Tire blank quality evaluation and processing method and system based on digital twinning

Abstract

The invention relates to the technical field of tire blank quality evaluation and processing, in particular to a tire blank quality evaluation and processing method and system based on digital twinning, and the method comprises the following steps: aiming at entity equipment required from tire blank molding to vulcanization in a tire production process in a data twinning system; constructing an equipment digital twinborn model and a process flow digital twinborn model; constructing a standard tire blank digital twinborn model, and controlling the entity production system to operate based on the standard tire blank digital twinborn model; receiving actual processing data fed back by the entity production system, and constructing an actual tire blank digital twinning model in the data twinning system; comparing the standard tire blank digital twin model with the actual tire blank digital twin model, and calculating tire blank quality deviation and quality classification; and based on a preset grading treatment strategy and the quality grading result of the tire blank, carrying out grading treatment on the tire blank. Therefore, quality evaluation can be carried out on the unvulcanized tire blank, grading treatment is carried out in time, resource waste is effectively reduced, the production cost is reduced, and the product quality is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of tire blank quality assessment and processing, and in particular to a tire blank quality assessment and processing method and system based on digital twins. Background Art

[0002] With the development of the automotive industry, the tire manufacturing industry has also been strongly driven. The rubber tire manufacturing process encompasses multiple steps, including raw material preparation, semi-component processing, tire building, vulcanization, quality inspection, and finished product storage and transportation. Furthermore, with the development and application of automated logistics technology, most of these tire manufacturing processes have become automated. However, existing tire manufacturing logistics systems often lack quality assessment and processing steps after the tire blank is built. Instead, vulcanization is performed first, followed by quality inspection. This results in significant resource waste. First, correctable tire blank quality issues, such as shoulder holes, hollow bead holes, or deformation, cannot be detected in advance, leading to scrapping after vulcanization. Second, tire blanks with significant and irreparable quality issues cannot be disposed of in advance, resulting in waste of materials and energy in the subsequent vulcanization process. Currently, with the vigorous development of new quality productivity, tire manufacturers urgently need innovative solutions to address these tire manufacturing issues, reduce resource waste, lower production costs, ensure product quality, and achieve green and sustainable development.

[0003] In the prior art, the patent with application number 202410283631.2 discloses a tire management system based on digital twins, including a digital twin management platform, a purchase order cloud, a mixing unit, a tire molding unit, a vulcanization unit, a detection unit, a warehousing unit, a serial number generation unit and a quality management unit; the digital twin management platform is used to maintain the operation of the digital twin tire management system; the purchasing unit is used to receive the purchase batch number and enter information on the purchased raw materials and ingredients, including the batch number and purchase time of the raw materials and ingredients; the mixing unit is used to receive the mixing traceability number and mix the raw materials and ingredients, and send the mixing traceability number; the tire molding unit is used to receive the molded tire blank traceability number and process the raw materials mixed by the mixing unit into a molded tire blank. It traces the entire process of the tire from raw materials to molded products with the cooperation of the serial number, finds the cause in time and solves the problem when it is found.

[0004] Patent CN119048416A discloses a tire inspection method, device, electronic device, and system. These methods capture tire images, including surface and X-ray images, to perform comprehensive inspections. These methods first preprocess the images, automatically identify and mark tire defects, and generate a digital twin image to aid analysis. Next, industrial cameras and X-ray equipment conduct circumferential inspections of the tires, collecting comprehensive tire inspection data. The data is then preprocessed, subjected to feature extraction, matching, and cluster analysis to identify valid data for tire defect information. Finally, this data is used to optimize a pre-defined tire inspection model, improving its accuracy and adaptability.

[0005] Patent CN119002407A discloses a method and device for adaptively correcting the assembly angle of automobile tires based on digital twins, including: S1: constructing an automobile tire model library; S2: constructing a digital twin system, including constructing a digital twin of a tire adaptive angle adjustment device and a control logic model, as well as a digital twin of the tire assembly process; S3: controlling the tire to be assembled to automatically adjust its angle; S4: determining whether the angle of the adjusted tire to be assembled is correct. S3 includes: S3.1: acquiring a point cloud image of the tire to be assembled and generating a digital twin of the tire to be assembled; S3.2: calculating the offset of the tire to be assembled, and the digital twin system performing adaptive angle adjustment simulation analysis to generate control parameters for the angle adjustment device; S3.3: controlling the angle of the tire to be assembled based on the compiled and parsed control parameters of the angle adjustment device.

[0006] Existing technologies based on digital twins can achieve traceability of the entire tire production process, tire quality inspection, and adaptive repair of tire assembly, but lack effective and specific methods or measures for quality assessment and processing of pre-vulcanized molded tires, including tire quality grading assessment methods, as well as logistics decisions, quality repairs, system closed-loop optimization and other measures based on the tire quality assessment results. Summary of the Invention

[0007] In view of this, the purpose of the present invention is to provide a tire blank quality assessment and processing method and system based on digital twins to overcome the current problem of being unable to assess the tire blank before vulcanization and thus avoid waste of resources.

[0008] To achieve the above objectives, the present invention adopts the following technical solutions:

[0009] In a first aspect, the present application provides a method for evaluating and processing embryo quality based on digital twins, comprising:

[0010] In the data twin system, digital twin models of equipment and process flow are constructed for the physical equipment required in the tire production process from tire blank molding to vulcanization.

