Electromechanical product design method and system based on deep learning of parts database

Abstract

The present invention relates to the field of electromechanical product design, and proposes a method and system for electromechanical product design based on deep learning of a parts database, comprising the following steps: establishing a parts database; establishing an initial hybrid design optimization model, training and testing the initial hybrid design optimization model through the parts database to obtain a hybrid design optimization model; obtaining electromechanical product design requirements, obtaining multiple required parts model index data corresponding to the electromechanical product design requirements based on an analysis model of the electromechanical product design requirements and parts assembly requirements, inputting the multiple required parts model index data into the hybrid design optimization model, and outputting multiple electromechanical product 3D models corresponding to the electromechanical product design requirements; and producing corresponding electromechanical products based on the electromechanical product 3D models. By establishing a parts database containing part model index data, part assembly plans, and electromechanical product 3D models, and using a deep learning network to establish a hybrid design optimization model, it is possible to automatically generate electromechanical product 3D models that meet the electromechanical product design requirements given by the user, thereby realizing intelligent design of electromechanical products based on product full life cycle data.

Description

Technical Field

[0001] The present invention relates to the field of electromechanical product design, and in particular to an electromechanical product design method and system based on deep learning of a parts database. Background Art

[0002] Electromechanical products, such as construction machinery and high-speed trains, are essential infrastructure for national economic development. Their adaptability and intelligence have always been a key concern and challenge for the manufacturing industry. The rapid development of technologies such as the Internet of Things, CPS, big data, and artificial intelligence has made it possible to collect, analyze, and mine increasing amounts of data related to the product lifecycle and provide it to designers. This vast amount of data generated during a product's service life contains more accurate and objective information. The key challenge currently facing researchers is how to effectively leverage product usage and operational data to achieve breakthroughs in research and application within the field of electromechanical product design.

[0003] Existing technologies use digital twins to establish a perceptual connection between physical entities and virtual models, enabling feedback control and closed-loop iterative optimization through the fusion of multi-source heterogeneous cyber-physical data. This allows for the integration of operating data and information feedback for electromechanical products, enabling closed-loop design. However, digital twin models often struggle to capture performance variations throughout a product's lifecycle, particularly the evolution of faults over time. They can only simulate designs based on existing measured data, lacking the ability to explore the relationships between product design and performance, and therefore lack predictive capabilities for design. Summary of the Invention

[0004] In view of this, the present invention proposes a mechatronic product design method and system based on deep learning of component database. By establishing a component database containing component model index data, component assembly scheme and mechatronic product 3D model, and using deep learning network to establish a hybrid design optimization model, it can automatically generate a mechatronic product 3D model that meets the requirements according to the mechatronic product design requirements given by the user. It solves the problem in the existing technology that the digital twin model is difficult to capture the performance change law of the product throughout its life cycle and cannot effectively use the product usage and operation data to provide support for the design, and realizes the intelligent design of mechatronic products based on the product's full life cycle data.

[0005] The technical solution of the present invention is implemented as follows: On the one hand, the present invention provides a method for designing electromechanical products based on deep learning of a parts database, comprising the following steps:

[0006] S1, establishing a parts database, wherein the parts database includes a plurality of parts model index data, a plurality of parts assembly schemes, and a plurality of electromechanical product 3D models. When the types and quantities of parts are determined, all combinations of the part model index data and any part assembly scheme correspond to a unique electromechanical product 3D model;

[0007] S2, establishing an initial hybrid design optimization model, training and testing the initial hybrid design optimization model through the parts database to obtain a hybrid design optimization model;

[0008] S3, obtaining electromechanical product design requirements, obtaining multiple required component model index data corresponding to the electromechanical product design requirements based on the electromechanical product design requirements and the component assembly requirement analysis model, inputting the multiple required component model index data into the hybrid design optimization model, and outputting multiple electromechanical product 3D models corresponding to the electromechanical product design requirements;

[0009] S4, producing a corresponding electromechanical product according to the electromechanical product 3D model.

[0010] Based on the above technical solution, preferably, step S2 includes:

[0011] The initial hybrid design optimization model includes an input layer, a first sub-network layer, a second sub-network layer, a feature fusion layer, an assembly optimization layer, and an output layer. The input layer is connected to the first sub-network layer and the second sub-network layer respectively. The first sub-network layer and the second sub-network layer are connected to the feature fusion layer respectively. The feature fusion layer is connected to the assembly optimization layer. The assembly optimization layer is connected to the output layer.

[0012] The input layer is used to receive input features; the first sub-network layer is used to extract the timing performance change features of components; the second sub-network layer is used to extract the structural features of components; the feature fusion layer is used to fuse the timing performance change features of components and the structural features of components to obtain fused features; the assembly optimization layer is used to apply multiple component assembly schemes to the fused features to obtain multiple corresponding 3D models of electromechanical products.

