A dry processing and forming analysis method for special-shaped parts made of carbon fiber composite materials

Abstract

The present invention relates to a dry processing forming analysis method for special-shaped parts of carbon fiber composite materials, belonging to the technical field of dry processing of carbon fiber collection. The present invention constructs an energy consumption prediction model based on the historical energy consumption change characteristic data information of the processing machine tool under various processing condition data, and finally predicts the energy consumption information of each processing sub-route based on the energy consumption prediction model and the estimated cutting force information at each position of each processing sub-route, and optimizes the dry processing forming of the special-shaped parts of carbon fiber composite materials according to the energy consumption information of each processing sub-route to obtain the final processing route. The present invention analyzes the energy consumption of the processing route of the special-shaped parts of carbon fiber composite materials, so as to be able to obtain a better processing route according to the energy consumption situation, reduce the processing energy consumption of the special-shaped parts of carbon fiber composite materials, and further reduce the processing cost of the special-shaped parts of carbon fiber composite materials.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon fiber processing, and particularly to an analysis method for dry processing and forming of special-shaped parts of carbon fiber composites. Background Art

[0002] Carbon fiber materials have many advantages such as high specific strength, high specific stiffness, corrosion resistance, and designability, and are increasingly widely used in fields such as aerospace and rail transit lightweighting. When carbon fiber materials are applied in these fields, they usually need secondary processing, such as through milling, drilling, etc. to achieve the final dimensional accuracy and assembly requirements of components. Statistical data shows that: the global consumption of carbon fiber in 2020 increased by 4.94% year-on-year compared with 2019, reaching 107,000 tons. In the future, the demand for carbon fiber will continue to be in a high-growth period, and it is expected to reach 200,000 tons in 2025 and 400,000 tons in 2030. Therefore, the energy consumption generated by the secondary processing of carbon fiber materials will also increase rapidly. However, during the processing, if the processing route planning is unreasonable, it will lead to an increase in the working power of the processing machine tool, and the energy consumption during the processing will be very high during long-term work, resulting in an increase in processing costs. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides an analysis method for dry processing and forming of special-shaped parts of carbon fiber composites.

[0004] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] The first aspect of the present invention provides an analysis method for dry processing and forming of special-shaped parts of carbon fiber composites, specifically including:

[0006] Obtain the process element information of the special-shaped parts of carbon fiber composites, and construct the processing route of the special-shaped parts of carbon fiber composites according to the process element information of the special-shaped parts of carbon fiber composites;

[0007] Set the feed rate and the depth of cut, and analyze the processing route of the special-shaped parts of carbon fiber composites according to the feed rate and the depth of cut to obtain the estimated cutting force information at each position of each processing sub-route;

[0008] Obtain the historical energy consumption change characteristic data information of the processing machine tool under various processing condition data, and construct an energy consumption prediction model according to the historical energy consumption change characteristic data information of the processing machine tool under various processing condition data;

[0009] Predict the energy consumption information of each machining sub-route based on the energy consumption prediction model and the predicted cutting force information of each machining sub-route at each position, and optimize the dry machining and forming of the carbon fiber composite special-shaped part according to the energy consumption information of each machining sub-route to obtain the final machining route.

[0010] Further, in this method, obtain the process element information of the carbon fiber composite special-shaped part, and construct the machining route of the carbon fiber composite special-shaped part according to the process element information of the carbon fiber composite special-shaped part, specifically:

[0011] Obtain the machining drawing information of the carbon fiber composite special-shaped part, and extract the machining elements from the machining drawing information of the carbon fiber composite special-shaped part to obtain the process element information of the carbon fiber composite special-shaped part;

[0012] Introduce the genetic algorithm, set the number of genetic generations based on the genetic algorithm, and perform initial route planning for the machining route according to the process element information of the carbon fiber composite special-shaped part, and use the initial route information of the carbon fiber composite special-shaped part as the population;

[0013] Perform genetic operations on the population until the maximum number of genetic generations is reached, then obtain the initial route information of the carbon fiber composite special-shaped part, and obtain the machining distance information of each initial route information;

[0014] Set a machining distance threshold. When the machining distance information is less than the initial route information corresponding to the machining distance threshold, and use the initial route information with the machining distance information less than the machining distance threshold as the machining route of the carbon fiber composite special-shaped part.

