Composite material processing parameter optimization method and system
Through multiple orthogonal experiments and process experiments, combined with polynomial fitting and neural network model, the optimal processing parameters are dynamically updated, which solves the problem of tool wear in fiber reinforced composite processing, and improves processing quality and tool life.
Patent Information
- Application Number
- CN202510448775.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-04-10
- Publication Date
- 2025-07-11
AI Technical Summary
In the processing of fiber reinforced composite materials, the dynamic impact of tool wear on processing parameters is not effectively considered in the prior art, resulting in short tool service life and unstable processing quality.
Multiple orthogonal experiments and process experiments are adopted, combined with polynomial fitting and neural network model, the optimal machining parameters are dynamically updated, the degree of tool wear is considered, and the cutting parameters are optimized to adapt to tool state changes.
Improve the processing quality and tool service life of fiber reinforced composite materials, dynamically optimize parameters to adapt to tool wear, extend tool life and stabilize processing effect.
Smart Images
Figure CN120299587A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of composite material processing data analysis, and particularly to a method and system for optimizing composite material processing parameters.
Background Art
[0002] Due to their excellent properties and lightweight advantages, fiber-reinforced composite materials are widely used in high-end manufacturing fields such as aerospace and automotive. However, fiber-reinforced composite materials are hard and brittle, and are typical difficult-to-machine materials. Optimizing cutting parameters is an important means to improve machining quality. Existing technical solutions generally adopt the orthogonal experiment method to calculate and select the optimal parameters that meet specific requirements. The specific steps are as follows:
[0003] 1. Determine the parameters to be optimized and their variation ranges. According to the constraints such as the type of machining material, precision, and equipment, determine the parameters to be optimized, such as cutting speed, feed rate, and tool geometry angle. The variation range of each parameter is given based on actual machining experience. Parameters exceeding the given range are considered unable to improve machining effects and are no longer considered during the optimization process.
[0004] 2. Design the parameter optimization scheme. Generally, the orthogonal experiment method is adopted to compile an orthogonal experiment table to arrange the mutual matching relationship of parameters. First, determine the number of factors of the orthogonal experiment table according to the number of parameters to be optimized. The number of factors is equal to the number of parameters to be optimized. Then, determine the number of levels of the orthogonal experiment table according to the variation range of each parameter. Finally, select a suitable orthogonal experiment table according to the factors and the number of levels to complete the matching combination of each level of different parameters.
[0005] 3. Conduct cutting experiments and calculate the optimal parameters. First, perform actual cutting according to different levels in the orthogonal experiment table, that is, each parameter combination, and repeat each set of parameters 1-2 times. Measure the evaluation indexes that need to be concerned, such as cutting force, surface roughness, and subsurface damage. Then, conduct an intuitive analysis of the experimental results to obtain the influence of each factor on one or several indexes, so as to first determine the relatively optimal process parameter combination. Then, conduct a variance analysis to further obtain the significance degree of the influence of each factor on the indexes. Finally, combine the intuitive analysis and variance analysis to determine the optimal process parameters.
[0006] In actual machining, when using existing parameter optimization methods, it often occurs that "one parameter is used for the entire machining process of one tool", that is, the optimal parameters are continuously used throughout the tool life cycle. However, the machining of fiber-reinforced composite materials is different from that of metal materials. When machining metal, a slip zone is formed under the action of the rake face, and the interaction between the cutting edge and the material is relatively weak. While the fracture of fibers mainly relies on the force applied by the cutting edge to cut, so the cutting edge bears a heavier burden, resulting in an extremely fast tool wear rate. When using high-speed steel tools to machine fiber-reinforced composite materials, the tool is even worn out after one machining and cannot be used anymore. Although the wear rate of cemented carbide and corresponding coated tools is relatively slower than that of high-speed steel, the continuous change of the nose radius will inevitably affect the machining quality. Therefore, one parameter cannot adapt to the entire tool life cycle, and the machining method of keeping the parameters unchanged may even reduce the tool service life.