[0011] Based on the tire order information, a standard tire embryo digital twin model is constructed in the data twin system, and the physical production system is controlled to perform operations based on the standard tire embryo digital twin model;

[0012] receiving actual processing data fed back by the physical production system, and constructing a digital twin model of the actual embryo in the data twin system;

[0013] Comparing the standard embryo digital twin model with the actual embryo digital twin model, calculating embryo quality deviation, and grading the embryo quality based on the embryo quality deviation;

[0014] Based on the preset grading strategy and the quality grading results of the embryos, the embryos are graded.

[0015] Furthermore, in some embodiments of the present application, the data twin system constructs a digital twin model of equipment and a digital twin model of the process flow for the physical equipment required in the tire production process from tire blank molding to vulcanization, including:

[0016] In the data twin system, a digital twin model of the physical equipment required for tire production, from blank molding to vulcanization, is constructed based on CAD, finite element modeling and simulation, and control logic modeling and simulation technologies. The equipment includes molding machines, vulcanizers, finishing equipment, and conveying equipment. The digital twin model of the equipment includes a geometric model, a behavioral model, and a physical model. The geometric model reflects the equipment's external dimensions, the behavioral model reflects the equipment's operating mode, and the physical model reflects the equipment's physical characteristics, including material and mechanical properties.

[0017] The process flow digital twin model is constructed based on discrete event simulation technology, and the process flow digital twin model is used to simulate the entire process from tire blank molding to vulcanization.

[0018] Furthermore, in some embodiments of the present application, the data twin system interacts with the physical production system through a preset virtual-reality interaction module.

[0019] Furthermore, in some embodiments of the present application, based on the tire order information, a standard tire embryo digital twin model is constructed in the data twin system, and based on the standard tire embryo digital twin model, the physical production system is controlled to perform operations, including:

[0020] Based on the geometric and physical information of the tire in the tire order information and according to historical and real-time processing time series data, a digital twin model of a standard tire blank before vulcanization is constructed in the data twin system;

[0021] The standard embryo digital twin model includes a geometric model, a physical model and a process model;

[0022] Among them, the geometric model includes the outline of the tire blank, the size and spatial position information of each component, and the components include the tread, cord layer and steel wire. The physical model includes the proportion of unvulcanized rubber components of the molded tire blank, mechanical parameters, cord physical properties, and vulcanization dynamics simulation results. The mechanical parameters include Mooney viscosity and elastic modulus, and the cord physical properties include strength and angular distribution; the process model includes the actual key process parameters for tire blank molding, and the actual key process parameters include molding pressure, temperature, bonding speed, mold size, and rubber extrusion rate.

[0023] Furthermore, in some embodiments of the present application, comparing the standard embryo digital twin model with the actual embryo digital twin model, calculating the embryo quality deviation, and grading the embryo quality based on the embryo quality deviation includes:

[0024] Comparing the standard embryo digital twin model with the actual embryo digital twin model, and calculating the geometric dimension deviation and physical dimension deviation of the embryo respectively;

[0025] Determine the comprehensive quality deviation index of the embryo based on the geometric dimension deviation and physical dimension deviation of the embryo;

[0026] The quality level of the embryo is determined based on the comprehensive quality deviation index of the embryo and the preset defect judgment threshold, where the quality levels include level 1 quality, level 2 quality and level 3 quality; where level 1 quality indicates that the embryo quality is qualified, level 2 quality indicates that the embryo has minor defects, and level 3 quality indicates that the embryo has major defects.

[0027] Furthermore, in some embodiments of the present application, the step of performing grading on the embryos based on the preset grading strategy and the embryo quality grading results includes:

[0028] The first-class quality tire blanks are transported to the storage or vulcanization unit in the physical production system for storage or vulcanization;

[0029] For the tire blanks with the second-level quality, they are transported to the tire blank trimming unit in the physical production system for trimming based on the trimming plan, which is a plan generated by the digital twin system based on the quality deviation;

[0030] For the third-level quality tire blanks, they are transported to the defective product recovery unit in the physical production system for recycling.

[0031] Furthermore, in some embodiments of the present application, the following is further included:

[0032] Obtaining information about the trimmed tire blank and re-evaluating its quality based on the information;

[0033] If the quality meets the standards, it will be transported to the storage or vulcanization unit in the physical production system for storage or vulcanization; if the quality does not meet the standards, it will be transported to the tire blank trimming unit in the physical production system for trimming, or after the number of corrections reaches a preset number, it will be transported to the defective product recovery unit in the physical production system for recycling.

[0034] Furthermore, in some embodiments of the present application, the following is further included:

[0035] When the quality of the tire embryo does not meet the standard, calculating the process deviation based on the standard tire embryo digital twin model and the actual tire embryo digital twin model;

[0036] If the process deviation is greater than the first process deviation threshold, the abnormal key process parameters are displayed for manual equipment maintenance;

[0037] If the process deviation is less than the first process deviation threshold, and if the process deviation is greater than the second process deviation threshold, the molding machine is guided to correct and compensate the control parameters based on the PID control technology;

[0038] If the process deviation is less than the second process deviation threshold, the digital twin model is optimized based on the large model technology and the VV&A method.