[0015] Further, in this method, set the feed rate and the depth of cut, and analyze the machining route of the carbon fiber composite special-shaped part according to the feed rate and the depth of cut to obtain the predicted cutting force information of each machining sub-route at each position, specifically:

[0016] Obtain the machining cutting force information of the carbon fiber composite special-shaped part under various feed rates and depths of cut through big data, and introduce a graph neural network, and input the machining cutting force information of the carbon fiber composite special-shaped part under various feed rates and depths of cut into the graph neural network;

[0017] Use the feed rate and the depth of cut as the first node, and the machining cutting force information as the second node to construct a directed description relationship, and connect the first node and the second node based on the directed description relationship to construct a topological structure diagram;

[0018] Construct a knowledge graph, input the topological structure diagram into the knowledge graph for storage, and set the feed rate and depth of cut. Input the feed rate and depth of cut into the knowledge graph for data matching;

[0019] Through data matching, obtain the estimated cutting force information corresponding to the feed rate and depth of cut, segment the processing route of the carbon fiber composite material special-shaped part to obtain several sub-routes, and obtain the estimated cutting force information at each position of each processing sub-route.

[0020] Further, in this method, obtain the historical energy consumption change characteristic data information of the processing machine tool under various processing condition data, and construct an energy consumption prediction model according to the historical energy consumption change characteristic data information of the processing machine tool under various processing condition data, specifically including:

[0021] Obtain the historical energy consumption change characteristic data information of the processing machine tool under various processing condition data, construct an energy consumption prediction model based on a deep neural network, and construct a feature matrix according to the historical energy consumption change characteristic data information of the processing machine tool under various processing condition data;

[0022] Input the feature matrix into the energy consumption prediction model for training. When the loss function of the energy consumption prediction model converges to a preset value, save the model parameters of the energy consumption prediction model and output the energy consumption prediction model.

[0023] Further, in this method, predict the energy consumption information of each processing sub-route based on the energy consumption prediction model and the estimated cutting force information at each position of each processing sub-route, specifically:

[0024] Obtain the historical energy consumption change characteristic data information of the processing machine tool within a preset time, and obtain the processing distance information of each processing sub-route;

[0025] Input the processing distance information of each processing sub-route, the estimated cutting force information at each position of each processing sub-route, and the historical energy consumption change characteristic data information of the processing machine tool within a preset time into the energy consumption prediction model for prediction;

[0026] Through prediction, obtain the energy consumption information of each processing sub-route, and output the energy consumption information of each processing sub-route as the output result.

[0027] Further, in this method, optimize the dry processing and forming of the carbon fiber composite material special-shaped part according to the energy consumption information of each processing sub-route to obtain the final processing route, specifically:

[0028] According to the energy consumption information of each processing sub - route, count the total energy consumption information of the processing route of each special - shaped carbon fiber composite part, and set a total energy consumption threshold to determine whether there is a processing route of a special - shaped carbon fiber composite part with total energy consumption information greater than the total energy consumption threshold;

[0029] When there is a processing route of a special - shaped carbon fiber composite part with total energy consumption information greater than the total energy consumption threshold, then use the processing route of the special - shaped carbon fiber composite part with total energy consumption information greater than the total energy consumption threshold as the final processing route;

[0030] When there is no processing route of a special - shaped carbon fiber composite part with total energy consumption information greater than the total energy consumption threshold, then re - plan the feed rate and the depth of cut, and obtain the total energy consumption information of the processing route of each special - shaped carbon fiber composite part under the feed rate and the depth of cut;

[0031] When the total energy consumption information is not greater than the total energy consumption threshold, then obtain the processing route of the special - shaped carbon fiber composite part with total energy consumption information not greater than the total energy consumption threshold, and sort the processing routes of the special - shaped carbon fiber composite parts with total energy consumption information not greater than the total energy consumption threshold, and obtain the processing route with the minimum total energy consumption as the final processing route.

[0032] The second aspect of the present invention provides a dry - processing forming analysis system for special - shaped carbon fiber composite parts, including a memory and a processor. The memory includes a dry - processing forming analysis method program for special - shaped carbon fiber composite parts. When the dry - processing forming analysis method program for special - shaped carbon fiber composite parts is executed by the processor, the steps of any one of the dry - processing forming analysis methods for special - shaped fiber composite parts are implemented.

