, effectively reducing production costs and improving product quality stability and production efficiency. Example

[0045] This invention provides an intelligent weight control system for aluminum wheel products, such as... Figure 3 As shown, it specifically includes: a data acquisition module, a processing module, an analysis module, and an AI module.

[0046] The data acquisition module is used to perform step 1 in embodiment 1. It connects to various on-site devices in real time to collect data such as blank weight, weight after machining, weight of semi-finished product, weight of finished product, rim / rim / spoke thickness, output, machining parameters, number of waste products with air holes in the wheel center, top die output, and side die output, ensuring the real-time performance and accuracy of data acquisition.

[0047] The processing module is used to perform step 2 in Embodiment 1, which cleans and integrates the collected data, classifies and calculates the metal utilization rate of casting wheels and casting wheels; calculates the single-piece level and improved weight, and determines whether the single-piece level is positive (profitable); determines the product weight grade (A / B / C / D grade) according to the customer's preset weight standard range, counts the quantity and proportion of each grade, and generates a bar chart of the A grade proportion and a line chart of the A grade output of a single product.

[0048] The analysis module is used to perform steps 3, 4, and 5 in Example 1. The system performs star rating based on product weight and size data, prioritizing issues; it automatically generates targeted processing suggestions. For intuitive on-site process adjustments such as fine-tuning of production parameters and calibration of material input, the system-generated suggestions can be directly pushed to the corresponding technical department for manual review. After approval, the suggestions are issued to the on-site production machine with one click, and the operator executes the process adjustment upon receiving the instruction. Technical personnel can view issue details and download test data through the system, or manually fill in supplementary processing suggestions. Simultaneously, the process adjustment process is fully traceable. The system automatically records information such as the issuance time of the adjustment instruction, the receiving machine number, the operator, the adjustment implementation steps, and the adjustment completion time, forming a complete process adjustment traceability file for easy subsequent verification and review. For example, if a machine is found to produce products with a continuous weight of grade B and excessively thick rims, the system automatically generates a suggestion to "fine-tune the Z-axis compensation value parameter of the machined rim to the XX range." After review by the production technology department, this suggestion is issued to the machine, and the operator provides feedback in the system after completing the adjustment. The system records the entire process information.

[0049] Record the real-time output of the top mold and side molds. When the output reaches 18,000 pieces (90% of the preset threshold of 20,000 pieces), the key monitoring mode is activated. Statistically analyze the correlation data between output and dimensional deviations. If it is found that the aluminum usage has increased due to mold wear, resulting in a cost difference exceeding the design value by 5%, a red warning is triggered, a pop-up reminder is displayed on the system interface, and a text message is sent to the relevant person in charge. Mold technicians can use the system to filter key attention items, view the maintenance progress tracked by the Gantt chart, receive mold process optimization tracking orders, and provide feedback on the processing results.

[0050] The system automatically calculates the average weight of each product from the previous shift daily, and calculates the average weight of the current shift every 4 hours, comparing it with the design value. If the average weight of the current shift is lower than the average weight of the previous shift but within the standard range, the system interface displays the heat treatment control status of the product in green. If the average weight of the current shift is higher than the average weight of the previous shift, it displays in red and prompts the user to initiate a weight adjustment process. After the relevant personnel initiate the adjustment process, the system tracks the completion status of each step of "application-approval-adjustment-verification" in real time, recording the adjustment parameters and verification results.

[0051] The AI ​​module is used to perform step 6 in embodiment 1, store various operation records and analysis results, generate a structured knowledge base, train the model through AI algorithms, and provide data support for subsequent intelligent adjustment.

[0052] The retesting closed-loop process, knowledge base formation, and model training phase: After the technical department completes the problem handling or on-site process adjustment, it submits a processing completion application in the system. The system automatically triggers the retesting process, collecting the weight and size data of the adjusted product for verification, focusing on whether the weight meets the standard range and whether the quality inspection data meets the standards. If the retesting is qualified, the system reminds the initiating department to confirm the closed loop. If it is unqualified, it returns to the technical department to formulate a new process adjustment plan and executes the "opinion generation - review and issuance - process adjustment - process traceability - retesting verification" process again. All process data (including test data, handling opinions, adjustment traceability information, parameter adjustment full record, retesting results, etc.) are automatically archived, forming a knowledge base for later use. On the one hand, similar problems are referenced, and on the other hand, big data is simultaneously input to train the model. The model is continuously trained and optimized based on massive historical data. When similar problems of similar products are encountered in the future, the model can directly output the corresponding problem handling results and process parameter adjustment suggestions. Technicians only need to confirm and can send them to the field machine for direct process adjustment with one click, which greatly shortens the problem handling cycle. For complex parameter adjustment environments such as multi-parameter coupling and large environmental fluctuations, the model can generate multi-stage adaptive parameter adjustment solutions based on the complete historical parameter adjustment process records to ensure the accuracy and stability of process adjustment in complex scenarios. At the same time, complex cases that fail to meet the standards in retesting will be marked as key training samples to help the model continuously improve its adaptability to complex scenarios. Example

[0053] Please see Figure 4 As shown, the present invention also provides an electronic device 100; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0054] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the intelligent weight control method for aluminum wheel products described in Embodiment 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device 100 (such as audio data), etc. In addition, the memory 101 may include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other non-volatile solid-state storage device.