[0039] Furthermore, in some embodiments of the present application, the optimization of the digital twin model based on the large model technology and the VV&A method includes:

[0040] Determine the molding machine equipment information to be optimized in the digital twin model and obtain the historical data of the molding machine;

[0041] Clean and normalize the historical data, perform in-depth analysis and deviation tracing on the model mismatch causes of the molding machine based on the preset digital twin model optimization model, and output a parameter correction plan;

[0042] Based on the VV&A method, the parameter modification scheme is simulated and tested, and the optimization effect is confirmed through actual equipment production verification;

[0043] If the optimization effect meets the requirements, the model of the molding machine in the digital twin model is updated based on the parameter correction plan.

[0044] In a second aspect, the present application provides a tire embryo quality assessment and processing system based on digital twins, characterized by comprising: a digital twin system and a physical production system;

[0045] The digital twin system includes a digital twin model of equipment and a digital twin model of the process flow built for the physical equipment required in the tire production process from tire blank molding to vulcanization;

[0046] The digital twin system also includes a standard tire embryo digital twin model constructed based on tire order information, and the standard tire embryo digital twin model is used to control the physical production system to perform operations;

[0047] The digital twin system is also used to receive actual processing data fed back by the physical production system;

[0048] The digital twin system further includes an actual embryo digital twin model constructed based on the actual processing data;

[0049] The digital twin system is also used to compare the standard embryo digital twin model with the actual embryo digital twin model, calculate the embryo quality deviation, and grade the embryo quality based on the embryo quality deviation; and grade the embryo based on the preset grading strategy and the embryo quality grading results.

[0050] The present invention relates to the technical field of tire blank quality assessment and processing, and specifically to a tire blank quality assessment and processing method and system based on digital twins. The method comprises constructing a digital twin model of equipment and a digital twin model of the process flow for the physical equipment required in the tire production process from blank forming to vulcanization in a data twin system; constructing a digital twin model of a standard blank in the data twin system based on tire order information, and controlling the physical production system to perform operations based on the digital twin model of the standard blank; receiving actual processing data fed back by the physical production system, and constructing a digital twin model of the actual blank in the data twin system; comparing the digital twin model of the standard blank with the digital twin model of the actual blank, calculating the blank quality deviation, and performing quality grading on the blank based on the blank quality deviation; and performing quality grading on the blank based on a preset grading processing strategy and the quality grading results of the blank. In this way, the quality of the unvulcanized blank can be assessed and processed based on the quality assessment results, avoiding the discovery of problems after vulcanization, effectively reducing resource waste, lowering production costs, and ensuring product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 It is a flow chart of the tire embryo quality assessment and processing method based on digital twins provided in an embodiment of the present invention.

[0053] Figure 2It is a structural diagram of the digital twin-based embryo quality assessment and processing system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other implementations obtained by those of ordinary skill in the art without inventive effort are within the scope of protection of the present invention.

[0055] Figure 1 This is a flow chart of the method for evaluating and processing embryo quality based on digital twins provided by an embodiment of the present invention. Figure 1 , this embodiment may include the following steps:

[0056] S101. In the data twin system, for the physical equipment required in the tire production process from tire blank molding to vulcanization, build equipment digital twin models and process flow digital twin models.

[0057] S102. Based on the tire order information, a standard tire blank digital twin model is constructed in the data twin system, and the physical production system is controlled to perform operations based on the standard tire blank digital twin model.

[0058] S103: Receive actual processing data fed back by the physical production system, and build a digital twin model of the actual embryo in the data twin system.

[0059] S104: Compare the standard embryo digital twin model with the actual embryo digital twin model, calculate the embryo quality deviation, and grade the embryo quality based on the embryo quality deviation.

[0060] S105: Based on the preset grading strategy and the quality grading result of the embryo, the embryo is graded.

[0061] The present invention relates to the technical field of tire blank quality assessment and processing, and specifically to a tire blank quality assessment and processing method and system based on digital twins. The method comprises constructing a digital twin model of equipment and a digital twin model of the process flow for the physical equipment required in the tire production process from blank forming to vulcanization in a data twin system; constructing a digital twin model of a standard blank in the data twin system based on tire order information, and controlling the physical production system to perform operations based on the digital twin model of the standard blank; receiving actual processing data fed back by the physical production system, and constructing a digital twin model of the actual blank in the data twin system; comparing the digital twin model of the standard blank with the digital twin model of the actual blank, calculating the blank quality deviation, and performing quality grading on the blank based on the blank quality deviation; and performing quality grading on the blank based on a preset grading processing strategy and the quality grading results of the blank. In this way, the quality of the unvulcanized blank can be assessed and processed based on the quality assessment results, avoiding the discovery of problems after vulcanization, effectively reducing resource waste, lowering production costs, and ensuring product quality.

[0062] Furthermore, in an embodiment of the present application, a digital twin model of equipment and a digital twin model of the process flow are constructed in the data twin system for the physical equipment required for the tire production process from embryonic tire molding to vulcanization, including: in the data twin system, a digital twin model of equipment is constructed based on CAD, finite element modeling and simulation, and control logic modeling and simulation technologies for the physical equipment required for the tire production process from embryonic tire molding to vulcanization; and a digital twin model of the process flow is constructed based on discrete event simulation technology.

[0063] Specifically, in this application, a digital twin model of the tire blank production logistics system can be constructed in the digital twin system, specifically including a digital twin model of equipment and a digital twin model of the process flow, and through the virtual-reality interaction module, data interaction and dynamic mapping between the physical production system of the tire and the digital twin system can be realized.