[0033] The third aspect of the present invention provides a computer - readable storage medium, including a dry - processing forming analysis method program for special - shaped carbon fiber composite parts. When the dry - processing forming analysis method program for special - shaped carbon fiber composite parts is executed by a processor, the steps of any one of the dry - processing forming analysis methods for special - shaped fiber composite parts are implemented.

[0034] The present invention solves the defects in the background technology and has the following beneficial effects:

[0035] The present invention obtains the process element information of the special-shaped parts made of carbon fiber composite materials, constructs the processing route of the special-shaped parts made of carbon fiber composite materials according to the process element information of the special-shaped parts made of carbon fiber composite materials, then sets the feed rate and the depth of cut, analyzes the processing route of the special-shaped parts made of carbon fiber composite materials according to the feed rate and the depth of cut, obtains the predicted cutting force information at each position of each processing sub-route, thereby obtains the historical energy consumption change characteristic data information of the processing machine tool under various processing condition data, constructs an energy consumption prediction model according to the historical energy consumption change characteristic data information of the processing machine tool under various processing condition data, finally predicts the energy consumption information of each processing sub-route based on the energy consumption prediction model and the predicted cutting force information at each position of each processing sub-route, and optimizes the dry processing and forming of the special-shaped parts made of carbon fiber composite materials according to the energy consumption information of each processing sub-route to obtain the final processing route. By analyzing the energy consumption of the processing route of the special-shaped parts made of carbon fiber composite materials, the present invention can obtain a better processing route according to the energy consumption, reduce the processing energy consumption of the special-shaped parts made of carbon fiber composite materials, and further reduce the processing cost of the special-shaped parts made of carbon fiber composite materials. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained according to these drawings.

[0037] Figure 1 Shows the overall flowchart of the dry processing and forming analysis method for special-shaped parts made of carbon fiber composite materials;

[0038] Figure 2 Shows a partial flowchart of the dry processing and forming analysis method for special-shaped parts made of carbon fiber composite materials;

[0039] Figure 3 Shows the system block diagram of the dry processing and forming analysis system for special-shaped parts made of carbon fiber composite materials. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] In order to more clearly understand the above objects, features and advantages of the present invention, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the scope of protection of the present invention is not limited by the specific embodiments disclosed below.

[0042] As Figure 1 shown, a dry processing and forming analysis method for special-shaped parts of carbon fiber composite materials is provided in the first aspect of the present invention, specifically including:

[0043] S102: Obtain the process element information of the special-shaped parts of carbon fiber composite materials, and construct the processing route of the special-shaped parts of carbon fiber composite materials according to the process element information of the special-shaped parts of carbon fiber composite materials;

[0044] Exemplarily, the process element information includes data such as hole position dimensions, roughness, profile, slot dimensions, etc. The processing route can be constructed by technical means such as mastercam, UG, genetic algorithm for planning the processing route.

[0045] S104: Set the feed rate and depth of cut, and analyze the processing route of the special-shaped parts of carbon fiber composite materials according to the feed rate and depth of cut to obtain the predicted cutting force information at each position of each processing sub-route;

[0046] S106: Obtain the historical energy consumption change characteristic data information of the processing machine tool under various processing condition data, and construct an energy consumption prediction model according to the historical energy consumption change characteristic data information of the processing machine tool under various processing condition data;

[0047] S108: Predict the energy consumption information of each processing sub-route based on the energy consumption prediction model and the predicted cutting force information at each position of each processing sub-route, and optimize the dry processing and forming of the special-shaped parts of carbon fiber composite materials according to the energy consumption information of each processing sub-route to obtain the final processing route.

[0048] It should be noted that the present invention analyzes the energy consumption situation of the processing route of the special-shaped parts of carbon fiber composite materials, so as to be able to obtain a better processing route according to the energy consumption situation, reduce the processing energy consumption of the special-shaped parts of carbon fiber composite materials, and further reduce the processing cost of the special-shaped parts of carbon fiber composite materials.