[0007] During the machining process of FRP materials, as the tool wears, the initial optimal machining parameters no longer match the current tool state, that is, the optimal machining parameters should be dynamically changed. The main purpose of this technical solution is to solve the mismatch problem between the fixed optimal machining parameters and the dynamically changing tool state in the existing machining methods, and to achieve the dynamic optimization of machining parameters.
[0008] In addition, there are also the following problems in the relevant content of existing patents and literature:
[0009] 1. The patent "A Green Optimization Method for Aeronautical Drilling Process Parameters Based on Multi-Level Grey Correlation Analysis" discloses a process parameter optimization method considering machining efficiency and machine tool energy consumption, but does not incorporate the dynamic influence of tool wear into the optimization process.
[0010] 2. The patent "Optimization Method for Process Parameters of Composite Material Laser Ultrasonic Vibration Milling Drilling" uses multiple regression analysis and genetic algorithms to systematically optimize the process parameters of composite material laser ultrasonic vibration milling drilling, improving the prediction accuracy of tool wear rate and drilling defect rate. However, its dynamic wear process is not fully simulated. This invention mainly calculates the wear rate based on the tool images before and after drilling experiments, which belongs to the static analysis method.
[0011] 3. The patent "A Method for Monitoring the Wear of Carbon Fiber Reinforced Composite Drills" discloses a method for monitoring the wear of carbon fiber reinforced composite drills, combining model-driven and data-driven, using current, vibration, and acoustic emission signals, and performing fusion monitoring through neural networks and Kalman filters. This patent only realizes tool wear detection and does not involve process parameter optimization.
[0012] 4. The patent "Composite Material Processing Tool Passivation Detection System and Method Based on Data Processing" discloses a composite material processing tool passivation detection system and method based on data processing, which improves the accuracy of composite material processing tool passivation detection. Bayesian inference and parameter optimization are performed on the current processing parameter combination according to the real-time estimated value to obtain the target processing parameter combination. However, the purpose of the parameter optimization carried out is to reduce tool wear, and it is not aimed at improving the overall hole-making quality or a certain quality index.
[0013] 5. The paper "Research on Optimization of Drilling Process Parameters and Tool Wear of Carbon Fiber Composite Materials" proposes to optimize the drilling process parameters by using the whale multi-objective optimization algorithm, and constructs a multi-objective optimization model with the minimum axial force, temperature, hole delamination and tool wear as the objectives, but does not consider the influence of tool wear on the change of optimal parameters.
[0014] 6. The paper "Optimization Decision of CFRP Processing Parameters Considering Cutting Energy Consumption and Surface Quality" proposes a multi-objective optimization model with cutting energy consumption and surface roughness as the objectives, as well as a process parameter optimization decision method integrating the diverse mutation-driven particle swarm optimization algorithm (DM-PSO) and the analytic hierarchy process (AHP), but does not consider the influence of tool wear on processing parameters.
[0015] 7. The paper "Multi-objective Optimization Method for Drilling Process Parameters of CFRP / Ti Laminated Components" establishes a multi-objective optimization model for drilling process parameters of CFRP / Ti laminated components according to the actual process conditions; combines the characteristics of discretization of drilling process parameters, constructs an exhaustive search algorithm for Pareto optimal process parameter set based on the Pareto dominance principle, and constructs an optimal process parameter decision algorithm based on the tolerance hierarchical sequence method; takes the T700-TDE85 / Ti-6Al-4V laminated component as the object, obtains the optimal process parameters, and verifies the feasibility and effectiveness of the method, but does not consider the influence of tool wear on processing parameters.
[0016] 8. The paper "Investigation and optimization of maching parameters indrilling of carbon fiber reinforced polymer(CFRP)composites" optimizes the processing parameters with multiple performance characteristics such as delamination coefficient, surface roughness and roundness when drilling carbon fiber reinforced polymer (CFRP) along the fiber direction by using grey relational analysis (GRA) combined with Taguchi technology, and does not consider the influence of tool wear on processing parameters.
[0017] Therefore, it is necessary to study a composite material processing parameter optimization method and system to address the deficiencies of the existing technology and solve or mitigate one or more of the above problems.
Summary of the Invention
[0018] In view of this, the present invention provides a method and system for optimizing composite material processing parameters, which can dynamically optimize the cutting parameters of fiber-reinforced composite materials on the premise of considering the influence of tool wear.
