Method, apparatus, device, medium, and product for manufacturing optimization of composites

Abstract

The embodiment of the application provides a composite material manufacturing optimization method, device, equipment, medium and product, and belongs to the technical field of electric digital data processing. The method comprises the following steps: based on product performance condition parameters, calling a composite material layering rule model to match a composite material layering topological structure; establishing a mapping relationship between geometric characteristic parameters and material curing attributes, and determining physical characteristics; calling a list from a manufacturing process parameter library; generating a layering process flow and parameters according to the topological structure and the production material list; generating a production task based on the list, the layering process flow and the parameters; executing the production task, synchronously collecting quality monitoring data and equipment operation data; and optimizing the production task based on an optimization mode, the quality monitoring data and the equipment operation data. According to the scheme of the application, automatic flow of design information to manufacturing information can be realized, so that the composite material forming quality and production efficiency are improved.

Description

Technical Field

[0001] This application belongs to the field of electronic digital data processing technology, and in particular relates to a method, apparatus, equipment, medium and product for optimizing the manufacturing of composite materials. Background Technology

[0002] With the development of composite material molding and manufacturing technology, the requirements for product molding quality and production efficiency are becoming increasingly higher.

[0003] In the production of composite materials, the coordination between layup structure design and manufacturing process is crucial. However, in existing technologies, the layup structure design and the curing characteristics of the material are disconnected. Different layup configurations cannot be accurately matched with suitable materials, which can easily lead to curing imbalance, excessive molding deformation, and internal stress concentration, resulting in molding quality problems. At the same time, the conversion between design information and manufacturing information relies on manual processing, which is cumbersome, inefficient, and prone to human error, affecting production consistency.

[0004] Therefore, an optimized solution for composite material manufacturing is needed. Summary of the Invention

[0005] This application provides a method, apparatus, equipment, medium, and product for optimizing the manufacturing of composite materials. By linking and matching the layup topology with the curing characteristics of the material, the automatic transfer of design information to manufacturing information can be achieved, thereby improving the molding quality and production efficiency of composite materials.

[0006] In a first aspect, embodiments of this application provide a method for optimizing the manufacturing of composite materials, the method comprising: Based on the product performance condition parameters corresponding to the composite material product to be generated, the preset composite material layup rule model is called to match the composite material layup topology that meets the product performance condition parameters. The composite material layup topology includes the sub-layup topology corresponding to each production area in the composite material product to be generated. Extract the geometric feature parameters corresponding to the sub-layout topology; Establish a first mapping relationship between geometric feature parameters and composite material curing properties of each candidate production material, and determine the curing physical characteristics of the sub-layout corresponding to the sub-layout topology based on the first mapping relationship; Using the solidification physical characteristics of the sub-layout as a threshold constraint, the production material list corresponding to the composite material product to be generated is called from the preset manufacturing process parameter library. Based on the composite material layup topology and production bill of materials, a layup process flow and layup process parameters are generated. Based on the production bill of materials, layup process flow and layup process parameters, a production task corresponding to the composite material product is generated. To execute production tasks, prepare target composite material products, and simultaneously collect quality monitoring data of target composite material products and equipment operation data for manufacturing target composite material products; Based on preset process parameter optimization methods, quality monitoring data, and equipment operation data, production tasks are optimized to obtain optimized production process information.

[0007] In one feasible implementation, product performance condition parameters include global load distribution, insulation protection level, and curing deformation sensitivity. Based on the product performance condition parameters corresponding to the composite material product to be generated, a preset composite material layup rule model is invoked to match the composite material layup topology that satisfies the product performance condition parameters, including: By using the performance constraint transformation model within the composite material layup rule model, the global load distribution, insulation protection level, and curing deformation sensitivity are transformed into regional performance constraint parameters corresponding to each region to be divided in the composite material product to be generated. Using the region division model within the composite material layup rule model, and based on the region performance constraint parameters corresponding to each region to be divided, the various regions to be produced in the composite material product to be generated are divided. The model is constructed by using the layup structure within the composite material layup rule model, and the fiber layup angle, number of layup layers, and interlayer arrangement order are retrieved to suit each production area. Based on the fiber layup angle, number of layup layers, and interlayer arrangement order, the sub-layup topology corresponding to each area to be produced is generated. Based on the stress transfer rules, ply overlap rules, and curing deformation co-adaptation rules between adjacent sub-ply topologies stored in the composite material ply rule model, the sub-ply topologies are integrated into a composite material ply topology.

[0008] In one feasible implementation, establishing a first mapping relationship between geometric feature parameters and the composite material curing properties of each candidate production material includes: The curing properties of candidate production materials are graded according to their curing performance to obtain the corresponding curing performance level of the candidate production materials. Based on the structural heat dissipation efficiency and material filling ratio of the geometric feature parameters, the corresponding geometric structure adaptation level of the geometric feature parameters is divided, and a second mapping relationship is established between the curing performance level and the geometric structure adaptation level according to the preset product molding standard. Transform the second mapping relationship into the first mapping relationship.

[0009] In one feasible implementation, the layup process includes layup process sub-processes corresponding to each production area in the composite material product to be generated; the physical characteristics of sublayment curing include curing exothermic rate and molding linear shrinkage rate. Based on the composite material layup topology and production bill of materials, the layup process flow and layup process parameters are generated, including: Based on the product design information and production bill of materials corresponding to the composite material product to be generated, determine each layup process sub-process and the corresponding candidate layup process parameters. Based on the curing heat release rate and the molding linear shrinkage rate, the process parameters of the candidate layup sub-process are adjusted to generate the layup sub-process parameters corresponding to each layup process sub-process. The process parameters of each laying sub-process are used as the laying process parameters.

[0010] In one feasible implementation, each production area includes a load-bearing area where the load requirement exceeds a preset load threshold and an insulation protection area. Based on the curing exothermic rate and molding linear shrinkage rate, the process parameters of the candidate layup sub-processes are adjusted to generate the corresponding layup sub-process parameters for each layup process sub-process, including: From the stress-bearing area and the insulation protection area, determine the process parameter adjustment area where the curing heat release rate is within the preset curing heat release rate limit range and the molding linear shrinkage rate is within the preset molding linear shrinkage rate limit range. Based on the curing heat release rate and the molding linear shrinkage rate, determine the single-layer laying tension correction amount and the interlayer compaction operation pressure correction amount; Adjust the single-layer paving tension parameter and the interlayer compaction operation pressure in the candidate paving sub-process parameters based on the single-layer paving tension correction amount and the interlayer compaction operation pressure correction amount.

[0011] In one feasible implementation, before generating the layup process flow and layup process parameters based on the composite material layup topology and the production bill of materials, the method further includes: Based on the composite material layup topology and production bill of materials, construct a product simulation model corresponding to the composite material product to be generated; Mechanical load simulation and curing deformation simulation were performed on the product simulation model to obtain simulation results; If the simulation results do not meet the preset mechanical load-bearing requirements and curing deformation requirements, the product design information corresponding to the composite material product to be generated is corrected to obtain the target product design information. Replace the target product design information with the product design information, and return the execution based on the product performance condition parameters corresponding to the composite material product to be generated. Call the preset composite material layup rule model and match the composite material layup topology that meets the product performance condition parameters.

[0012] Secondly, embodiments of this application provide a manufacturing optimization apparatus for composite materials, the apparatus comprising: The first calling module is used to call the preset composite material layup rule model based on the product performance condition parameters corresponding to the composite material product to be generated, and match the composite material layup topology that meets the product performance condition parameters. The composite material layup topology includes the sub-layup topology corresponding to each area to be produced. The extraction module is used to extract the geometric feature parameters corresponding to the sub-layout topology. The determination module is used to establish a first mapping relationship between geometric feature parameters and composite material curing properties of each candidate production material, and to determine the curing physical characteristics of the sub-layout corresponding to the sub-layout topology based on the first mapping relationship. The second calling module is used to use the physical characteristics of the sub-layup curing as a threshold constraint to call the production material list corresponding to the composite material product to be generated from the preset manufacturing process parameter library. The process generation module is used to generate the layup process flow and layup process parameters based on the composite material layup topology and production bill of materials, and to generate the corresponding production task for the composite material product based on the production bill of materials, layup process flow and layup process parameters. The preparation module is used to perform production tasks, prepare target composite material products, and simultaneously collect quality monitoring data of target composite material products and equipment operation data for manufacturing target composite material products; The optimization module is used to optimize production tasks based on preset process parameter optimization methods, quality monitoring data, and equipment operation data, thereby obtaining optimized production process information.