[0049] As Figure 2 shown, in S102 of this method, the path planning is mainly realized by the genetic algorithm, specifically as follows:

[0050] S202: Obtain the processing drawing information of the special-shaped parts of carbon fiber composite materials, and extract the processing elements from the processing drawing information of the special-shaped parts of carbon fiber composite materials to obtain the process element information of the special-shaped parts of carbon fiber composite materials;

[0051] S204: Introduce a genetic algorithm, set the number of generations based on the genetic algorithm, perform an initial planning of the processing route according to the process element information of the carbon fiber composite material special-shaped part, and use the initial route information of the carbon fiber composite material special-shaped part as the population;

[0052] S206: Perform genetic operations on the population until the maximum number of generations is reached, then obtain the initial route information of the carbon fiber composite material special-shaped part, and obtain the processing distance information of each initial route information;

[0053] S208: Set a processing distance threshold. When the processing distance information is less than the initial route information corresponding to the processing distance threshold, and use the initial route information with the processing distance information less than the processing distance threshold as the processing route of the carbon fiber composite material special-shaped part.

[0054] It should be noted that through this method, continuous genetic operations can be carried out according to the number of generations to obtain several processing routes with processing distance information less than the processing distance threshold, thereby improving the rationality of processing.

[0055] Furthermore, in this method, the feed rate and the depth of cut are set, and the processing route of the carbon fiber composite material special-shaped part is analyzed according to the feed rate and the depth of cut to obtain the estimated cutting force information at each position of each processing sub-route. Specifically:

[0056] Obtain the processing cutting force information of the carbon fiber composite material special-shaped part under various feed rates and depths of cut through big data, and introduce a graph neural network, and input the processing cutting force information of the carbon fiber composite material special-shaped part under various feed rates and depths of cut into the graph neural network;

[0057] Take the feed rate and the depth of cut as the first nodes, and take the processing cutting force information as the second nodes, construct a directed description relationship, connect the first nodes and the second nodes based on the directed description relationship, and construct a topological structure diagram;

[0058] Construct a knowledge graph, input the topological structure diagram into the knowledge graph for storage, and set the feed rate and the depth of cut, and input the feed rate and the depth of cut into the knowledge graph for data matching;

[0059] Through data matching, obtain the estimated cutting force information corresponding to the feed rate and the depth of cut, and divide the processing route of the carbon fiber composite material special-shaped part to obtain several sub-routes, and obtain the estimated cutting force information at each position of each processing sub-route.

[0060] It should be noted that under different feed rates and depths of cut, the real-time cutting force is inconsistent. For a machining route, the machining distance, feed rate, and depth of cut will all affect the final energy consumption value of the machining equipment. Through this method, the predicted cutting force information at each position of each machining sub-route can be estimated, so as to calculate the energy consumption of each sub-route.

[0061] Furthermore, in this method, the historical energy consumption change characteristic data information of the machining tool under various machining condition data is obtained, and an energy consumption prediction model is constructed based on the historical energy consumption change characteristic data information of the machining tool under various machining condition data, specifically including:

[0062] Obtain the historical energy consumption change characteristic data information of the machining tool under various machining condition data, construct an energy consumption prediction model based on a deep neural network, and construct a feature matrix according to the historical energy consumption change characteristic data information of the machining tool under various machining condition data;

[0063] Input the feature matrix into the energy consumption prediction model for training. When the loss function of the energy consumption prediction model converges to a preset value, save the model parameters of the energy consumption prediction model and output the energy consumption prediction model.

[0064] It should be noted that after the machining equipment has been used for a certain number of years, there will be a certain degree of degradation, resulting in inconsistent energy consumption of the machining equipment under different machining condition data. The machining condition data includes machining distance information, machining cutting force information, feed rate, depth of cut and other data.

[0065] Furthermore, in this method, based on the energy consumption prediction model and the predicted cutting force information at each position of each machining sub-route, the energy consumption information of each machining sub-route is predicted, specifically as follows:

[0066] Obtain the energy consumption change characteristic data information of the machining tool within a preset time, and obtain the machining distance information of each machining sub-route;

[0067] Input the machining distance information of each machining sub-route, the predicted cutting force information at each position of each machining sub-route, and the energy consumption change characteristic data information of the machining tool within a preset time into the energy consumption prediction model for prediction;

[0068] Through prediction, obtain the energy consumption information of each machining sub-route, and output the energy consumption information of each machining sub-route as the output result.