[0019] On the one hand, the present invention provides a method for optimizing composite material processing parameters, and the method for optimizing composite material processing parameters includes the following steps:
[0020] S1: Determine the parameters to be optimized and their variation ranges during the processing of composite materials;
[0021] S2: Conduct multiple orthogonal experiments on the parameters to be optimized and compile a combined scheme for the experimental results;
[0022] S3: Conduct process experiments on the compiled combined scheme;
[0023] S4: For the process experiments of the compiled combined scheme, establish a characterization change function of the compiled combined scheme with the physical characteristics of the final product as the result;
[0024] S5: Determine the evaluation index of the orthogonal experiment according to the characterization change function of the compiled combined scheme;
[0025] S6: Solve the optimal parameters of the parameters to be optimized within their variation ranges through the evaluation index of the orthogonal experiment;
[0026] S7: Dynamically update the optimal parameters.
[0027] For the above aspects and any possible implementation manners, a further implementation manner is provided. Specifically, S1 includes: determining the parameters to be optimized according to the processing material type, accuracy, and constraint conditions of the equipment, and giving the variation range of each parameter based on the results of the actual processing process.
[0028] For the above aspects and any possible implementation manners, a further implementation manner is provided. Specifically, S2 includes:
[0029] S21: Adopt the orthogonal experiment method to compile an orthogonal experiment table to arrange the mutual matching relationship of parameters;
[0030] S22: Select different composite materials and perform parameter matching. The number of composite materials is equal to the number of levels of the orthogonal experiment table. Number the different composite materials in the order of the number of levels. Each composite material corresponds to the parameter to be optimized at one level, and keep the parameter to be optimized corresponding to the composite material in the subsequent experiments.
[0031] For the aspects and any possible implementation manners described above, a further implementation manner is provided. Specifically, S21 is as follows: Determine the number of factors of the orthogonal experiment table according to the number of parameters to be optimized, and the number of factors is equal to the number of parameters to be optimized; Determine the number of levels of the orthogonal experiment table according to the variation range of each parameter to be optimized; Select a suitable orthogonal experiment table according to the factors and the number of levels to complete the matching combination of each level of different parameters.
[0032] For the aspects and any possible implementation manners described above, a further implementation manner is provided. Specifically, S3 includes continuously processing all numbered composite materials according to the process parameters at their respective corresponding levels under specific machine tool and equipment conditions, using the same composite materials as in actual production to complete the process experiment.
[0033] For the aspects and any possible implementation manners described above, a further implementation manner is provided. Specifically, S4 includes: Using the parameter to be optimized as the abscissa and the physical property result after processing the composite material as the ordinate, mark the physical property results of each composite material when the corresponding parameter to be optimized, and then use polynomial fitting to obtain the functional formula of the change of the physical property results with the increase of the parameter to be optimized, so as to characterize the change after processing the composite material, and fit each composite material to obtain the corresponding functional formula.
[0034] For the aspects and any possible implementation manners described above, a further implementation manner is provided. Specifically, S5 includes: Grouping the physical property results of each composite material in a preset manner, using the change function of the physical property results of each composite material, and inversely obtaining the serial number of the parameter to be optimized corresponding to the same physical property result for each composite material, selecting the serial number of the parameter to be optimized corresponding to the same physical property result for the composite material, and using the quantization result of the serial number of the parameter to be optimized as the evaluation index.
[0035] For the aspects and any possible implementation manners described above, a further implementation manner is provided. Specifically, S6 is as follows: According to the evaluation index obtained in S5, use the orthogonal experiment principle to conduct intuitive analysis and variance analysis to obtain the optimal process parameters under the physical property results of the selected grouping; Analyze the change law of the optimal parameters with the increase of the physical property results, and establish a mapping relationship from the physical property results to the optimal parameters of the parameters to be optimized.
[0036] For the aspects and any possible implementation manners described above, a further implementation manner is provided. Specifically, S7 is as follows:
[0037] S71: Given the initial value of the optimal parameter;
[0038] S72: Starting from the initial value of the optimal parameter, predict the physical property results of the parameter to be optimized;
[0039] S73: Update the optimal parameters according to the physical property results of the parameters to be optimized predicted.