[0013] Thirdly, embodiments of this application provide a manufacturing optimization apparatus for composite materials, the apparatus comprising: a processor, and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the method described in the first aspect.

[0014] Fourthly, embodiments of this application provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the method described in the first aspect.

[0015] Fifthly, embodiments of this application provide a computer program product, which, when executed by a processor, implements the method described in the first aspect.

[0016] The composite material manufacturing optimization method, apparatus, equipment, medium, and product of this application embodiment, by calling a preset composite material layup rule model based on the product performance condition parameters corresponding to the composite material product to be generated, matches the composite material layup topology that meets the product performance condition parameters, and can achieve accurate conversion of the product's overall performance requirements to the layup structure; extracts the geometric feature parameters corresponding to the sub-layup topology; establishes a first mapping relationship between the geometric feature parameters and the composite material curing properties of each candidate production material; and determines the sub-layup curing physical characteristics corresponding to the sub-layup topology based on the first mapping relationship. In this way, accurate matching of different configuration layups and suitable materials can be achieved, fundamentally avoiding problems such as curing imbalance and poor deformation. Using the sub-layup curing physical characteristics as a threshold constraint, the production bill of materials corresponding to the composite material product to be generated can be called from a preset manufacturing process parameter library, which can ensure the compatibility of materials and structural forming requirements; based on the production bill of materials, layup process flow, and layup process parameters, the production task corresponding to the composite material product is generated, realizing the automatic conversion of design information to manufacturing information, replacing manual conversion, and improving the efficiency and consistency of process generation. Based on preset process parameter optimization methods, quality monitoring data, and equipment operation data for manufacturing target composite material products, production tasks are optimized to obtain optimized production process information. This can form a closed-loop control of design-manufacturing-feedback-optimization, continuously improving product molding quality and production stability. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic flowchart of a method for optimizing the manufacturing of composite materials according to an embodiment of this application is shown; Figure 2 A schematic diagram of a composite material manufacturing optimization apparatus provided in an embodiment of this application is shown. Figure 3 A schematic diagram of the hardware structure of the composite material manufacturing optimization equipment provided in an embodiment of this application is shown. Detailed Implementation

[0019] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0020] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0021] Existing technologies lack an automated derivation link from design information such as ply topology to manufacturing bill of materials and process parameters. Moreover, design and manufacturing data are not directly linked and transferred, resulting in multiple manual intervention steps and a high error rate.

[0022] Traditional composite material molding technology selects materials solely based on standard curing parameters, failing to consider layup geometry (such as thickness, number of layers, and heat dissipation efficiency) as key variables. Instead, it treats different layup configurations as homogeneous blocks, employing a uniform material and curing process. This approach, which ignores the influence of structure on curing characteristics, leads to a mismatch between the slow heat dissipation of thick layers and the intense heat release of the material, and the fast heat dissipation of thin layers and the slow curing of the material. This directly causes molding defects such as curing imbalance, deformation exceeding tolerances, and internal stress concentration.

[0023] In addition, existing composite material molding technologies also have the following problems: The problem of data fragmentation is significant: In the production of composite materials, computer-aided design (CAD) drawings, process parameters such as injection pressure and molding temperature for high-pressure resin transfer molding (HP-RTM), and equipment operating data such as tension of automated layup robots are all stored independently. This data version inconsistency easily leads to deviations in process execution. In actual testing, the scrap rate for vehicle models was as high as 8.3% due to data asynchrony.

[0024] The efficiency of design and manufacturing collaboration is low: the traditional production model relies on manual conversion of the design bill of materials into the manufacturing bill of materials, which takes more than 48 hours for a single conversion. It is difficult to quickly adapt to design adjustments and can easily cause production processes to stop.

[0025] Insufficient product lifecycle traceability capabilities: Existing technologies cannot connect the entire process of prepreg raw material procurement, molding and processing production, and subsequent operation and maintenance, thus failing to achieve complete information traceability and failing to meet the stringent quality traceability and control standards for battery pack accessories.

[0026] There is a lag in the optimization of process parameters: during the production stage, it is impossible to collect and analyze various molding data such as molding temperature, curing time, and laying tension in real time, and it is also impossible to rely on historical production data to iteratively improve the process, resulting in production efficiency remaining in the low range of 65% to 70% for a long time.

[0027] To address the problems in the prior art, embodiments of this application provide a method, apparatus, equipment, medium, and product for optimizing the manufacturing of composite materials.

[0028] The manufacturing optimization method for composite materials provided in the embodiments of this application will be introduced first below.

[0029] Figure 1 A schematic flowchart of a method for optimizing the manufacturing of composite materials according to an embodiment of this application is shown. Figure 1 As shown, the method may include the following steps: S110 to S170.

[0030] S110. Based on the product performance condition parameters corresponding to the composite material product to be generated, call the preset composite material layup rule model, and match the composite material layup topology that meets the product performance condition parameters. The composite material layup topology includes the sub-layup topology corresponding to each area to be produced.

[0031] In this embodiment, the composite material product to be generated refers to a finished composite material product that is in the design and planning stage and has not yet been formally manufactured. The composite material product to be generated can be a battery pack cover, which refers to the shell cover covering the top of the power battery module and is a core protective component of the battery pack. Product performance parameters refer to the core performance indicators and constraints that the composite material product to be generated must meet, extracted from user needs and application scenarios during the composite material product design stage. These include, but are not limited to, global load distribution, load-bearing strength, stiffness requirements, fatigue resistance, insulation protection level, temperature resistance level, electromagnetic shielding performance, curing deformation sensitivity, and dimensional accuracy requirements.

[0032] A pre-defined composite material layup rule model refers to an integrated logical model that pre-loads various composite material layup design rules. After inputting product performance parameters, this model can automatically output a composite material layup topology that matches and satisfies the product performance conditions. Composite material layup rules refer to pre-defined design criteria, constraints, and assembly coordination specifications used to regulate the design and arrangement of composite material layups. These can include composite material molding design criteria, layup structure constraint logic, and composite material arrangement specifications.

[0033] In this embodiment, the composite material layup rule model can be any of the following structures: random forest, support vector machine, convolutional neural network, and multilayer perceptron. The training input data for the composite material layup rule model can be the product performance sample parameters (e.g., mechanical load-bearing capacity, deformation requirements, stiffness, and strength) corresponding to the prepared composite material products. The labels corresponding to the training input data can be the layup topology corresponding to the prepared composite material products, and the composite material products under this layup topology conform to the product performance sample parameters.

[0034] In some embodiments, when the structure of the composite material layup rule model is a convolutional neural network or a multilayer perceptron, the composite material layup rule model can be trained as follows: Input training input data into the initial layup rule model, calculate the residual between the output of the initial layup rule model and the corresponding label, update the weights of the initial layup rule model using backpropagation, and iterate until a stopping condition is met to complete model training and obtain the composite material layup rule model. It is easy to understand that the initial layup rule model is an initial, untrained convolutional neural network or multilayer perceptron.

[0035] In some embodiments, the performance requirements of composite materials, such as product mechanical load-bearing capacity, insulation protection, and molding deformation, can be combined with industry design standards and process experience to define the parameter range of fiber laying angle, number of layup layers, and interlayer arrangement. At the same time, the connection specifications for stress transmission, interlayer overlap, and deformation coordination can be formulated, and the requirements for regional division and structural integration can be matched. In this way, the entire set of composite material layup rules can be determined in advance. Then, the various composite material layup design rules can be integrated into a logical model to obtain the composite material layup rule model.