[0064] In practical applications, a multi-dimensional digital twin model of equipment can be constructed based on technologies such as CAD, finite element modeling and simulation, and control logic modeling and simulation. Among them, the equipment includes physical equipment required for tire blank molding to vulcanization, such as molding machines, vulcanizers, finishing equipment, and conveying equipment. The digital twin model of equipment includes geometric models, behavioral models, and physical models. Among them, the geometric model reflects the appearance and dimensions of the equipment and is used for equipment visualization. The behavioral model reflects the operating mode of the equipment and is used to simulate the movement laws of the equipment. The physical model is used to reflect the physical characteristics of the equipment, including materials, mechanical properties and other characteristics, and can be used to evaluate the health status of the equipment. In addition, a process flow model is constructed based on discrete event simulation (DES) technology to simulate the entire process of tire blank from molding to vulcanization, which can be used to pre-verify the impact of process parameters on quality (such as the impact of vulcanization temperature fluctuations on rubber properties).

[0065] On this basis, based on tire order information, a standard tire embryo digital twin model is constructed in the data twin system, and the physical production system is controlled to perform operations based on the standard tire embryo digital twin model.

[0066] Specifically, the digital twin system obtains tire order information and constructs a digital twin model M of the unvulcanized standard tire blank of the corresponding order based on the tire's geometry, physical properties, real-time vulcanization parameters, and tire blank properties. std and sends molding operation instructions to the tire blank molding unit of the physical production system to control its operation.

[0067] In actual applications, after receiving a tire order, the digital twin system can use the built-in digital twin model construction module to automatically build a pre-vulcanized standard tire digital twin model M based on the geometric and physical property information of the tire in the order, and based on historical and real-time processing time series data and simulation derivation technology. std , serving as an important reference for subsequent embryo quality assessment and trimming.

[0068] It should be noted that the standard tire embryo digital twin model in this application is mainly composed of the associated fusion of geometric model, physical model and process model.

[0069] The geometric model includes key geometric dimension information such as the outline of the tire blank, the size and spatial position of each component (such as tread, cord layer, steel wire), and can be expressed by mathematical formula as I g =[d g1 , d g2 ......, d gi ],(d g Indicates a key geometric feature parameter). In addition, the geometric model can also be used for 3D visualization interaction and display functions to help users intuitively understand the appearance, shape and logistics position of the embryo.

[0070] The physical model covers key physical dimension information including the proportion of unvulcanized rubber components of the molded tire, mechanical parameters (Mooney viscosity, elastic modulus), physical properties of the cord (strength, angle distribution), and vulcanization dynamics simulation results. It can be expressed in mathematical formula as I P =[d p1 , d p2 ......, d pj ],(d p represents a key physical characteristic parameter).

[0071] The process model covers the actual key process parameters of tire blank molding, including molding pressure, temperature, bonding speed, mold size, rubber extrusion rate and other data, which can be expressed by mathematical formula I r=[d r1 , d r2 ......, d rk ],(d r represents a key molding process parameter).

[0072] It can be seen from this that the standard embryo digital twin model can be expressed as M std ={I g_std , I p_std , I r_std}.

[0073] At this time, the tire blank molding unit receives the operation instruction, starts the tire blank molding operation according to the process model of the standard tire blank digital twin model, and feeds back the actual molding process data, i.e., the actual processing data, to the digital twin system. The digital twin system dynamically constructs the actual tire blank digital twin model M based on the real-time data. real .

[0074] In actual applications, after the tire blank forming unit receives the operation instruction from the digital twin system, multiple sets of tire blank forming machines combine the cord layer, tread, sidewall, bead and other components into a tire blank according to the process parameters and processing sequence in the process model of the standard tire blank digital twin model. At this time, various sensors installed on the equipment include high-precision sensors such as light thickness gauges, visual cameras, force sensors (monitoring fitting pressure), etc., which collect data such as the molded tire blank size, surface defects, and component fitting stress in real time. The equipment transmits the operating parameters in real time and transmits the above data to the digital twin system through the OPC UA protocol. Based on the received data, the digital twin system dynamically constructs a digital twin model of the actual tire blank after molding through the model construction module, which can be expressed as M real ={I g_real , I p_real , I r_real It should be noted that the actual embryo digital twin model should be aligned with the standard embryo digital twin model parameters. If they are not aligned, system errors and troubleshooting are required.

[0075] Furthermore, in an embodiment of the present application, the standard embryo digital twin model and the actual embryo digital twin model are compared, the embryo quality deviation is calculated, and the embryo quality is graded based on the embryo quality deviation, including: comparing the standard embryo digital twin model and the actual embryo digital twin model, and calculating the geometric dimension deviation and physical dimension deviation of the embryo respectively; determining the comprehensive quality deviation index of the embryo based on the geometric dimension deviation and physical dimension deviation of the embryo; determining the quality level of the embryo based on the comprehensive quality deviation index of the embryo and a preset defect judgment threshold, wherein the quality levels include first-level quality, second-level quality and third-level quality; wherein, first-level quality indicates that the embryo quality is qualified, second-level quality indicates that the embryo has minor defects, and third-level quality indicates that the embryo has major defects.

[0076] Specifically, after the tire blank is formed, the digital twin system first compares and calculates the geometric dimension deviation and physical dimension deviation between the actual tire blank digital twin model and the standard tire blank digital twin model to obtain a comprehensive deviation index, and then performs quality grading judgment on the actual formed tire blank to achieve quality assessment.