[0040] For the aspects and any possible implementation manners as described above, a composite material processing parameter optimization system is further provided. The composite material processing parameter optimization system includes an input module, a central module and an output module. The input module is connected to the output module through the central module. A processor is provided on the central module, and the processor is configured to execute the composite material processing parameter optimization method described above.
[0041] Compared with the prior art, the present invention can achieve the following technical effects:
[0042] The present invention solves the problem that during the processing of FRP materials, as the tool wear increases, the initial optimal processing parameters are no longer "optimal". By using the method proposed by the present invention, the dynamic update of the optimal parameters can be ensured, effectively improving the processing quality and the tool service life.
[0043] Of course, it is not necessary for any product implementing the present invention to achieve all the technical effects described above simultaneously.
Description of the Drawings
[0044] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.
[0045] Figure 1 is a flowchart of a composite material processing parameter optimization method provided by an embodiment of the present invention.
Detailed Embodiments
[0046] In order to better understand the technical solutions of the present invention, the embodiments of the present invention will be described in detail below with reference to the drawings.
[0047] It should be clear that the described embodiments are only some embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.
[0048] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "the" and "said" used in the embodiments of the present invention and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.
[0049] As Figure 1As shown in the figure, the present invention provides a method for optimizing composite material processing parameters, and the method for optimizing composite material processing parameters includes the following steps:
[0050] S1: Determine the parameters to be optimized and their variation ranges during the processing of composite materials;
[0051] S2: Conduct multiple orthogonal experiments on the parameters to be optimized and compile a combination plan for the experimental results;
[0052] S3: Conduct process experiments on the compiled combination plan;
[0053] S4: For the process experiments of the compiled combination plan, taking the physical characteristics of the final product as the result, establish a characterization change function for the compiled combination plan;
[0054] S5: Determine the evaluation index of the orthogonal experiment according to the characterization change function of the compiled combination plan;
[0055] S6: Solve the optimal parameters of the parameters to be optimized within their variation ranges through the evaluation index of the orthogonal experiment;
[0056] S7: Dynamically update the optimal parameters.
[0057] The specific content of S1 includes: Determine the parameters to be optimized according to the processing material type, accuracy and equipment constraints, and give the variation range of each parameter based on the results of the actual processing process.
[0058] The specific content of S2 includes:
[0059] S21: Adopt the orthogonal experiment method to compile an orthogonal experiment table to arrange the mutual matching relationship of parameters;
[0060] S22: Select different composite materials and conduct parameter matching. The number of composite materials is equal to the number of levels of the orthogonal experiment table. Number the different composite materials in the order of the number of levels. Each composite material corresponds to the parameters to be optimized at one level, and keep the parameters to be optimized corresponding to the composite material in the subsequent experiments.
[0061] The specific content of S21 is: Determine the number of factors of the orthogonal experiment table according to the number of parameters to be optimized, and the number of factors is equal to the number of parameters to be optimized; Determine the number of levels of the orthogonal experiment table according to the variation range of each parameter to be optimized; Select a suitable orthogonal experiment table according to the factors and the number of levels to complete the matching combination of each level of different parameters.
[0062] The specific content of S3 includes continuously processing all numbered composite materials according to their respective process parameters at the corresponding levels under specific machine tool and equipment conditions, using the same composite materials as in actual production to complete the process experiments.
[0063] The specific steps of S4 are as follows: taking the parameter to be optimized as the abscissa and the physical property result after processing the composite material as the ordinate, marking the physical property results of each composite material corresponding to the parameter to be optimized, and then using polynomial fitting to obtain the functional formula of the change of the physical property results with the increase of the parameter to be optimized, so as to characterize the change after processing the composite material, and fitting a corresponding functional formula for each composite material.
[0064] The specific steps of S5 are as follows: grouping the physical property results of each composite material in a preset manner, using the change function of the physical property results of each composite material, inversely calculating the serial number of the parameter to be optimized corresponding to the same physical property result for each composite material, selecting the serial number of the parameter to be optimized corresponding to the same physical property result, and taking the quantization result of the serial number of the parameter to be optimized as the evaluation index.