[0036] The composite material layup topology here refers to the composite material layup layout scheme that meets the product performance parameters and is selected from a variety of preset candidate composite material layup topologies according to preset composite material layup rules.

[0037] In this embodiment, the "area to be produced" refers to several partitions or regions divided into the overall composite material product to be produced based on performance requirements, stress characteristics, and functional applications. The sub-layout topology corresponding to the area to be produced refers to the layup arrangement of the composite material corresponding to each area to be produced.

[0038] S120. Extract the geometric feature parameters corresponding to the sub-layout topology.

[0039] In this embodiment, various geometrically related data, i.e., geometric feature parameters, are extracted and obtained from the neutron ply topology to characterize the ply shape and structural morphology. These geometric feature parameters can include, but are not limited to, ply thickness, number of ply layers, overall ply dimensions, stack arrangement, structural outline, regional cross-sectional area, and heat dissipation structure dimensions. By extracting the geometric feature parameters corresponding to the subply topology, the concrete ply structure layout can be transformed into calculable and matchable quantifiable geometric data.

[0040] S130. Establish a first mapping relationship between geometric feature parameters and composite material curing properties of each candidate production material, and determine the sub-layup curing physical characteristics corresponding to the sub-layup topology based on the first mapping relationship.

[0041] In this embodiment, the composite material curing properties of each candidate production material refer to the inherent curing and molding performance parameters of the candidate production material or raw material itself, which are the molding characteristics inherent in the material. Curing and molding refers to the process of processing the composite material raw material to solidify and define it. The composite material curing properties may include, but are not limited to: curing reaction temperature, curing heat release, curing heat release rate, curing time, curing shrinkage rate, and gel time.

[0042] It should be noted that the shape of the layup affects the rate of heat dissipation. For example, if the composite material has a thick layup and many layers, the heat inside cannot dissipate and tends to accumulate. Therefore, the geometry or shape of the layup affects the heat dissipation conditions.

[0043] Furthermore, each composite material possesses specific curing properties. For example, some composite materials release heat rapidly during curing or hardening, resulting in fast drying, while others release heat gently or steadily during curing or hardening, leading to slow drying. Different composite materials inherently possess different curing properties, meaning they exhibit varying curing heat release characteristics. The sublayment topology directly impacts the compatibility requirements for composite material curing and the actual molding effect. For instance, thicker areas have poor heat dissipation, making them unsuitable for materials with excessively rapid heat release, as they are prone to overheating and deformation; thinner areas dissipate heat quickly, making them suitable for materials that allow for rapid molding. In other words, different part structures require different suitable materials, and incorrect matching will lead to problems in the finished product. Therefore, a two-way mapping relationship, namely the first mapping relationship, can be established between the geometric characteristic parameters corresponding to the sublayment topology and the composite material curing properties of each candidate production material.

[0044] Specifically, a bidirectional correlation, or first mapping relationship, can be established between each geometric feature parameter and each composite material curing property based on a preset structural material curing property matching rule. The preset structural material curing property matching rule refers to the pre-defined criteria for how geometric feature parameters and composite material curing properties should be paired or matched.

[0045] In some embodiments, the curing property matching rule for structural materials can be: matching the curing properties of composite materials with large layup thickness, many layers, slow internal heat dissipation, and easy heat accumulation with the curing properties of composite materials with low heat release during curing and slow curing speed.

[0046] In this embodiment, the physical characteristics of subply curing corresponding to the subply topology refer to the various physical states and molding conditions that must be met when processing, curing, and molding the area to be produced corresponding to the subply. The physical characteristics of subply curing may include curing temperature, heat release during curing, and the degree of shrinkage during the curing process.

[0047] In S130, the solidification physical characteristics of the sub-layout corresponding to the sub-layout topology can be determined based on the first mapping relationship.

[0048] S140. Using the physical characteristics of the cured sub-layout as a threshold constraint, retrieve the production material list corresponding to the composite material product to be generated from the preset manufacturing process parameter library.

[0049] In this embodiment, the physical characteristics of the sub-layup curing are used as a threshold constraint, that is, the limiting standard or condition that cannot be broken during curing and molding. Based on the threshold constraint, the material configuration list required for the production of the composite material product to be generated is retrieved and matched in the preset process database.

[0050] S150. Based on the composite material layup topology and the production bill of materials, generate the layup process flow and layup process parameters, and based on the production bill of materials, layup process flow and layup process parameters, generate the production task corresponding to the composite material product.

[0051] In this embodiment, based on the composite material layup topology and production bill of materials, the sequence and method of material laying are planned, i.e., the laying process flow. At the same time, the specific operation values ​​or parameters of the laying process flow are determined, i.e., the laying process parameters. The laying process parameters may include process indicators such as laying tension, compaction pressure, laying speed, and interlayer bonding requirements.

[0052] Specifically, the laying sequence, method, stacking method, and laying area of ​​the materials can be planned according to the composite material layup topology, forming a complete laying process flow, i.e., the laying process flow. Practical values ​​such as laying tension, pressing pressure, laying speed, and interlayer bonding gaps can be determined by combining the inherent properties of the materials in the production bill of materials, i.e., the laying process parameters. The production bill of materials, laying process flow, and laying process parameters are then summarized and compiled into a production task that can be directly implemented. Specifically, a production task instruction can be generated and sent to the composite material product preparation equipment.

[0053] S160. Perform production tasks, prepare target composite material products, and simultaneously collect quality monitoring data of target composite material products and equipment operation data for manufacturing target composite material products.

[0054] In this embodiment, a predetermined production task is executed, and the target composite material product is processed and manufactured according to the production material list, laying process flow and laying process parameters. During the production process, quality monitoring data related to quality such as thickness, adhesion, curing state and appearance defects are collected or detected simultaneously, and equipment operation data for manufacturing the target composite material product are also collected.

[0055] S170. Based on preset process parameter optimization methods, quality monitoring data, and equipment operation data, the production task is optimized to obtain optimized production process information.

[0056] In this embodiment, the preset process parameter optimization method is a pre-executed adjustment criterion and correction method for process parameters. According to the process parameter optimization method, the collected quality monitoring data and equipment operation data can be compared to identify deviations in the laying process flow and laying process parameters. Based on the deviations, the laying process flow and laying process parameters can be adjusted in a targeted manner to correct the original production task and obtain optimized production process information.

[0057] The preset process parameter optimization method refers to the preset parameter adjustment rules and optimization strategies, including adjustment logic such as interval limit correction, batch adaptation parameter adjustment, defect reverse adjustment, and multi-index balancing ratio. It is used to correct production process parameters or layup process parameters such as layup tension, molding temperature and pressure, and curing time. The process parameter optimization method here can be a neural network model built using machine learning algorithms. This neural network model can be trained based on historical process, quality inspection, and equipment operation data. It can autonomously discover the correlation between the layup process flow, layup process parameters, equipment operation data, and product quality. The neural network model outputs optimized production process information.

[0058] This neural network model can employ a multilayer perceptron or convolutional neural network structure. It can use historical deployment process flow, historical deployment process parameters, and historical equipment operation data as its second training input data. The corresponding second label can be optimal production process information or a standard combination of process parameters. The neural network model can be trained as follows: input the second training input data into an untrained initial neural network model; calculate the residual between the output of the initial neural network model and the corresponding second label; update the weights of the initial neural network model through backpropagation; iterate until stopping conditions such as accuracy and number of iterations are met, completing model training. After training, the model can uncover correlations between data and output optimized production process information.

[0059] Through S110 to S170, materials can be precisely matched according to structure and curing characteristics, and material selection can meet product molding requirements. It can standardize the laying process and process parameters, ensure the neatness and uniformity of the layer structure, and promptly detect process defects through quality monitoring throughout the production process. It can also optimize process solutions, reduce product deformation, cracking and other defects, and efficiently complete the seamless transformation from design to manufacturing, eliminating manual conversion errors. It can precisely match materials and process parameters to meet structural curing requirements, standardize the generation of production tasks, reduce practical deviations, and dynamically iterate and optimize the process to ensure product molding quality and batch consistency.