[0069] It should be noted that through this method, the energy consumption information of each machining sub-route can be obtained.

[0070] Further, in this method, the dry processing and forming of carbon fiber composite material special-shaped parts is optimized according to the energy consumption information of each processing sub-route to obtain the final processing route, specifically as follows:

[0071] According to the energy consumption information of each processing sub-route, the total energy consumption information of the processing route of each carbon fiber composite material special-shaped part is counted, and a total energy consumption threshold is set to determine whether there is a processing route of a carbon fiber composite material special-shaped part with a total energy consumption information greater than the total energy consumption threshold;

[0072] When there is a processing route of a carbon fiber composite material special-shaped part with a total energy consumption information greater than the total energy consumption threshold, the processing route of the carbon fiber composite material special-shaped part with a total energy consumption information greater than the total energy consumption threshold is used as the final processing route;

[0073] When there is no processing route of a carbon fiber composite material special-shaped part with a total energy consumption information greater than the total energy consumption threshold, the feed rate and the depth of cut are re-planned, and the total energy consumption information of the processing route of each carbon fiber composite material special-shaped part under the feed rate and the depth of cut is obtained;

[0074] When the total energy consumption information is not greater than the total energy consumption threshold, the processing route of the carbon fiber composite material special-shaped part with the total energy consumption information not greater than the total energy consumption threshold is obtained, and the processing routes of the carbon fiber composite material special-shaped parts with the total energy consumption information not greater than the total energy consumption threshold are sorted, and the processing route with the minimum total energy consumption is obtained as the final processing route.

[0075] It should be noted that through this method, the rationality of processing can be further improved, so as to select a more excellent processing route to process carbon fiber composite material special-shaped parts and reduce the processing cost.

[0076] In addition, this method also includes:

[0077] Predict the energy consumption data of each carbon fiber composite material special-shaped part on each processing equipment through the energy consumption prediction model, and randomly select a processing equipment for each carbon fiber composite material special-shaped part according to the energy consumption data of each carbon fiber composite material special-shaped part on each processing equipment to process, and construct a processing task allocation combination;

[0078] Obtain the processing energy consumption value of the randomly selected processing equipment in processing the carbon fiber composite material special-shaped part, and count the processing energy consumption value of the randomly selected processing equipment in processing the carbon fiber composite material special-shaped part to obtain the second total energy consumption value;

[0079] Set a second total energy consumption threshold, and judge whether the second total energy consumption value is greater than the second total energy consumption threshold. When the second total energy consumption value is not greater than the second total energy consumption threshold, output the processing task allocation combination;

[0080] When the second total energy consumption value is greater than the second total energy consumption threshold, update the processing task allocation combination, reselect processing equipment for processing until the second total energy consumption value is not greater than the second total energy consumption threshold, and output the processing task allocation combination.

[0081] It should be noted that through this method, the rationality of processing allocation can be improved, thereby further optimizing processing energy consumption and further reducing processing costs.

[0082] In addition, this method further includes:

[0083] Obtain the energy consumption data information of the sub-components of the processing equipment under the working temperature environment characteristics of each sub-component of the processing equipment through big data, and input the energy consumption data information of the sub-components of the processing equipment under the working temperature environment characteristics of each sub-component of the processing equipment into the knowledge graph for storage;

[0084] By setting temperature sensors on the sub-components of the processing equipment, obtain the temperature information of the sub-components of the processing equipment through the temperature sensors, and input the temperature information of the sub-components of the processing equipment into the knowledge graph for data matching;

[0085] Through data matching, obtain the energy consumption data information of the sub-components of the processing equipment under the current working temperature environment characteristics, and update the real-time energy consumption data according to the energy consumption data information of the sub-components of the processing equipment under the current working temperature environment characteristics;

[0086] Update the processing task allocation combination based on the updated real-time energy consumption data, and perform processing allocation according to the updated processing task allocation combination.

[0087] It should be noted that since the energy consumption required for the sub-components (such as motors) of the processing equipment to reach the same working parameters (rotational speed) in different working environments is inconsistent, updating the real-time energy consumption data through this method can further improve the rationality of the dry processing of special-shaped parts of carbon fiber composite materials.