[0065] The specific step of S6 is: according to the evaluation index obtained in S5, using the principle of orthogonal experiment for intuitive analysis and variance analysis to obtain the optimal process parameters under the physical property results of the selected grouping; analyzing the change law of the optimal parameters with the increase of the physical property results, and establishing the mapping relationship from the physical property results to the optimal parameters of the parameter to be optimized.
[0066] The specific step of S7 is:
[0067] S71: Given the initial value of the optimal parameter;
[0068] S72: Starting from the initial value of the optimal parameter, predicting the physical property results of the parameter to be optimized;
[0069] S73: Updating the optimal parameter according to the predicted physical property results of the parameter to be optimized.
[0070] The present invention also provides a composite material processing parameter optimization system, which includes an input module, a central module and an output module. The input module is connected to the output module through the central module; a processor is provided on the central module, and the processor is used to execute the composite material processing parameter optimization method described above.
[0071] Example 1:
[0072] In the present invention, the wear of the cutting tool is characterized by the quantization of the nose radius, and the nose radius is measured by a commercial cutting tool wear measuring instrument. To visualize the implementation manner of this method, taking the drilling of FRP as an example for illustration, the specific steps are as follows:
[0073] 1. Determine the parameters to be optimized and their variation ranges.
[0074] According to the constraints such as the type of processing material, precision, and equipment, determine the hole-making parameters to be optimized. Given the variation range of each parameter based on actual hole-making experience, parameters exceeding the given range are considered unable to improve the processing effect and will not be considered during the optimization process.
[0075] 2. Compile the experimental parameter combination plan.
[0076] In the first step, adopt the orthogonal experiment method to compile an orthogonal experiment table to arrange the mutual matching relationship of parameters, that is: first, determine the number of factors of the orthogonal experiment table according to the number of hole-making parameters to be optimized, and the number of factors is equal to the number of parameters to be optimized; then, determine the number of levels of the orthogonal experiment table according to the variation range of each parameter; finally, select a suitable orthogonal experiment table according to the factors and the number of levels to complete the matching combination of each level of different parameters.
[0077] In the second step, select new drills and match the hole-making parameters. The number of new drills is equal to the number of levels of the orthogonal experiment table. Number the new drills in the order of the number of levels. Each new drill corresponds to the hole-making parameters under one level, and keep the parameters corresponding to this drill unchanged in the subsequent hole-making experiments.
[0078] 3. Conduct hole-making process experiments.
[0079] For all numbered new drills, under the process parameters corresponding to their respective levels, use the same material as in actual production to continuously make holes under specific machine tool and equipment conditions. The number of holes to be made is estimated by general experience. For example, when a certain type of cemented carbide drill processes carbon fiber composite materials, when continuously processing 100 holes, the hole diameter accuracy or a certain processing accuracy no longer meets the requirements, then the number of holes to be made is selected as 100 - 150. During this process, measure the tip radius of the main cutting edge of each drill every several holes made, and the measurement interval is determined according to the actual situation.
[0080] 4. Characterize the tool wear degree.
[0081] Use the number of holes made as the abscissa and the tip radius value as the ordinate to mark the tip radius of each tool at the corresponding number of holes made. Then, use polynomial fitting to obtain the function formula for the change of the tip radius with the increase in the number of holes made, so as to characterize the change of the tool wear degree, and obtain the corresponding function formula for each tool by fitting.
[0082] 5. Determine the evaluation index of the orthogonal experiment. For each tool, select 4 - 6 nose radius values from small to large at a specific interval. For example, select four groups of nose radius values: 3μm, 6μm, 9μm, and 12μm. Using the nose radius change function of each tool, inversely calculate the hole - making number sequence number corresponding to the same nose radius degree of wear for each tool. Select the hole - making sequence numbers corresponding to the same nose radius for each tool on the test piece, and use these selected holes as the basis for measuring the evaluation index, and quantitatively measure the index values of the holes corresponding to these sequence numbers, such as hole diameter, burr, or axial force, etc.