[0060] In some embodiments, product performance parameters include global load distribution, insulation protection level, and curing deformation sensitivity. Global load distribution refers to the distribution of external forces, pressures, and impacts across all areas of the composite material product to be formed. Insulation protection level refers to the safety protection index that measures the product's ability to isolate current and prevent electric shock. Curing deformation sensitivity refers to the ease with which the material shrinks, warps, or deforms during heat curing.

[0061] In some embodiments, based on the product performance condition parameters corresponding to the composite material product to be generated, a preset composite material layup rule model is invoked to match the composite material layup topology that satisfies the product performance condition parameters, i.e., S110, which may include the following steps: S111. Through the performance constraint transformation model within the composite material layup rule model, the global load distribution, insulation protection level, and curing deformation sensitivity are transformed into regional performance constraint parameters corresponding to each region to be divided in the composite material product to be generated.

[0062] In this embodiment, the performance constraint conversion model refers to the numerical conversion and decomposition module within the composite material layup rule model. This module decomposes, converts, and quantifies the global load distribution, insulation protection level, and curing deformation sensitivity into design constraint parameters for each local region of the composite material product to be generated. The regions to be divided refer to the local regions of the composite material product to be generated, separated based on the global load distribution, insulation protection level, and curing deformation sensitivity. Regional performance constraint parameters refer to the performance limits or design constraint parameters specific to each region. The structure of the performance constraint conversion model can be a multilayer perceptron (MLP), a fully connected neural network, or a convolutional neural network. The performance constraint conversion model can be trained as follows: global load distribution samples, insulation protection level samples, and curing deformation sensitivity samples are input into the initial performance constraint conversion model. The residual between the output of the initial performance constraint conversion model and the standard performance constraint parameters for each region obtained through manual calibration or simulation is calculated. Backpropagation updates the weights of the initial performance constraint conversion model, iterating until the stopping condition is met, thus completing the training.

[0063] In S111, the global load distribution, insulation protection level, and curing deformation sensitivity can be decomposed and converted using the performance constraint conversion model to obtain the exclusive performance constraint index corresponding to each region to be divided in the composite material product to be generated. In this way, the overall design requirements can be refined and adapted to each region.

[0064] S112. Using the region division model within the composite material layup rule model, and based on the region performance constraint parameters corresponding to each region to be divided, divide the composite material product to be generated into various regions to be produced.

[0065] In this embodiment, the region partitioning model refers to the partitioning judgment module in the composite material layup rule model. It is used to define and split the physical regions of the composite material product to be generated based on the performance constraint parameters of each region, thus defining the unit range for the layup design, and obtaining the various regions to be produced in the composite material product. A region to be produced refers to a formal, independent block in the composite material product to be generated after the region partitioning model has defined its boundaries (regional performance constraint parameters) and determined its range. The region to be partitioned is a preliminary, virtual partition outline, while the region to be produced is a physical partition that has undergone quantitative judgment and boundary verification. For example, given the overall stress, insulation, and deformation requirements of an entire composite material product (such as a battery pack cover), it is first roughly divided into temporary regions to obtain the regions to be partitioned; the dimensions, constraints, and connection logic of these temporary regions are verified, and the final fixed boundaries are defined to form formal partitions, thus obtaining the regions to be produced.

[0066] In some embodiments, the structure of the region partitioning model may include a multilayer perceptron, a fully connected neural network, or a convolutional neural network. The region partitioning model can be trained as follows: Input the region performance constraint parameter samples of each region to be partitioned into the initial region partitioning model; calculate the residual between the output of the initial region partitioning model and the standard label (where the standard label refers to the standard region partitioning result obtained through manual calibration or simulation); update the weights of the initial region partitioning model through backpropagation; and iterate until stopping conditions such as accuracy and number of iterations are met, thus completing model training.

[0067] In S112, based on the region division model, the composite material product to be generated is divided into regions using the performance constraint parameters of each region as the basis for judgment. Regions with clear boundaries and definite constraint parameter attributes are divided into production regions. Furthermore, the performance constraint parameters of each region can be transformed into performance constraint conditions for each production region.

[0068] S113. Construct a model using the layup structure within the composite material layup rule model, and retrieve the fiber laying angle, number of layup layers, and interlayer arrangement order suitable for each production area.

[0069] In this embodiment, the layup structure construction model is a built-in logical module of the composite material layup rule model. It has a built-in molding matching database and design criteria, which can be used to automatically retrieve and match the corresponding fiber layup angle, number of layup layers, and interlayer stacking order according to the predetermined load, insulation, and deformation constraints of each production area, i.e., fiber layup angle, number of layup layers, and interlayer arrangement order, and output basic layup parameters that are adapted to the performance requirements of the production area.

[0070] In some embodiments, the structure of the layup construction model may include a multilayer perceptron, a fully connected neural network, or a convolutional neural network. The layup construction model can be trained as follows: performance constraint parameter samples for each region are input into the initial layup construction model; the residuals between the input of the initial layup construction model and the standard fiber laying angle, standard layup number, and standard interlayer arrangement order for the corresponding region are calculated; the weights of the initial layup construction model are updated through backpropagation; and the training is iterated until the stopping condition is met.

[0071] In S113, a model can be built based on the ply structure. For each shaped production area, the three core ply parameters that match the performance constraints can be selected and retrieved: fiber layup angle, total number of ply layers, and the order of stacking of each layer, providing a design basis for building a local ply structure.

[0072] S114. Generate the sub-layout topology corresponding to each production area based on the fiber laying angle, number of layup layers, and interlayer arrangement order.

[0073] In this embodiment, a local ply structure can be built for each area to be produced based on the fiber layup angle, number of ply layers, and interlayer stacking order, generating a corresponding sub-ply topology.

[0074] S115. Based on the stress transfer rules, ply overlap rules, and curing deformation co-adaptation rules between adjacent sub-ply topologies stored in the composite material ply rule model, integrate each sub-ply topology into a composite material ply topology.

[0075] In this embodiment, the stress transfer rules between adjacent sub-lay topologies refer to the design criteria used to regulate the force transmission path, magnitude, and distribution at the junction of adjacent sub-lay topologies. The layup overlap rules are the bonding standards that constrain the splicing positions of adjacent sub-lay topologies. They can be used to limit overlap width, layer staggering methods, and edge connection forms to ensure stable connections at the splicing points and prevent delamination and separation defects. The curing deformation coordination and adaptation rules refer to the overall shrinkage and warping changes during the curing of layups in each production area. They can be used to coordinate the deformation amplitude and trend, prevent excessive deformation differences in the production areas, and reduce the probability of overall component distortion and dimensional deviations.

[0076] Through S111 to S115, the overall performance requirements can be broken down into zonal quantification indicators, enabling differentiated layup designs and precisely adapting to the load-bearing, insulation, and deformation resistance requirements of various areas. This allows for the construction of local layup structures in zones, followed by overall fusion according to connection rules, avoiding stress concentration and connection failure at joints. Unified control of curing deformation in each area effectively reduces finished product deformation and dimensional deviations, improving overall structural strength and molding pass rate.

[0077] In some embodiments, the performance constraint conversion model can be constructed in the following way: summarizing the overall performance requirements of the product, such as load-bearing capacity, insulation, and deformation resistance; setting the index decomposition and conversion standard; decomposing and quantifying the whole machine constraint parameters into basic performance parameters of each region according to the predetermined threshold; and building the basic performance parameters of each region into a performance constraint conversion model.

[0078] In some embodiments, the region partitioning model can be constructed by combining the component geometry, stress points and heat dissipation structure, matching performance parameter difference judgment criteria, defining partition boundary judgment conditions and merging and splitting rules, thereby constructing the region partitioning model.

[0079] In some embodiments, the layup structure construction model can be constructed by: inputting an adaptation parameter library of fiber layup angle, number of layup layers, and interlayer arrangement order, adding stress transfer, overlap connection, and deformation coordination constraint rules, and integrating them to form a layup structure construction model.