[0077] In terms of calculating the geometric dimension deviation, the comprehensive geometric deviation is calculated by comparing the key geometric feature parameters in the above two models. The specific formula is:

[0078]

[0079] Among them, δ gi Represents a key geometric characteristic parameter d gi The deviation value is expressed as:

[0080]

[0081] Among them, w gi Representative deviation δ gi The weight, n g Represents the number of key geometric features.

[0082] In terms of calculating physical dimension deviations, by comparing the key physical characteristic parameters of the two models mentioned above, the Mooney-Rivlin hyperelastic model is used to analyze the nonlinear mechanical properties of the tire twin model material. The vulcanization kinetic model Arrhenius equation is introduced to simulate the vulcanization reaction. The digital twin model of the process flow is used to pre-verify the impact of environmental fluctuations on the rubber compound properties during the tire logistics process. The comprehensive logistics deviation is obtained. The specific formula is:

[0083]

[0084] Among them, δ pj Represents a key physical characteristic parameter d pi The deviation value can be expressed as:

[0085]

[0086] Among them, w pj Represents physical deviation δ pj The weight, n p Represents the number of key physical characteristics.

[0087] Finally, the comprehensive quality deviation index is obtained: δ = w g δ g +w p δ p ,(where w g Represents the comprehensive geometric deviation δ g The weight, w p Represents the comprehensive physical deviation δ p weight.)

[0088] On this basis, the digital twin system uses its internal quality assessment module to make tire quality grading judgments based on the obtained comprehensive quality deviation index and the preset defect judgment threshold. The preset thresholds include the slight defect threshold δ th1 (5% is acceptable), major defect threshold δ th2 (15% is acceptable), and the quality judgment is divided into three levels, as follows:

[0089] First-class quality: qualified embryo (δ≤δ th1 );

[0090] Second level quality: slight defects (δ th1 ≤δ≤δ th2 ):

[0091] Level 3 quality: major defects (δ>δ th2 ).

[0092] On this basis, the tire blanks are graded based on the preset grading processing strategy and the quality grading results of the tire blanks, including: for tire blanks of level one quality, they are transported (via preset conveying equipment) to the storage or vulcanization unit in the physical production system for storage or vulcanization; for tire blanks of level two quality, they are transported to the tire blank trimming unit in the physical production system for trimming based on the trimming plan, which is a plan generated by the digital twin system based on quality deviation; for tire blanks of level three quality, they are transported to the defective product recovery unit in the physical production system for recycling.

[0093] Specifically, based on the tire quality grading assessment results, the digital twin system makes grading decisions and processes tires of different quality grades, including logistics diversion, quality repair, and defective product recycling. Tires of grade one quality can be directly sent to the storage or vulcanization unit via conveyor equipment; tires of grade two quality need to be sent to the trimming unit via conveyor equipment for trimming. The digital twin system generates a tire trimming plan based on quality deviations and guides and monitors the trimming process. The system can use the least squares method to fit the parameters of qualified tires and calculate the trimming compensation amount. The maintenance equipment adjusts the pressure and temperature parameters based on the compensation amount. The trimmed tires are rescanned and the tire digital twin data is updated. Tires of grade three quality are sent directly to the recycling unit via the conveyor unit for material classification and recycling.

[0094] At the same time, for the second-level quality tire blanks that have been refurbished, information about the repaired tire blanks is obtained, and the quality is re-determined based on the information; if the quality meets the standards, it is transported to the storage or vulcanization unit in the physical production system for storage or vulcanization; if the quality does not meet the standards, it is transported to the tire blank repair unit in the physical production system for repair, or after the number of corrections reaches a preset number, it is transported to the defective product recovery unit in the physical production system for recycling.

[0095] On this basis, when the quality of the tire blank does not meet the standard, the process deviation can be calculated based on the digital twin model of the standard tire blank and the digital twin model of the actual tire blank; if the process deviation is greater than the first process deviation threshold, the abnormal key process parameters will be displayed for manual equipment maintenance; if the process deviation is less than the first process deviation threshold, and if the process deviation is greater than the second process deviation threshold, the PID control technology will be used to guide the molding machine to correct and compensate the control parameters; if the process deviation is less than the second process deviation threshold, the digital twin model will be optimized based on the large model technology and VV&A method.

[0096] By comparing and analyzing the process models of the actual tire embryo digital twin model and the standard tire embryo digital twin model, the process deviation is obtained. The specific formula is:

[0097]

[0098] Among them, δ rk Represents the key process characteristic parameter d rk Deviation value, that is:

[0099]

[0100] Among them, w rk Representative deviation δ rk The weight of the comprehensive process deviation threshold is set as δ r opt_1 (0.5% is acceptable) and the upper limit δ ropt_2 (10% is acceptable).

[0101] According to δ rk Make judgments and decisions based on the size of the process deviation threshold:

[0102] If δ r >δ r opt_2 , it is determined that there is a fault in the equipment or a large abnormality in the process parameters, which requires manual maintenance. The digital twin system will issue an alarm through the human-computer interaction module and display all abnormal key process parameters to guide workers to repair the equipment until it meets the processing accuracy.

[0103] If δ r opt_1 ≤δ r ≤δ r opt_2 , it is determined that the equipment process parameters deviate slightly from the process standards. The digital twin system guides the molding machine to correct and compensate the relevant parameters based on PID control technology until δ r <δ r opt_1 , to achieve adaptive adjustment and optimization of process parameters.