[0083] 6. Solve the optimal parameters corresponding to a specific wear degree. According to the evaluation index obtained in step 5, use the principle of orthogonal experiment for intuitive analysis and variance analysis to obtain the optimal process parameters under the selected nose radius of the tool, that is, under a specific tool wear degree. Analyze the variation law of the optimal parameters with the increase of the nose radius, and establish a mapping relationship from the nose radius to the optimal parameters.
[0084] 7. Dynamic update of the optimal parameters.
[0085] The first step is to give the initial value of the optimal parameters. The initial value of the optimal parameters corresponds to the optimal parameters when the hole - making sequence number of the tool is "1".
[0086] The second step is to predict the nose radius of the tool. Theoretically, the nose radius increases with each hole made. Therefore, ideally, predicting the nose radius once for each hole made can obtain the highest accuracy. However, in practice, the sensitivity of the optimal parameters to the change in the nose radius may not be very high. Therefore, the nose radius is predicted using the neural network model every 5 - 10 holes. In addition, there is a certain uncertainty in the change of the nose radius, that is, when multiple tools with the same geometric structure make holes under the same working conditions, after the number of holes made reaches a certain amount, the nose radius values of each tool show a normal distribution. The prediction of the nose radius is crucial for the dynamic adjustment of the parameters. The present invention proposes two prediction methods:
[0087] (1) Neural network model prediction method. Using the functional formula characterizing the tool wear degree in step 4, calculate the nose radius values of the tool corresponding to a specific hole - making sequence number under different combinations of spindle speeds and feed rates to form a data set. Use a part of the data set as the training data of the neural network and another part as the verification data set to predict the nose radius of the tool.
[0088] (2) Mean value prediction method. Considering the uncertainty of the nose radius, using a complex model for prediction cannot obtain an accurate value that must be consistent with the actual situation. Therefore, to improve efficiency, the mean value prediction method is directly adopted, that is: sum up the nose radii obtained by each tool in step 4 when making the same number of holes and calculate the mean value, and then fit this mean value into a new polynomial again to form a set of average values of the nose radius change.
[0089] Step 3: Update the optimal parameters. According to the new blunt radius in the previous step, re-match and map the optimal process parameters according to the method described in Step 6.
[0090] Thus, it is possible to dynamically adjust the optimal process parameters as the number of holes drilled increases. The dynamic optimization of the optimal parameters for other cutting processes of FRP is the same as the above hole-making process.
[0091] The above has introduced in detail a method and system for optimizing composite material processing parameters provided by an embodiment of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application; at the same time, for those of ordinary skill in the art, according to the idea of the present application, there will be changes in the specific implementation manner and application scope. In summary, the content of this specification should not be construed as a limitation to the present application.
[0092] As used in the specification and claims, certain terms are used to refer to specific components. Those skilled in the art should understand that hardware manufacturers may use different terms to refer to the same component. The specification and claims do not use the difference in name as a way to distinguish components, but use the difference in function of components as the criterion for distinction. As used throughout the specification and claims, the terms "comprising" and "including" are open-ended terms, and should therefore be interpreted as "including / including but not limited to". "Roughly" means within an acceptable error range. Those skilled in the art can solve the technical problem within a certain error range and basically achieve the technical effect. The subsequent description of the specification is a preferred implementation manner for implementing the present application, but the description is for the purpose of explaining the general principle of the present application and is not used to limit the scope of the present application. The protection scope of the present application shall be subject to what is defined by the appended claims.
[0093] It should also be noted that the term "including", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a commodity or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such a commodity or system. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the commodity or system including the said element.
[0094] It should be understood that the term "and / or" used herein is only a relationship describing associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally represents an "or" relationship between the associated objects before and after.
[0095] The foregoing description has shown and described several preferred embodiments of the present application. However, as previously mentioned, it should be understood that the present application is not limited to the forms disclosed herein, should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications, and environments, and can be changed within the scope of the application concept described herein through the above teachings or the techniques or knowledge in the relevant field. Any changes and variations made by those skilled in the art without departing from the spirit and scope of the present application shall fall within the protection scope of the appended claims of the present application.