[0080] In some embodiments, establishing a first mapping relationship between geometric feature parameters and the composite material curing properties of each candidate production material, i.e., S130, may include the following steps: S31. Classify the curing performance of the candidate production materials based on their molding and curing properties to obtain the corresponding curing performance level of the candidate production materials.

[0081] In this embodiment, the molding and curing properties of the candidate production material refer to the various physical and technological characteristics exhibited by the composite material itself during the heating and curing molding stage. In S31, the molding and curing properties of the candidate production material can be evaluated and graded according to a preset grading threshold range to obtain the corresponding curing performance level of the candidate production material. The curing performance level is a grade of superiority or inferiority based on the various molding and curing properties of the composite material according to preset standards, used to quantitatively distinguish the curing and molding capabilities of different candidate production materials.

[0082] S32. Based on the structural heat dissipation efficiency and material filling ratio of the geometric feature parameters, classify the corresponding geometric structure adaptation level of the geometric feature parameters, and establish a second mapping relationship between the curing performance level and the geometric structure adaptation level according to the preset product molding standard.

[0083] In this embodiment, the structural heat dissipation efficiency of the geometric feature parameters refers to the heat dissipation efficiency determined or influenced by the geometric feature parameters, and the material filling ratio of the geometric feature parameters refers to the ratio of the actual filling volume of the composite material in the production area determined or influenced by the geometric feature parameters to the overall spatial volume of the area. In S32, based on the structural heat dissipation efficiency and the material filling ratio, and according to a preset geometric structure adaptation evaluation standard, different structural forms can be divided into corresponding adaptation levels, i.e., geometric structure adaptation levels. The geometric structure adaptation level is used to characterize the ability of the sub-layout topology structure itself to adapt to curing and molding.

[0084] The preset geometric fit criteria here refer to the pre-defined quality control requirements for finished products, which may include judgment standards such as dimensional accuracy, deformation limits, density, appearance defects, structural strength, and molding stability. The second mapping relationship refers to the matching correspondence between the curing performance level and the geometric fit level, used to determine the geometric fit level that is compatible with the curing performance level, or vice versa.

[0085] Specifically, we can first sort out the established geometric structure adaptation evaluation criteria such as dimensional accuracy, deformation limit, molding density, and structural stability, and define the qualified index range. Then, combined with the molding characteristics of materials with different curing performance levels and the heat dissipation and filling conditions corresponding to different geometric structure adaptation levels, we can screen out the grade combinations that can meet the molding requirements, formulate corresponding matching constraint rules, and establish a second mapping relationship between curing performance level and geometric structure adaptation level based on the matching constraint rules.

[0086] S33. Transform the second mapping relationship into the first mapping relationship.

[0087] In this embodiment, the second mapping relationship between the curing performance level and the geometric structure adaptation level is converted into a first mapping relationship in which the geometric characteristic parameters directly match the curing properties of the composite material, by combining the molding and curing properties of the candidate production material corresponding to the product curing performance level and the geometric structure adaptation level.

[0088] Through S31 to S33, material curing and grading, structural adaptation and classification can be completed step by step and a grade mapping can be established. Ultimately, it is transformed into a direct matching relationship between geometric parameters and material properties, accurately matching and adapting materials, avoiding curing heat accumulation and deformation defects, and improving the molding quality and structural stability of finished products.

[0089] In some embodiments, the layup process includes layup sub-processes corresponding to each production area in the composite material product to be generated; the physical characteristics of the sub-layup curing include curing heat release rate and molding linear shrinkage rate.

[0090] In some embodiments, generating a layup process flow and layup process parameters, i.e., S150, based on the composite material layup topology and the production bill of materials, may include the following steps: S151. Based on the product design information and production material list corresponding to the composite material product to be generated, determine each layup process sub-process and the corresponding candidate layup process parameters.

[0091] In this embodiment, based on product design information such as product design configuration and layup structure, and combined with the material specifications and material ratio information of the production bill of materials, independent layup sub-processes corresponding to different production areas are divided, namely layup process sub-processes. At the same time, the basic process parameters corresponding to each layup process sub-process are initially proposed, namely candidate layup sub-process process parameters.

[0092] S152. Based on the curing heat release rate and the molding linear shrinkage rate, adjust the process parameters of the candidate layup sub-process to generate the layup sub-process parameters corresponding to each layup process sub-process.

[0093] Because the curing reaction continuously releases heat, a higher curing heat release rate makes it easier for localized heat to accumulate, potentially leading to localized overheating and uneven curing, resulting in deformations such as warping and bulging. The molding linear shrinkage rate represents the dimensional shrinkage of the material after curing and shaping. Shrinkage causes the overall or localized dimensions of the component to decrease, and differences in shrinkage at different locations can cause dimensional deformation deviations such as misalignment, gaps, and shape offsets. Therefore, by combining the curing heat release rate and the molding linear shrinkage rate, it is possible to predict the deformation deviations between the actual molded component and the ideal standard structure caused by heat accumulation and dimensional shrinkage during the curing process.

[0094] In this embodiment, based on deformation deviation, the preliminary selected candidate laying sub-process parameters such as laying tension, compaction pressure, laying speed, and interval time are finely adjusted to obtain the laying sub-process parameters, thereby avoiding overheating defects and dimensional deviations, and finally determining the formal process parameters suitable for each sub-process.

[0095] S153. Use the process parameters of each laying sub-process as the laying process parameters.

[0096] In this embodiment, the process parameters of each laying sub-process can be integrated into laying process parameters.

[0097] Through steps S151 to S153, the process can be divided into zones and parameters can be initially determined based on design and material information. These parameters can then be adjusted by considering the heat release and shrinkage characteristics during curing, ultimately finalizing the entire set of process parameters. This effectively avoids problems such as uneven curing and dimensional deformation, ensuring that the finished product's dimensions conform to design standards and improving molding quality and structural consistency.

[0098] In some embodiments, each production area includes a load-bearing area where the load requirement exceeds a preset load threshold and an insulation protection area. The load-bearing area where the load requirement exceeds the preset load threshold refers to the structural part where the load exceeds a set critical value and the ply strength and stiffness are required to meet the standards. This load-bearing area is used to withstand external forces, bear weight, and bend and pull. The insulation protection area refers to the part that isolates current and prevents leakage.

[0099] In some embodiments, adjusting the process parameters of the candidate layup sub-process based on the curing exothermic rate and the molding linear shrinkage rate to generate the layup sub-process parameters corresponding to each layup process sub-process, i.e., S152, may include the following steps: S521. From the stress-bearing area and the insulation protection area, determine the process parameter adjustment area where the curing heat release rate is within the preset curing heat release rate limit range and the molding linear shrinkage rate is within the preset molding linear shrinkage rate limit range.

[0100] In this embodiment, the curing exothermic rate limit range refers to the allowable critical range of the material's curing exothermic rate. Exceeding this allowable critical range can easily lead to heat accumulation, uneven curing, and thermal deformation defects. The molding linear shrinkage rate limit range refers to the compliant critical range of dimensional shrinkage after the material has cured and set. Exceeding this compliant critical range can result in dimensional deviations and component warping or misalignment.

[0101] In S521, regions whose curing heat release rate and molding linear shrinkage rate are both within the corresponding critical limit range can be selected from the load-bearing region and the insulation protection region as regions to be adjusted. These regions are those where the heat release rate and shrinkage rate are close to the compliance critical range, making them highly susceptible to thermal damage and dimensional deformation defects, requiring adjustments to process parameters such as laying tension and compaction pressure.

[0102] S522. Determine the single-layer laying tension correction amount and the interlayer compaction operation pressure correction amount based on the curing heat release rate and the molding linear shrinkage rate.

[0103] In this embodiment, by combining the two physical properties of the material in the corresponding area, namely the curing heat release rate and the molding shrinkage range, and referring to the process specification standards, the adjustment values ​​of the single-layer laying tension and the interlayer bonding compaction pressure that can offset the influence of heat accumulation and compensate for the dimensional shrinkage deviation are calculated, namely the layer laying tension correction amount and the interlayer compaction operation pressure correction amount.