[0104] If δ r <δ r opt_1 , it is determined that the digital twin model of the equipment is mismatched with the actual operating performance of the equipment, and production and processing cannot be guided by precise virtual-real mapping. It is necessary to combine the big model technology and the VV&A (Verification, Validation, and Accreditation) method to optimize the digital twin model of the molding machine, including: determining the molding machine equipment information to be optimized in the digital twin model, and obtaining the historical data of the molding machine; cleaning and normalizing the historical data, and conducting in-depth analysis and deviation tracing of the model mismatch causes of the molding machine based on the preset digital twin model optimization big model, and outputting a parameter correction plan; simulating and testing the parameter correction plan based on the VV&A method, and verifying it through actual equipment production to determine the optimization effect; if the optimization effect meets the standard, updating the model of the molding machine in the digital twin model based on the parameter correction plan.

[0105] For example: First, mark the molding machine device ID that needs model optimization, and obtain the historical data of the molding machine; then clean and normalize the historical data of the device, and use the built-in digital twin model optimization large model of the digital twin system to conduct in-depth analysis and deviation traceability of the causes of mismatch of the molding machine digital twin model, and output the candidate molding machine digital twin model parameter correction plan; then use the VV&A method simulation test to verify the rationality of the molding machine digital twin model correction plan, and then verify its effectiveness through small-batch production of actual equipment, and record the optimization effect of key indicators; finally, the molding machine digital twin optimization model certified by the VV&A method is automatically synchronized to the digital twin system to replace the old model, record version information and change content, and monitor the consistency between the model output and actual data in real time in subsequent production, and continuously accumulate optimization experience.

[0106] The digital twin-based tire blank quality assessment and processing method provided in this application can realize quality grading assessment, grading processing and system closed-loop optimization of the tire blank before vulcanization, which can reduce resource waste in the tire manufacturing process, reduce production costs and ensure product quality.

[0107] Based on the same inventive concept, the present application also provides a tire quality assessment and processing system based on digital twins, which is used to implement the above-mentioned method embodiment. The system includes a digital twin system and a physical production system; the digital twin system includes an equipment digital twin model and a process flow digital twin model constructed for the physical equipment required from tire molding to vulcanization in the tire production process; the digital twin system also includes a standard tire digital twin model constructed based on tire order information, and the standard tire digital twin model is used to control the physical production system to perform operations; the digital twin system is also used to receive actual processing data feedback from the physical production system; the digital twin system also includes an actual tire digital twin model constructed based on actual processing data; the digital twin system is also used to compare the standard tire digital twin model with the actual tire digital twin model, calculate the tire quality deviation, and perform quality grading on the tire based on the tire quality deviation; and grade the tire based on the preset grading processing strategy and the quality grading results of the tire.

[0108] Figure 2 FIG is a structural diagram of a tire embryo quality assessment and processing system based on digital twins provided in an embodiment of the present invention. Figure 2 As shown, in this embodiment, the system may include a physical production system, a virtual-real interaction module, and a digital twin system. The physical production system and the digital twin system are connected and interact with each other through the virtual-real interaction module, jointly completing the entire process of the tire blank, from forming to quality assessment, grading, and system closed-loop optimization.

[0109] Specifically, the physical production system serves as the physical execution carrier for tire blank manufacturing. It is composed of units such as tire blank molding, trimming, defective product recovery and storage or vulcanization. The units work together to achieve orderly operation of the entire tire blank production process.

[0110] The tire blank forming unit is used to form the tire blank. Based on the standard model process parameters transmitted by the digital twin system, it uses multi-station forming equipment to assemble components such as the cord ply, tread, and bead according to preset process requirements. Simultaneously, this unit uses sensors to collect multi-dimensional data such as the tire blank's dimensions, surface topography, and fitting stress in real time. This data is then transmitted to the digital twin system for the construction of an actual tire blank model, providing basic data support for subsequent quality assessments and enabling real-time interaction between the production process and the virtual model.

[0111] The tire blank repair unit processes tires identified as having minor defects after quality assessment. This unit receives targeted repair plans generated by the digital twin system and, using precision-controlled equipment, performs contour corrections, thickness compensation, and other operations on the tire blanks. After these corrections, the blanks are retested, an updated model is generated, and resubmitted for quality assessment to ensure that the blanks meet quality standards, effectively improving product qualification rates and achieving efficient resource utilization.

[0112] The defective product recovery unit is responsible for handling tire blanks with major defects or those that remain substandard after correction. Automated equipment sorts and recycles these blanks, maximizing the use of available materials and minimizing resource waste. Detailed defect types are recorded and fed back into the digital twin system, providing empirical evidence for analyzing the root causes of quality issues, optimizing production processes, and refining digital models, driving continuous improvement in the production system.

[0113] The storage or vulcanization units implement differentiated processing based on the tire quality grade. Qualified tires are directly sent to the vulcanization unit or storage unit. Furthermore, during the vulcanization process, vulcanization equipment integrated with a digital twin model dynamically adjusts vulcanization process parameters based on the actual tire model parameters. This ensures precise and controllable vulcanization, avoids over- or under-vulcanization, and ensures stable vulcanization quality. In the storage phase, standardized access management is implemented for tires, ensuring the continuity and coordination of the production process and providing orderly support for subsequent processes.

[0114] The digital twin system, serving as the virtual mirror and intelligent decision-making core for the entire tire production process, achieves precise mirroring and dynamic optimization of the physical production system by constructing a multi-dimensional digital twin model, integrating data processing and analysis capabilities, and implementing closed-loop quality assessment and decision-making control. Comprising modules for model building, data processing, quality assessment, decision-making control, and human-computer interaction, the system forms a complete technological chain from physical data collection to virtual analysis and decision-making, providing core support for tire quality testing and production process optimization.