Claims
1. A method for optimizing processing parameters of a composite material, characterized in that, The method for optimizing the processing parameters of the composite material includes the following steps: S1: Determine the parameters to be optimized and their variation ranges during the processing of the composite material; S2: Conduct multiple orthogonal experiments on the parameters to be optimized and compile a combined scheme for the experimental results; S3: Conduct process experiments on the compiled combined scheme; S4: For the process experiments of the compiled combined scheme, taking the physical characteristics of the final product as the result, establish a characterization change function for the compiled combined scheme; S5: Determine the evaluation index of the orthogonal experiment according to the characterization change function of the compiled combined scheme; S6: Solve the optimal parameters of the parameters to be optimized within their variation ranges through the evaluation index of the orthogonal experiment; S7: Dynamically update the optimal parameters.
2. The method for optimizing the composite material processing parameters according to claim 1, characterized in that The specific content of S1 includes: Determine the parameters to be optimized according to the type of processing material, accuracy, and constraint conditions of the equipment, and give the variation range of each parameter according to the results of the actual processing process.
3. The method for optimizing composite material processing parameters according to claim 1, characterized in that The specific content of S2 includes: S21: Adopt the orthogonal experiment method and compile an orthogonal experiment table to arrange the mutual matching relationship of the parameters; S22: Select different composite materials and perform parameter matching. The number of composite materials is equal to the number of levels of the orthogonal experiment table. Number the different composite materials in the order of the number of levels. Each composite material corresponds to the parameters to be optimized at one level, and keep the parameters to be optimized corresponding to the composite material in the subsequent experiments.
4. The method for optimizing the composite material processing parameters according to claim 3, wherein The specific content of S21 is: Determine the number of factors of the orthogonal experiment table according to the number of parameters to be optimized, and the number of factors is equal to the number of parameters to be optimized; Determine the number of levels of the orthogonal experiment table according to the variation range of each parameter to be optimized; Select a suitable orthogonal experiment table according to the factors and the number of levels to complete the matching combination of each level of different parameters.
5. The method for optimizing composite material processing parameters according to claim 1, characterized in that, The specific content of S3 includes continuously processing all numbered composite materials according to their respective process parameters at the corresponding levels under specific machine tool and equipment conditions, using the same composite materials as in actual production to complete the process experiments.
6. The method for optimizing composite material processing parameters according to claim 1, wherein The specific content of S4 includes: Taking the parameters to be optimized as the abscissa and the physical characteristics results after processing the composite material as the ordinate, mark the physical characteristics results when each composite material corresponds to the parameters to be optimized, and then use polynomial fitting to obtain the function formula for the change of the physical characteristics results as the parameters to be optimized increase, so as to characterize the change after processing the composite material, and fit a corresponding function formula for each composite material.
7. The method for optimizing composite material processing parameters according to claim 1, wherein The specific content of S5 includes: Group the physical characteristics results for each composite material in a preset manner, use the change function of the physical characteristics results of each composite material itself to inversely calculate the serial number of the parameters to be optimized corresponding to the same physical characteristics result for each composite material, select the serial number of the parameters to be optimized corresponding to the same physical characteristics result for the composite material, and use the quantization result of the serial number of the parameters to be optimized as the evaluation index.
8. The method for optimizing composite material processing parameters according to claim 1, characterized in that, The specific content of S6 is: According to the evaluation index obtained in S5, use the principle of orthogonal experiment for intuitive analysis and variance analysis to obtain the optimal process parameters under the physical characteristics results of the selected group; Analyze the change law of the optimal parameters as the physical characteristics results increase, and establish a mapping relationship from the physical characteristics results to the optimal parameters of the parameters to be optimized.
9. The method for optimizing composite material processing parameters according to claim 1, characterized in that The specific content of S7 is: S71: Given the initial values of the optimal parameters; S72: Starting from the initial values of the optimal parameters, predict the physical property results of the parameters to be optimized; S73: Update the optimal parameters according to the predicted physical property results of the parameters to be optimized.
10. A composite material processing parameter optimization system, characterized in that, The composite material processing parameter optimization system includes an input module, a central module, and an output module. The input module is connected to the output module through the central module; a processor is provided on the central module, and the processor is used to execute the composite material processing parameter optimization method according to any one of claims 1-9.