[0104] S523. Adjust the single-layer paving tension parameter and the interlayer compaction operation pressure in the candidate paving sub-process parameters according to the single-layer paving tension correction amount and the interlayer compaction operation pressure correction amount.

[0105] In this embodiment, the single-layer laying tension parameter and the interlayer compaction operation pressure in the candidate laying sub-process parameters can be adjusted based on the single-layer laying tension correction amount and the interlayer compaction operation pressure correction amount.

[0106] Through S521 to S523, the area where process parameters need to be adjusted can be accurately determined, the tension and pressure correction values ​​can be calculated and the parameters can be optimized, the curing heat release and molding shrinkage deformation can be effectively controlled, the structural load-bearing and insulation performance can be guaranteed, and the product molding quality and production qualification rate can be improved.

[0107] In some embodiments, before generating the layup process flow and layup process parameters based on the composite material layup topology and the production bill of materials, i.e. before S150, the method may further include the following steps: S41. Based on the composite material layup topology and production bill of materials, construct a product simulation model corresponding to the composite material product to be generated.

[0108] In this embodiment, a digital simulation model of the composite material product to be generated is constructed based on the composite material layup topology and the production bill of materials. This product simulation model is a digital three-dimensional model built upon the layup structure and material parameters. It can simulate the product's structural morphology and material distribution, and is used for subsequent mechanical and curing deformation calculations and analyses.

[0109] In some embodiments, the product simulation model may be a three-dimensional CAD model of the composite material cover.

[0110] S42. Perform mechanical load simulation and curing deformation simulation on the product simulation model to obtain simulation results.

[0111] In this embodiment, mechanical load simulation refers to simulating the product's exposure to external forces, loads, bending, and tensile stresses, calculating structural stress, stiffness, and load-bearing capacity, and verifying whether it meets the requirements for load-bearing use. Curing deformation simulation refers to simulating the heat release and shrinkage process of material curing, predicting deformation such as warping, dimensional deviations, and interlayer misalignment after molding. In S42, the product simulation model can be called to perform load simulation and curing deformation simulation, calculating and outputting various simulation analysis results such as stress data and deformation values, i.e., simulation results.

[0112] S43. If the simulation results do not meet the preset mechanical load-bearing requirements and curing deformation requirements, correct the product design information to obtain the target product design information.

[0113] In this embodiment, the mechanical load-bearing requirement refers to the product's preset standards for strength, stiffness, load-bearing limit, and other stress performance. The curing deformation requirement refers to the product's preset standards for molding accuracy, such as deformation range and upper limit of dimensional deviation. In S43, simulation data can be compared with preset standards. If either the stress performance or molding deformation fails to meet the standard, i.e., neither the mechanical load-bearing requirement nor the curing deformation requirement is satisfied, the product design information, such as the layup arrangement, material specifications, and structural dimensions, is adjusted. After optimization, a final design scheme that meets the performance and accuracy requirements is formed, i.e., the target product design information.

[0114] S44. Replace the product design information with the target product design information, and return to execute the product performance condition parameters corresponding to the composite material product to be generated, call the preset composite material layup rule model, and match the composite material layup topology that meets the product performance condition parameters.

[0115] In this embodiment, the product design information is replaced with the target product design information, and then the process returns to execute S110.

[0116] In some embodiments, product performance parameters are a type of product design information. This product design information can be the specific design information corresponding to the composite material product to be generated (e.g., the composite battery pack cover to be generated) that R&D personnel can complete on a Product Lifecycle Management (PLM) platform. This product design information may include the 3D design information corresponding to the composite material product to be generated. The product design information can also be automatically linked to a composite material database for molding process reliability simulation, i.e., executing steps S41 to S44 to generate a Bill of Materials (BOM) with process constraints, i.e., product design information. This product design information can be stored on the PLM platform. Process constraints refer to the limiting conditions that must be followed in production and processing, including process boundary requirements such as layup angle, layup sequence, molding temperature and pressure, tension range, and curing time. The composite material database may include performance parameters of carbon fiber or glass fiber. The above process can be referred to as the design phase.

[0117] In some embodiments, based on the product design information (Design Bill of Materials) in the PLM platform, a process parameter library can be called to automatically generate a manufacturing bill of materials and process routes (e.g., raw material cutting → automatic placement → molding and curing → trimming and inspection), and bind the process parameters to the production process, i.e., the process route or process flow. The placement process flow is one type of process flow, and the process parameters may include placement process parameters, such as placement tension and curing temperature. The above can be the process planning stage, specifically S120 to S150 mentioned above.

[0118] In some embodiments, a PLM platform or system can issue process parameters and production tasks to a manufacturing execution system. Molding equipment can then perform processing according to these parameters and tasks (e.g., an automated layup robot operates according to tension closed-loop control logic) to produce the composite material product to be manufactured. Molding equipment refers to the mechanical equipment that completes the shaping and processing of composite material products, including automated layup robots, molding machines, resin injection equipment, curing ovens, etc., responsible for layup, pressure application, injection, and curing operations. Sensors can collect operating data from the molding equipment and quality inspection data from the product, and transmit this data back to the PLM system, forming a digital twin of the production process—a virtual mirror storing equipment operating data and quality monitoring data during production. This digital twin can be a digital virtual model that synchronously replicates the actual on-site layup and molding process.

[0119] In some embodiments, if a design change occurs, i.e., the product design information changes, the PLM platform or system can trigger a change process. Specifically, the updated product design information can be converted into the current production task, completing the update of the manufacturing BOM and process flow, i.e., updating the production task, and the updated production task is notified to relevant departments or modules in real time to ensure production data synchronization. The above steps can be the production execution phase.

[0120] In some embodiments, during the supply chain system phase, raw material requirements (such as specifications and delivery time) can be pushed to the supplier portal through the PLM system, and suppliers can provide online feedback on production progress and quality inspection reports. The PLM system can then automatically update the supply chain status.

[0121] In some embodiments, during the after-sales phase, the full lifecycle data in the PLM system can be linked through the product identification code (e.g., product ID). After-sales personnel can quickly query product configuration and production process parameters based on the product identification code, and also combine the data for fault analysis and predictive maintenance.

[0122] In some embodiments, a Product Lifecycle Management (PLM) platform can be used to manage data throughout the entire lifecycle of composite material products, from idea to design, manufacturing, after-sales service, and disposal. This PLM platform, also known as the PLM core platform, can employ a centralized data management architecture to store product lifecycle data. The stored data may include product design information, product process data, product production data, supply chain data, and after-sales data.

[0123] Product design information here can include 3D CAD models of composite material covers, simulation data (such as impact simulation reports), and bills of materials (BOM). Product process data can include manufacturing process parameter libraries, molding process parameter libraries (such as resin injection pressure, molding temperature), and process route planning documents. Product production data can include tension control parameters of automated layup robots, molding equipment operating data, and quality inspection data. Supply chain data can include supplier qualifications, raw material batch information, and delivery cycles. After-sales data can include product serial numbers, repair records, and after-sales fault analysis reports.

[0124] In some embodiments, the method may further include: sharing design requirements and quality standards through a supplier portal to enable real-time tracking of outsourced component production progress. It may also include assigning a unique identification code to each product (e.g., a composite material battery pack cover), associating it with raw material batches, process parameters, test results, and after-sales records to enable traceability via barcode scanning.

[0125] This application provides a manufacturing optimization system for composite materials. The system includes a PLM platform, five functional modules, and a data interaction interface. The five functional modules include an integrated design and process module, a real-time data acquisition module, an intelligent process optimization module, a supply chain collaboration module, and a full lifecycle traceability module.

[0126] This integrated design and manufacturing module is compatible with multiple 3D design software programs, allowing direct import of product models and automatic conversion of bill of materials (BOM) versions. Leveraging its built-in composite material molding process constraint rule library (such as process feasibility verification based on fiber layup angles), it can verify the feasibility of design information. This integrated design and manufacturing module also supports seamless data import from mainstream CAD software (such as CATIA and SolidWorks), automatically converting the design BOM to the manufacturing BOM.