[0088] As Figure 3 shown, the second aspect of the present invention provides a dry processing and forming analysis system 4 for special-shaped parts of carbon fiber composite materials, including a memory 41 and a processor 42. The memory 41 includes a program for the dry processing and forming analysis method of special-shaped parts of carbon fiber composite materials. When the program for the dry processing and forming analysis method of special-shaped parts of carbon fiber composite materials is executed by the processor 42, the steps of the dry processing and forming analysis method of fiber composite material special-shaped parts in any one of the above are realized, where, including:

[0089] Obtain the process element information of the carbon fiber composite special-shaped part, and construct the processing route of the carbon fiber composite special-shaped part according to the process element information of the carbon fiber composite special-shaped part;

[0090] Set the feed rate and the depth of cut, and analyze the processing route of the carbon fiber composite special-shaped part according to the feed rate and the depth of cut to obtain the estimated cutting force information at each position of each processing sub-route;

[0091] Obtain the historical energy consumption change characteristic data information of the processing machine tool under various processing condition data, and construct an energy consumption prediction model according to the historical energy consumption change characteristic data information of the processing machine tool under various processing condition data;

[0092] Predict the energy consumption information of each processing sub-route based on the energy consumption prediction model and the estimated cutting force information at each position of each processing sub-route, and optimize the dry processing and forming of the carbon fiber composite special-shaped part according to the energy consumption information of each processing sub-route to obtain the final processing route.

[0093] Furthermore, in this system, obtain the process element information of the carbon fiber composite special-shaped part, and construct the processing route of the carbon fiber composite special-shaped part according to the process element information of the carbon fiber composite special-shaped part. Specifically:

[0094] Obtain the processing drawing information of the carbon fiber composite special-shaped part, and extract the processing elements from the processing drawing information of the carbon fiber composite special-shaped part to obtain the process element information of the carbon fiber composite special-shaped part;

[0095] Introduce the genetic algorithm, set the number of genetic generations based on the genetic algorithm, initialize the planning of the processing route according to the process element information of the carbon fiber composite special-shaped part, and use the initial route information of the carbon fiber composite special-shaped part as the population;

[0096] Perform genetic operations on the population until the maximum number of genetic generations is reached, then obtain the initial route information of the carbon fiber composite special-shaped part, and obtain the processing distance information of each initial route information;

[0097] Set the processing distance threshold. When the processing distance information is less than the initial route information corresponding to the processing distance threshold, and use the initial route information with the processing distance information less than the processing distance threshold as the processing route of the carbon fiber composite special-shaped part.

[0098] Furthermore, in this system, set the feed rate and the depth of cut, and analyze the processing route of the carbon fiber composite special-shaped part according to the feed rate and the depth of cut to obtain the estimated cutting force information at each position of each processing sub-route. Specifically:

[0099] Obtain the machining cutting force information of the special-shaped parts of carbon fiber composite materials under various feed rates and depths of cut through big data, and introduce a graph neural network. Input the machining cutting force information of the special-shaped parts of carbon fiber composite materials under various feed rates and depths of cut into the graph neural network;

[0100] Take the feed rate and depth of cut as the first nodes, and the machining cutting force information as the second nodes, construct a directed description relationship, connect the first nodes and the second nodes based on the directed description relationship, and construct a topological structure diagram;

[0101] Construct a knowledge graph, input the topological structure diagram into the knowledge graph for storage, and set the feed rate and depth of cut. Input the feed rate and depth of cut into the knowledge graph for data matching;

[0102] Through data matching, obtain the estimated cutting force information corresponding to the feed rate and depth of cut, segment the machining route of the special-shaped parts of carbon fiber composite materials, obtain several sub-routes, and obtain the estimated cutting force information at each position of each machining sub-route.

[0103] Furthermore, in this system, obtain the historical energy consumption change characteristic data information of the machining tool under various machining condition data, and construct an energy consumption prediction model according to the historical energy consumption change characteristic data information of the machining tool under various machining condition data, specifically including:

[0104] Obtain the historical energy consumption change characteristic data information of the machining tool under various machining condition data, construct an energy consumption prediction model based on a deep neural network, and construct a feature matrix according to the historical energy consumption change characteristic data information of the machining tool under various machining condition data;

[0105] Input the feature matrix into the energy consumption prediction model for training. When the loss function of the energy consumption prediction model converges to a preset value, save the model parameters of the energy consumption prediction model and output the energy consumption prediction model.