[0055] The at least one processor 102 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100, connecting various parts of the electronic device 100 via various interfaces and lines.

[0056] The memory 101 in the electronic device 100 stores multiple instructions to implement an intelligent weight control method for aluminum wheel products, and the processor 102 can execute the multiple instructions to implement an intelligent weight control method for aluminum wheel products. Example

[0057] If the modules / units integrated in the electronic device 100 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0058] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or its equivalents are included in this invention.

[0059] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0060] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0061] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0062] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for intelligent weight control of aluminum wheel products, characterized in that, Includes the following steps: Step 1: Connect with various equipment on site in real time, establish a data acquisition link, and collect multi-dimensional data during the production process of aluminum wheel products; Step 2: Process the multi-dimensional data, classify and calculate the metal utilization rate of casting wheels and casting wheels, calculate the level of individual pieces and the weight improvement, and determine the product weight grade. Step 3: Based on the multi-dimensional data of the product, sort the priority levels of product issues; Step 4: Record the real-time and cumulative output of the top mold and side mold in the multi-dimensional data, set the cost recovery threshold and cost difference threshold, and trigger an alarm if the corresponding data exceeds the cost recovery threshold and cost difference threshold. Step 5: Obtain the shift weight data for each product and determine whether the shift weight data is qualified. If it is not qualified, trigger an alarm. Step 6: Collect dimensional data from at least 10 adjusted products for verification. If all dimensional data are qualified, the retest is passed; if not all dimensional data are qualified, the retest is failed, and a new process adjustment plan is formulated until the product data is qualified.

2. The intelligent weight control method for aluminum wheel products as described in claim 1, characterized in that, The multi-dimensional data includes weighing data, dimensional data, production data, process data, quality data, and mold data.

3. The intelligent weight control method for aluminum wheel products as described in claim 2, characterized in that, In step 2, determining the product weight grade specifically involves: The determination is based on a preset product weight standard range, including: The weight is classified into four levels: weight within the standard range and lower than the design value, weight within the standard range but higher than the design value, weight exceeding the lower limit of the standard range, and weight exceeding the upper limit of the standard range.

4. The intelligent weight control method for aluminum wheel products as described in claim 1, characterized in that, In step 3, the priority level of product issues is sorted as follows: The semi-finished product weight is qualified compared with the design value, earning 1 star; the measured value of the rim thickness is within the standard range, earning 1 star; the measured value of the flange thickness is within the standard range, earning 2 stars; the measured value of the spoke thickness is within the standard range, earning 1 star. Products rated 4-5 stars are considered high priority, products rated 2-3 stars are considered medium priority, and products rated 1 star are considered low priority.

5. The intelligent weight control method for aluminum wheel products as described in claim 1, characterized in that, In step 4, the preset production of top mold and side mold exceeding 20,000 pieces is the threshold for recovering mold costs.

6. The intelligent weight control method for aluminum wheel products as described in claim 1, characterized in that, Step 5 specifically involves: Obtain the average weight of each product from the previous production shift and the average weight of the current shift over the last 4 hours. Compare the current shift's average weight with the average weight of the previous shift and the product's design value. If the current shift's average weight is greater than the average weight of the previous shift and exceeds the design value, issue a warning.

7. The intelligent weight control method for aluminum wheel products as described in claim 1, characterized in that, Step 6 also includes: Data is archived throughout the entire process to form a knowledge base for reference in similar issues later; simultaneously, the data is input into the big data training model for continuous training and optimization based on historical data.

8. A weight intelligent control system for aluminum wheel products, characterized in that, The intelligent weight control system for aluminum wheel products is used to implement the intelligent weight control method for aluminum wheel products as described in any one of claims 1 to 7, and includes the following modules: The data acquisition module is used to connect various on-site devices in real time, establish a data acquisition link, and collect multi-dimensional data during the production process of aluminum wheel products. The processing module is used to process multi-dimensional data, classify and calculate the metal utilization rate of casting wheels and casting wheels, calculate the level of individual pieces and improve weight, and determine the weight grade of products. The analysis module is used to prioritize product issues based on multi-dimensional product data; record the real-time and cumulative production of top and side dies in the multi-dimensional data; set cost recovery thresholds and cost difference thresholds; if the corresponding data exceeds the cost recovery threshold and cost difference threshold, an alert is triggered; acquire shift weight data for each product and determine whether the shift weight data is qualified; if it is not qualified, an alert is triggered; collect dimensional data of at least 10 adjusted products for verification; if all dimensional data are qualified, the retest passes; if not all dimensional data are qualified, the retest fails, and a new process adjustment plan is formulated until the product data is qualified. The AI ​​module is used for end-to-end data archiving, forming a knowledge base for reference in similar issues later; simultaneously, data is input into the big data training model, and continuous training and optimization are carried out based on historical data.

9. An electronic device, characterized in that, Including memory and processor; Memory, used to store computer programs; A processor is used to execute the computer program, which, when executed by the processor, implements the steps of the intelligent weight control method for aluminum wheel products as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the intelligent weight control method for aluminum wheel products as described in any one of claims 1 to 7.

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