[0127] The real-time data acquisition module is used to collect high-frequency operating parameters of the molding equipment, such as temperature, pressure, and laying tension, to monitor the on-site production conditions in real time, with a sampling frequency of ≥10Hz. The real-time data acquisition module can interface with the Manufacturing Execution System (MES) via sensors to collect process parameters and other operating parameters of the molding equipment.

[0128] The intelligent process optimization module can utilize historical data stored on the PLM platform and use machine learning algorithms to dynamically adjust process parameters, such as automatically adjusting curing time based on batch differences; thereby adapting to material and batch differences and reducing molding defects.

[0129] The supply chain collaboration module is used to share design requirements and quality standards through the supplier portal, enabling real-time tracking of the production progress of outsourced parts.

[0130] The full lifecycle traceability module is used to assign a unique identification code to each product (such as the cover of a composite material battery pack), and associate it with raw material batches, process parameters, test results, and after-sales records to achieve traceability by scanning the code.

[0131] The data interaction interface is a standardized interface that allows the PLM platform to conduct bidirectional data transmission and flow with the Manufacturing Execution System (MES), Enterprise Resource Planning (ERP) system, and Warehouse Management System (WMS) system based on the data interaction interface in the composite material manufacturing optimization system. Specifically, the PLM platform or system can output process parameters and production plans to the MES through the data interaction interface, receive production execution data and equipment status feedback from the MES through the data interaction interface, and synchronize material requirements information with the ERP system through the data interaction interface.

[0132] Figure 2 A schematic diagram of a composite material manufacturing optimization apparatus provided in an embodiment of this application is shown. Figure 2 As shown, the manufacturing optimization device 200 for the composite material may include a first calling module 210, an extraction module 220, a determination module 230, a second calling module 240, a process generation module 250, a preparation module 260, and an optimization module 270.

[0133] The first calling module 210 is used to call a preset composite material layup rule model based on the product performance condition parameters corresponding to the composite material product to be generated, and match the composite material layup topology that meets the product performance condition parameters. The composite material layup topology includes the sub-layup topology corresponding to each area to be produced.

[0134] Extraction module 220 is used to extract the geometric feature parameters corresponding to the sub-layout topology.

[0135] The determination module 230 is used to establish a first mapping relationship between geometric feature parameters and composite material curing properties of each candidate production material, and to determine the curing physical characteristics of the sub-layout corresponding to the sub-layout topology based on the first mapping relationship.

[0136] The second calling module 240 is used to use the solidified physical characteristics of the sub-layout as a threshold constraint to call the production material list corresponding to the composite material product to be generated from the preset manufacturing process parameter library.

[0137] The process generation module 250 is used to generate the layup process flow and layup process parameters based on the composite material layup topology and the production bill of materials, and to generate the corresponding production task for the composite material product based on the production bill of materials, layup process flow and layup process parameters.

[0138] The preparation module 260 is used to perform production tasks, prepare the target composite material product, and simultaneously collect quality monitoring data of the target composite material product and equipment operation data for manufacturing the target composite material product.

[0139] The optimization module 270 is used to optimize production tasks based on preset process parameter optimization methods, quality monitoring data, and equipment operation data to obtain optimized production process information.

[0140] In some embodiments, product performance parameters include global load distribution, insulation protection level, and curing deformation sensitivity. The first invocation module 210 may include a first conversion module, a first partitioning module, a first retrieval module, a first generation module, and an integration module.

[0141] The first conversion module is used to convert the global load distribution, insulation protection level, and curing deformation sensitivity into regional performance constraint parameters corresponding to each region to be divided in the composite material product to be generated, through the performance constraint conversion model within the composite material layup rule model.

[0142] The first partitioning module is used to partition the composite material product to be generated into various production areas based on the region performance constraint parameters corresponding to each region, using the region partitioning model within the composite material layup rule model.

[0143] The first retrieval module is used to construct a model through the layup structure in the composite material layup rule model, and retrieve the fiber layup angle, number of layup layers and interlayer arrangement order suitable for each production area.

[0144] The first generation module is used to generate the sub-layout topology structure corresponding to each area to be produced based on the fiber laying angle, the number of layup layers, and the interlayer arrangement order.

[0145] The integration module is used to integrate the topologies of each sub-layout into a composite material layup topology based on the stress transfer rules, layup overlap rules, and curing deformation co-adaptation rules between adjacent sub-layout topologies stored in the composite material layup rule model.

[0146] In some embodiments, the determining module 230 may include a grading module, a second division module, and a second conversion module. The grading module is used to grade the curing properties of candidate production materials to obtain the curing performance level corresponding to the candidate production materials.

[0147] The second division module is used to divide the geometric feature parameters into corresponding geometric structure adaptation levels based on the structural heat dissipation efficiency and material filling ratio of the geometric feature parameters, and to establish a second mapping relationship between the curing performance level and the geometric structure adaptation level based on the preset product molding standard.

[0148] The second conversion module is used to convert the second mapping relationship into the first mapping relationship.

[0149] In some embodiments, the layup process includes layup sub-processes corresponding to each production area in the composite material product to be generated; the physical characteristics of sub-layup curing include curing exothermic rate and molding linear shrinkage rate. The process generation module 250 may include a preliminary process determination module and a parameter adjustment module.

[0150] The preliminary process determination module is used to determine each layup process sub-process and the corresponding candidate layup process parameters based on the product design information and production bill of materials for the composite material product to be generated.

[0151] The parameter adjustment module is used to adjust the process parameters of the candidate layup sub-process based on the curing exothermic rate and the molding linear shrinkage rate, and generate the layup sub-process parameters corresponding to each layup process sub-process; the process parameters of each layup sub-process are used as the layup process parameters.

[0152] In some embodiments, each production area includes a load-bearing area where the load requirement exceeds a preset load threshold and an insulation protection area; the parameter adjustment module may include a limit range determination module, a correction amount determination module and an adjustment submodule.

[0153] The limit range determination module is used to determine the process parameter adjustment area from the stress-bearing area and the insulation protection area where the curing heat release rate is within a preset curing heat release rate limit range and the molding linear shrinkage rate is within a preset molding linear shrinkage rate limit range.

[0154] The correction amount determination module is used to determine the correction amount for single-layer laying tension and the correction amount for interlayer compaction pressure based on the curing heat release rate and molding linear shrinkage rate.

[0155] The adjustment submodule is used to adjust the single-layer laying tension parameter and the interlayer compaction operation pressure in the candidate laying subprocess process parameters based on the single-layer laying tension correction amount and the interlayer compaction operation pressure correction amount.

[0156] In some embodiments, the manufacturing optimization apparatus 200 for the composite material may include a simulation model building module, a simulation module, a correction module, and a replacement module.

[0157] Before generating the layup process flow and layup process parameters based on the composite material layup topology and production bill of materials, the simulation model building module is used to build a product simulation model corresponding to the composite material product to be generated, according to the composite material layup topology and production bill of materials.

[0158] The simulation module is used to perform mechanical load simulation and curing deformation simulation on the product simulation model to obtain simulation results.

[0159] The correction module is used to correct the product design information and obtain the target product design information when the simulation results do not meet the preset mechanical load-bearing requirements and curing deformation requirements.

[0160] The replacement module is used to replace the product design information with the target product design information.

[0161] And trigger the first calling module 210, which is used to call the preset composite material layup rule model based on the product performance condition parameters corresponding to the composite material product to be generated, and match the composite material layup topology that meets the product performance condition parameters.

[0162] Figure 3 A schematic diagram of the hardware structure of the composite material manufacturing optimization equipment provided in an embodiment of this application is shown.

[0163] The equipment for optimizing the manufacturing of composite materials may include a processor 301 and a memory 302 storing computer program instructions.

[0164] Specifically, the processor 301 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0165] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. In one instance, memory 302 may include removable or non-removable (or fixed) media, or memory 302 may be non-volatile solid-state memory. Memory 302 may be internal or external to the integrated gateway disaster recovery device.