[0115] Among them, the model construction module is the underlying architectural foundation of the digital twin system. It integrates the digital twin model construction theory and large model technology to realize the multi-dimensional, multi-level, and full-factor digital representation of physical production factors, form a high-precision digital twin model of equipment, process flow and embryo, and perform real-time optimization and improvement.

[0116] The data processing module provides high-quality data support for the digital twin system's precise modeling, intelligent evaluation, and decision-making optimization, achieving closed-loop management from data acquisition to knowledge transformation. This module integrates data transmission and reception, cleaning, fusion, analysis, statistics, and storage, forming a comprehensive data management system. The data transmission and reception function utilizes IoT transmission protocols to enable bidirectional interaction between physical equipment and virtual models, collecting real-time data from multiple sources, including tire dimensions, equipment parameters, and order information, and accurately issuing process instructions. The data cleaning function utilizes noise filtering and outlier removal techniques to correct sensor errors and ensure data reliability. The data fusion function integrates heterogeneous data, including geometric, physical, and process data, through spatiotemporal alignment and format unification, forming a standardized input set. The data analysis function can be used to discover correlations between quality deviations and process parameters. The data statistics function aggregates historical data across multiple dimensions to generate quality trend and equipment data reports. The data storage function utilizes a layered architecture, with a time series database storing high-frequency real-time data to support real-time monitoring, and a relational database storing model parameters and historical records for traceability.

[0117] The quality assessment module achieves quantitative assessment and grading of tire quality by comparing key parameters of the standard tire model with the actual tire model.

[0118] Based on quality assessment results, the decision-making and control module generates targeted production strategies, achieving closed-loop control of logistics scheduling, defect repair, and process optimization. In terms of tiered processing strategies, the flow of tires is dynamically planned based on their quality level (e.g., qualified tires are prioritized for vulcanization, those with minor defects are sent to the finishing unit, and those with major defects are recycled), ensuring the rational allocation of production resources.

[0119] The human-machine interaction module provides a visual interface and manual intervention interface, enabling human-machine collaboration. The digital twin system uses the HMI module's 3D visualization interface to display production line operating status, equipment health, and tire quality distribution in real time, enabling operators to intuitively grasp the overall production picture. Furthermore, when quality defects or equipment failures are detected, operators are alerted through audio and visual alarms, SMS notifications, and other means. Interactive interfaces are also provided for manually adjusting process parameters and triggering temporary maintenance, enhancing the system's flexibility and reliability.

[0120] The virtual-reality interaction module is the link between the physical world and the virtual world. It can realize device protocol conversion and efficient data transmission through the edge computing gateway, ensuring real-time synchronization between physical devices and digital twin systems; with the help of industrial Ethernet, OPC UA protocol and application programming interface (API), the digital twin system can be integrated with upper-level systems such as manufacturing execution system (MES), enterprise resource planning (ERP), warehouse management system (WMS), etc., to achieve the whole process from order demand to production execution.

[0121] In terms of data transmission, it includes uplink data transmission and downlink data transmission. Uplink data refers to data flowing from the physical to the virtual, including sensor data and equipment operating parameters of the molding unit, trimming process data and secondary inspection results of the trimming unit, and waste type, recycling volume, and processing status information of the recycling unit. Downlink data refers to data flowing from the virtual to the physical, including operation instructions (molding process parameters, trimming compensation amount, conveying path) and manual intervention instructions (such as forced recycling of a batch of tire blanks) issued by the digital twin system.

[0122] The specific implementation scheme of the digital twin-based embryo quality assessment and processing system provided in the embodiments of the present application can refer to the implementation scheme of the digital twin-based embryo quality assessment and processing method in any of the above embodiments, which will not be repeated here.

[0123] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0124] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" is at least two.

[0125] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0126] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0127] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0128] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0129] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0130] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0131] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for evaluating and processing embryo quality based on digital twins, characterized in that: include: In the data twin system, digital twin models of equipment and process flow are constructed for the physical equipment required in the tire production process from tire blank molding to vulcanization. Based on the tire order information, a standard tire embryo digital twin model is constructed in the data twin system, and the physical production system is controlled to perform operations based on the standard tire embryo digital twin model; receiving actual processing data fed back by the physical production system, and constructing a digital twin model of the actual embryo in the data twin system; Comparing the standard embryo digital twin model with the actual embryo digital twin model, calculating embryo quality deviation, and grading the embryo quality based on the embryo quality deviation; Based on the preset grading strategy and the quality grading results of the embryos, the embryos are graded.

2. The method for evaluating and processing tire embryo quality based on digital twin according to claim 1, characterized in that: In the data twin system, the equipment digital twin model and the process digital twin model are constructed for the physical equipment required in the tire production process from tire blank molding to vulcanization, including: In the data twin system, a digital twin model of the physical equipment required for tire production, from blank molding to vulcanization, is constructed based on CAD, finite element modeling and simulation, and control logic modeling and simulation technologies. The equipment includes molding machines, vulcanizers, finishing equipment, and conveying equipment. The digital twin model of the equipment includes a geometric model, a behavioral model, and a physical model. The geometric model reflects the equipment's external dimensions, the behavioral model reflects the equipment's operating mode, and the physical model reflects the equipment's physical characteristics, including material and mechanical properties. The process flow digital twin model is constructed based on discrete event simulation technology, and the process flow digital twin model is used to simulate the entire process from tire blank molding to vulcanization.