[0106] Furthermore, in this system, predict the energy consumption information of each machining sub-route based on the energy consumption prediction model and the estimated cutting force information at each position of each machining sub-route, specifically:

[0107] Obtain the historical energy consumption change characteristic data information of the machining tool within a preset time, and obtain the machining distance information of each machining sub-route;

[0108] Input the machining distance information of each machining sub-route, the estimated cutting force information at each position of each machining sub-route, and the historical energy consumption change characteristic data information of the machining tool within a preset time into the energy consumption prediction model for prediction;

[0109] Through prediction, the energy consumption information of each processing sub-route is obtained, and the energy consumption information of each processing sub-route is output as the output result.

[0110] Furthermore, in this system, the dry processing and forming of the carbon fiber composite material special-shaped parts is optimized according to the energy consumption information of each processing sub-route to obtain the final processing route. Specifically:

[0111] According to the energy consumption information of each processing sub-route, the total energy consumption information of the processing route of each carbon fiber composite material special-shaped part is statistically calculated, and a total energy consumption threshold is set to determine whether there is a processing route of a carbon fiber composite material special-shaped part with a total energy consumption information greater than the total energy consumption threshold;

[0112] When there is a processing route of a carbon fiber composite material special-shaped part with a total energy consumption information greater than the total energy consumption threshold, the processing route of the carbon fiber composite material special-shaped part with a total energy consumption information greater than the total energy consumption threshold is used as the final processing route;

[0113] When there is no processing route of a carbon fiber composite material special-shaped part with a total energy consumption information greater than the total energy consumption threshold, the feed rate and the depth of cut are re-planned, and the total energy consumption information of the processing route of each carbon fiber composite material special-shaped part under the feed rate and the depth of cut is obtained;

[0114] When the total energy consumption information is not greater than the total energy consumption threshold, the processing route of the carbon fiber composite material special-shaped part with the total energy consumption information not greater than the total energy consumption threshold is obtained, and the processing routes of the carbon fiber composite material special-shaped parts with the total energy consumption information not greater than the total energy consumption threshold are sorted, and the processing route with the minimum total energy consumption is obtained as the final processing route.

[0115] The third aspect of the present invention provides a computer-readable storage medium, including a program for the dry processing and forming analysis method of carbon fiber composite material special-shaped parts. When the program for the dry processing and forming analysis method of carbon fiber composite material special-shaped parts is executed by a processor, the steps of the dry processing and forming analysis method of any fiber composite material special-shaped part are implemented.

[0116] In several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed with each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be electrical, mechanical, or other forms.

[0117] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units; they may be located in one place or distributed over multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0118] In addition, in each embodiment of the present invention, all the functional units may be integrated into one processing unit, or each unit may be a separate unit alone, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0119] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), magnetic disks or optical discs and other various media that can store program codes.

[0120] Alternatively, if the above-mentioned integrated units of the present invention are implemented in the form of software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the various embodiments of the present invention. And the foregoing storage medium includes: removable storage devices, ROM, RAM, magnetic disks or optical discs and other various media that can store program codes.

[0121] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A dry processing and forming analysis method for special-shaped parts made of carbon fiber composite materials, characterized in that, Specifically, it includes: Obtain the process element information of the carbon fiber composite special-shaped part, and construct the processing route of the carbon fiber composite special-shaped part according to the process element information of the carbon fiber composite special-shaped part; Set the feed rate and the depth of cut, and analyze the processing route of the carbon fiber composite special-shaped part according to the feed rate and the depth of cut to obtain the estimated cutting force information at each position of each processing sub-route; Obtain the historical energy consumption change characteristic data information of the processing machine tool under various processing condition data, and construct an energy consumption prediction model according to the historical energy consumption change characteristic data information of the processing machine tool under various processing condition data; Predict the energy consumption information of each processing sub-route based on the energy consumption prediction model and the estimated cutting force information at each position of each processing sub-route, and optimize the dry processing and forming of the carbon fiber composite special-shaped part according to the energy consumption information of each processing sub-route to obtain the final processing route; Set the feed rate and the depth of cut, and analyze the processing route of the carbon fiber composite special-shaped part according to the feed rate and the depth of cut to obtain the estimated cutting force information at each position of each processing sub-route. Specifically: Obtain the processing cutting force information of the carbon fiber composite special-shaped part under various feed rates and depths of cut through big data, and introduce a graph neural network, and input the processing cutting force information of the carbon fiber composite special-shaped part under various feed rates and depths of cut into the graph neural network; Use the feed rate and the depth of cut as the first node, and the processing cutting force information as the second node to construct a directed description relationship, and connect the first node and the second node based on the directed description relationship to construct a topological structure diagram; Construct a knowledge graph, input the topological structure diagram into the knowledge graph for storage, and set the feed rate and the depth of cut, and input the feed rate and the depth of cut into the knowledge graph for data matching; Through data matching, obtain the estimated cutting force information corresponding to the feed rate and the depth of cut, divide the processing route of the carbon fiber composite special-shaped part to obtain several sub-routes, and obtain the estimated cutting force information at each position of each processing sub-route.