[0166] In one instance, memory 302 may be read-only memory (ROM). In one instance, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0167] Memory 302 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, generally, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to one aspect of this disclosure.

[0168] The processor 301 reads and executes computer program instructions stored in the memory 302 to achieve... Figure 1 The manufacturing optimization method for the composite material in the illustrated embodiment.

[0169] In one example, the composite material manufacturing optimization device may also include a communication interface 303 and a bus 304. As shown in the figure, the processor 301, memory 302, and communication interface 303 are connected via the bus 304 and communicate with each other.

[0170] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0171] Bus 304 includes hardware, software, or both, that couples components of an online data traffic metering device together. For example, and not as a limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 304 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0172] Furthermore, in conjunction with the composite material manufacturing optimization methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the composite material manufacturing optimization methods in the above embodiments.

[0173] This application also provides a computer program product, including a computer program that, when executed by a processor, implements any of the composite material manufacturing optimization methods described in the above embodiments.

[0174] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0175] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memory (ROM), flash memory, erasable read-only memory (EROM), floppy disks, compact disc read-only memory (CD-ROM), optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0176] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0177] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in 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, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0178] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for optimizing the manufacturing of composite materials, characterized in that, include: Based on the product performance condition parameters corresponding to the composite material product to be generated, a preset composite material layup rule model is called to match the composite material layup topology that satisfies the product performance condition parameters. The composite material layup topology includes the sub-layup topology corresponding to each production area in the composite material product to be generated. Extract the geometric feature parameters corresponding to the sub-layout topology; A first mapping relationship is established between the geometric feature parameters and the composite material curing properties of each candidate production material, and based on the first mapping relationship, the curing physical characteristics of the sub-layout corresponding to the sub-layout topology are determined; Using the solidified physical characteristics of the sub-layout as a threshold constraint, the production material list corresponding to the composite material product to be generated is retrieved from the preset manufacturing process parameter library. Based on the composite material layup topology and the production bill of materials, a layup process flow and layup process parameters are generated, and based on the production bill of materials, the layup process flow and the layup process parameters, a production task corresponding to the composite material product is generated; The production task is executed to prepare the target composite material product, and the quality monitoring data of the target composite material product and the equipment operation data of the equipment used to manufacture the target composite material product are collected simultaneously. Based on the preset process parameter optimization method, the quality monitoring data, and the equipment operation data, the production task is optimized to obtain optimized production process information.

2. The method according to claim 1, characterized in that, The product performance parameters include global load distribution, insulation protection level, and curing deformation sensitivity. The process of calling a preset composite material layup rule model based on the product performance condition parameters corresponding to the composite material product to be generated, and matching the composite material layup topology that satisfies the product performance condition parameters, includes: Through the performance constraint transformation model within the composite material layup rule model, the global load distribution, insulation protection level, and curing deformation sensitivity are transformed into regional performance constraint parameters corresponding to each region to be divided in the composite material product to be generated. Using the region division model within the composite material layup rule model, and based on the region performance constraint parameters corresponding to each region to be divided, the production regions in the composite material product to be generated are divided. The model is constructed by using the layup structure within the composite material layup rule model, and the fiber laying angle, number of layup layers, and interlayer arrangement order are retrieved to suit each of the production areas. Based on the fiber layup angle, the number of layup layers, and the interlayer arrangement order, a sub-layup topology corresponding to each production area is generated. Based on the stress transfer rules, ply overlap rules, and curing deformation co-adaptation rules between adjacent sub-ply topologies stored in the composite material ply rule model, each sub-ply topology is integrated into a composite material ply topology.

3. The method according to claim 1, characterized in that, Establishing a first mapping relationship between the geometric feature parameters and the composite material curing properties of each candidate production material includes: The curing properties of the candidate production materials are graded according to their curing performance to obtain the corresponding curing performance level of the candidate production materials. Based on the structural heat dissipation efficiency and material filling ratio of the geometric feature parameters, the corresponding geometric structure adaptation level of the geometric feature parameters is divided, and a second mapping relationship is established between the curing performance level and the geometric structure adaptation level according to the preset product molding standard. Transform the second mapping relationship into the first mapping relationship.

4. The method according to claim 1, characterized in that, The layup process includes layup process sub-processes corresponding to each production area in the composite material product to be generated. The physical characteristics of the sub-layout curing include curing heat release rate and molding linear shrinkage rate; The step of generating the layup process flow and layup process parameters based on the composite material layup topology and the production bill of materials includes: Based on the product design information corresponding to the composite material product to be generated and the production material list, determine each of the layup process sub-processes and the candidate layup sub-process process parameters corresponding to each of the layup process sub-processes; Based on the curing heat release rate and the molding linear shrinkage rate, adjust the process parameters of the candidate layup sub-process to generate the layup sub-process parameters corresponding to each layup process sub-process. The process parameters of each of the laying sub-processes are used as the laying process parameters.

5. The method according to claim 4, characterized in that, Each production area includes a load-bearing area where the load requirement exceeds a preset load threshold and an insulation protection area. Based on the curing exothermic rate and the molding linear shrinkage rate, the process parameters of the candidate layup sub-process are adjusted to generate the layup sub-process parameters corresponding to each of the layup process sub-processes, including: From the stress-bearing area and the insulation protection area, it is determined that the curing heat release rate is within a preset curing heat release rate limit range, and the molding linear shrinkage rate is within a preset molding linear shrinkage rate limit range, which is a process parameter to be adjusted area. Based on the curing heat release rate and the molding linear shrinkage rate, determine the single-layer laying tension correction amount and the interlayer compaction operation pressure correction amount; Based on the single-layer paving tension correction amount and the interlayer compaction operation pressure correction amount, adjust the single-layer paving tension parameter and the interlayer compaction operation pressure in the candidate paving sub-process parameters.

6. The method according to claim 1, characterized in that, Before generating the layup process flow and layup process parameters based on the composite material layup topology and the production bill of materials, the method further includes: Based on the composite material layup topology and the production bill of materials, construct a product simulation model corresponding to the composite material product to be generated; Mechanical load simulation and curing deformation simulation were performed on the product simulation model to obtain simulation results; If the simulation results do not meet the preset mechanical load-bearing requirements and curing deformation requirements, the product design information corresponding to the composite material product to be generated is corrected to obtain the target product design information. Replace the product design information with the target product design information, and return to execute the product performance condition parameters corresponding to the composite material product to be generated, call the preset composite material layup rule model, and match the composite material layup topology that meets the product performance condition parameters.

7. A manufacturing optimization apparatus for composite materials, characterized in that, The device includes: The first calling module is used to call a preset composite material layup rule model based on the product performance condition parameters corresponding to the composite material product to be generated, and match the composite material layup topology that satisfies the product performance condition parameters. The composite material layup topology includes the sub-layup topology corresponding to each area to be produced. The extraction module is used to extract the geometric feature parameters corresponding to the sub-layout topology; The determination module is used to establish a first mapping relationship between the geometric feature parameters and the composite material curing properties of each candidate production material, and to determine the sub-layup curing physical characteristics corresponding to the sub-layup topology based on the first mapping relationship; The second calling module is used to use the solidification physical characteristics of the sub-layout as a threshold constraint to call the production material list corresponding to the composite material product to be generated from the preset manufacturing process parameter library. The process generation module is used to generate a layup process flow and layup process parameters based on the composite material layup topology and the production bill of materials, and to generate a production task corresponding to the composite material product based on the production bill of materials, the layup process flow and the layup process parameters. The preparation module is used to perform the production task, prepare the target composite material product, and simultaneously collect quality monitoring data of the target composite material product and equipment operation data for manufacturing the target composite material product; The optimization module is used to optimize the production task based on preset process parameter optimization methods, the quality monitoring data, and the equipment operation data, to obtain optimized production process information.

8. A manufacturing optimization device for composite materials, characterized in that, The device includes: a processor and a memory storing computer program instructions; the processor reads and executes the computer program instructions to implement the method as described in any one of claims 1-6.

9. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions, which, when executed by a processor, implement the method as described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1-6.

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