3. The method for evaluating and processing tire embryo quality based on digital twin according to claim 1, characterized in that: The data twin system interacts with the physical production system through a preset virtual-reality interaction module.

4. The method for evaluating and processing tire embryo quality based on digital twin according to claim 1, characterized in that: The method includes constructing a standard tire embryo digital twin model in the data twin system based on the tire order information, and controlling the physical production system to perform operations based on the standard tire embryo digital twin model, including: Based on the geometric and physical information of the tire in the tire order information and according to historical and real-time processing time series data, a digital twin model of a standard tire blank before vulcanization is constructed in the data twin system; The standard embryo digital twin model includes a geometric model, a physical model and a process model; Among them, the geometric model includes the outline of the tire blank, the size and spatial position information of each component, and the components include the tread, cord layer and steel wire. The physical model includes the proportion of unvulcanized rubber components of the molded tire blank, mechanical parameters, cord physical properties, and vulcanization dynamics simulation results. The mechanical parameters include Mooney viscosity and elastic modulus, and the cord physical properties include strength and angular distribution; the process model includes the actual key process parameters for tire blank molding, and the actual key process parameters include molding pressure, temperature, bonding speed, mold size, and rubber extrusion rate.

5. The method for evaluating and processing tire embryo quality based on digital twin according to claim 4, characterized in that: The comparing the standard embryo digital twin model with the actual embryo digital twin model, calculating the embryo quality deviation, and grading the embryo quality based on the embryo quality deviation includes: Comparing the standard embryo digital twin model with the actual embryo digital twin model, and calculating the geometric dimension deviation and physical dimension deviation of the embryo respectively; Determine the comprehensive quality deviation index of the embryo based on the geometric dimension deviation and physical dimension deviation of the embryo; The quality level of the embryo is determined based on the comprehensive quality deviation index of the embryo and the preset defect judgment threshold, where the quality levels include level 1 quality, level 2 quality and level 3 quality; where level 1 quality indicates that the embryo quality is qualified, level 2 quality indicates that the embryo has minor defects, and level 3 quality indicates that the embryo has major defects.

6. The method for evaluating and processing embryo quality based on digital twin according to claim 5, characterized in that: The step of performing grading on the embryos based on the preset grading strategy and the embryo quality grading results includes: The first-class quality tire blanks are transported to the storage or vulcanization unit in the physical production system for storage or vulcanization; For the tire blanks with the second-level quality, they are transported to the tire blank trimming unit in the physical production system for trimming based on the trimming plan, which is a plan generated by the digital twin system based on the quality deviation; For the third-level quality tire blanks, they are transported to the defective product recovery unit in the physical production system for recycling.

7. The method for evaluating and processing embryo quality based on digital twin according to claim 6, characterized in that: Also includes: Obtaining information about the trimmed tire blank and re-evaluating its quality based on the information; If the quality meets the standards, it is transported to the storage or vulcanization unit in the physical production system for storage or vulcanization; If the quality does not meet the standards, it will be transported to the tire blank trimming unit in the physical production system for trimming, or after the number of corrections reaches a preset number, it will be transported to the defective product recovery unit in the physical production system for recycling.

8. The method for evaluating and processing embryo quality based on digital twin according to claim 7, characterized in that: Also includes: When the quality of the tire embryo does not meet the standard, calculating the process deviation based on the standard tire embryo digital twin model and the actual tire embryo digital twin model; If the process deviation is greater than the first process deviation threshold, the abnormal key process parameters are displayed for manual equipment maintenance; If the process deviation is less than the first process deviation threshold, and if the process deviation is greater than the second process deviation threshold, the molding machine is guided to correct and compensate the control parameters based on the PID control technology; If the process deviation is less than the second process deviation threshold, the digital twin model is optimized based on the large model technology and the VV&A method.

9. The method for evaluating and processing tire embryo quality based on digital twin according to claim 8, characterized in that: The optimization of the digital twin model based on large model technology and VV&A method includes: Determine the molding machine equipment information to be optimized in the digital twin model and obtain the historical data of the molding machine; Clean and normalize the historical data, perform in-depth analysis and deviation tracing on the model mismatch causes of the molding machine based on the preset digital twin model optimization model, and output a parameter correction plan; Based on the VV&A method, the parameter modification scheme is simulated and tested, and the optimization effect is confirmed through actual equipment production verification; If the optimization effect meets the requirements, the model of the molding machine in the digital twin model is updated based on the parameter correction plan.

10. A tire quality assessment and processing system based on digital twins, characterized in that: include: Digital twin systems and physical production systems; The digital twin system includes a digital twin model of equipment and a digital twin model of the process flow built for the physical equipment required in the tire production process from tire blank molding to vulcanization; The digital twin system also includes a standard tire embryo digital twin model constructed based on tire order information, and the standard tire embryo digital twin model is used to control the physical production system to perform operations; The digital twin system is also used to receive actual processing data fed back by the physical production system; The digital twin system further includes an actual embryo digital twin model constructed based on the actual processing data; The digital twin system is also used to compare the standard embryo digital twin model with the actual embryo digital twin model, calculate the embryo quality deviation, and grade the embryo quality based on the embryo quality deviation; and grade the embryo based on the preset grading strategy and the embryo quality grading results.

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