2. The dry processing and forming analysis method for a special-shaped part made of carbon fiber composite material according to claim 1, wherein Obtain the process element information of the carbon fiber composite special-shaped part, and construct the processing route of the carbon fiber composite special-shaped part according to the process element information of the carbon fiber composite special-shaped part. Specifically: Obtain the processing drawing information of the carbon fiber composite special-shaped part, and extract the processing element information of the carbon fiber composite special-shaped part by extracting the processing elements from the processing drawing information of the carbon fiber composite special-shaped part; Introduce a genetic algorithm, set the number of genetic generations based on the genetic algorithm, and perform initial route planning for the processing according to the process element information of the carbon fiber composite special-shaped part, and use the initial route information of the carbon fiber composite special-shaped part as the population; Perform genetic operations on the population until the maximum number of genetic generations is reached, then obtain the initial route information of the carbon fiber composite special-shaped part, and obtain the processing distance information of each initial route information. Set a machining path threshold. When the machining path information is less than the machining path threshold, the initial path information corresponding to the machining path information being less than the machining path threshold is used as the machining path of the carbon fiber composite material special-shaped part.

3. A dry processing and forming analysis method for a special-shaped part made of carbon fiber composite material according to claim 1, characterized in that, Obtain the historical energy consumption change characteristic data information of the machining tool under various machining condition data, and construct an energy consumption prediction model according to the historical energy consumption change characteristic data information of the machining tool under various machining condition data, specifically including: Obtain the historical energy consumption change characteristic data information of the machining tool under various machining condition data, construct an energy consumption prediction model based on a deep neural network, and construct a feature matrix according to the historical energy consumption change characteristic data information of the machining tool under various machining condition data; Input the feature matrix into the energy consumption prediction model for training. When the loss function of the energy consumption prediction model converges to a preset value, save the model parameters of the energy consumption prediction model and output the energy consumption prediction model.

4. A dry processing and forming analysis method for special-shaped parts made of carbon fiber composite materials according to claim 1, characterized in that Predict the energy consumption information of each machining sub-path based on the energy consumption prediction model and the estimated cutting force information of each machining sub-path at each position, specifically: Obtain the energy consumption change characteristic data information of the machining tool within a preset time, and obtain the machining path information of each machining sub-path; Input the machining path information of each machining sub-path, the estimated cutting force information of each machining sub-path at each position, and the energy consumption change characteristic data information of the machining tool within a preset time into the energy consumption prediction model for prediction; Through prediction, obtain the energy consumption information of each machining sub-path, and output the energy consumption information of each machining sub-path as the output result.

5. A dry processing and forming analysis system for a special-shaped part made of carbon fiber composite material, characterized in that It includes a memory and a processor. The memory includes a dry processing and forming analysis method program for carbon fiber composite material special-shaped parts. When the dry processing and forming analysis method program for carbon fiber composite material special-shaped parts is executed by the processor, the steps of the dry processing and forming analysis method for carbon fiber composite material special-shaped parts according to any one of claims 1-4 are implemented.

6. A computer-readable storage medium, characterized in that, It includes a dry processing and forming analysis method program for carbon fiber composite material special-shaped parts. When the dry processing and forming analysis method program for carbon fiber composite material special-shaped parts is executed by the processor, the steps of the dry processing and forming analysis method for carbon fiber composite material special-shaped parts according to any one of claims 1-4 are implemented.

Images