[0013] Based on the above technical solution, preferably, the first sub-network layer adopts a long short-term memory network, the first sub-network layer includes an input gate, a forget gate, a unit state, an output gate and a hidden state, and the characteristic function expression of the component timing performance change of the first sub-network layer is:

[0014] m r =Sigmoid(α1·[h t-1 ,x t ]+τ1);

[0015] m y =Sigmoid(α2·[h t-1 ,x t ]+τ2);

[0016] d t =m y ⊙dt-1 +m r ⊙tanh(α3[h t-1 ,x t ]+τ3);

[0017] m c =Sigmoid(α4·[h t-1 ,x t ]+τ4);

[0018] h t =m c ⊙tanh(d t );

[0019]

[0020] F s =h n ;

[0021] Among them, m r 、m y 、m c are the outputs of the input gate, forget gate, and output gate respectively, d t is the cell state at the current time step, h t is the hidden state of the current time step, h t-1 is the hidden state of the previous time step, h n is the hidden state of the last time step, x t is the input of the current time step, α1, α2, α3, α4 are the weight matrices of the input gate, forget gate, unit state update and output gate respectively, τ1, τ2, τ3, τ4 are the bias vectors of the input gate, forget gate, unit state update and output gate respectively, T s is the input feature of the input layer, ⊙ is the Hadamard product, x n is the input of the last time step, F s is the output of the first sub-network layer.

[0022] On the basis of the above technical solution, preferably, the second sub-network layer adopts a convolutional neural network, including a convolutional layer, a sigmoid activation layer and a pooling layer, and the component structure characteristic function expression of the second sub-network layer is:

[0023]

[0024] Among them, F j is the output of the second sub-network layer, Pooling(·) is the pooling function, exp(·) is the exponential function, T s is the input feature of the input layer, ω1 is the weight matrix of the component structure feature, and δ1 is the bias vector of the component structure feature.

[0025] On the basis of the above technical solution, preferably, the feature fusion function expression of the feature fusion layer is:

[0026] F r =λF s +(1-λ)F j ;

[0027] Among them, F r is the fusion feature, λ is the fusion weight of the first sub-network layer, F s is the output of the first sub-network layer, F j is the output of the second sub-network layer.

[0028] On the basis of the above technical solution, preferably, the assembly optimization layer includes a fully connected layer, and the function expression of the output of the assembly optimization layer is:

[0029]

[0030] in, A collection of 3D models of electromechanical products, ys (·) is the assembly optimization feature parameter mapping function, F r It is a fusion feature;

[0031] f ys (·) is | F r | and the mapping function of the 3D model of the electromechanical product, where |F r |Corresponding to multiple 3D models of electromechanical products.

[0032] Based on the above technical solution, preferably, step S3 includes:

[0033] The component assembly demand analysis model includes a demand input layer, a demand analysis layer, a component model layer and a component data output layer, wherein the demand input layer is connected to the demand analysis layer, the demand analysis layer is connected to the component model layer, and the component model layer is connected to the component data output layer;

[0034] The demand input layer is used to input the design requirements of electromechanical products, wherein the design requirements of electromechanical products include the size, functional requirements and service life of the electromechanical products;

[0035] The demand analysis layer is used to determine the types of components required for the electromechanical product design based on the electromechanical product design requirements;

[0036] The component model layer is used to search for multiple required component model index data of the required components from the component database according to the required component type;

[0037] The component data output layer connection is used to output the plurality of required component model indicator data.

[0038] Based on the above technical solution, preferably, step S1 includes:

[0039] The component model index data includes component geometric properties, component material properties, and component timing performance change properties, and the component assembly plan includes assembly sequence and combination method.

[0040] Based on the above technical solution, preferably, step S4 includes:

[0041] Based on the 3D model of the electromechanical product, the corresponding rapid manufacturing technology is selected, a manufacturing process flow is formulated, the corresponding electromechanical product is produced, and appearance inspection and performance testing are carried out. The rapid manufacturing technology includes 3D printing, CNC machining, numerical control machining and injection molding.

[0042] On the other hand, the present invention also provides a mechatronic product design system based on deep learning of a parts database, the system comprising:

[0043] a database establishment module for establishing a parts database, wherein the parts database includes a plurality of parts model index data, a plurality of parts assembly schemes, and a plurality of electromechanical product 3D models. When the types and quantities of parts are determined, all combinations of the parts model index data and any parts assembly scheme correspond to a unique electromechanical product 3D model;

[0044] A hybrid design optimization module is used to establish an initial hybrid design optimization model, train and test the initial hybrid design optimization model through a parts database, and obtain a hybrid design optimization model;

[0045] A product demand analysis module is configured to obtain electromechanical product design requirements, obtain multiple required component model indicator data corresponding to the electromechanical product design requirements based on the electromechanical product design requirements and the component assembly requirement analysis model, input the multiple required component model indicator data into the hybrid design optimization model, and output multiple electromechanical product 3D models corresponding to the electromechanical product design requirements;

[0046] The electromechanical product generation module is used to produce the corresponding electromechanical product according to the electromechanical product 3D model.

[0047] The electromechanical product design method and system based on deep learning of component database of the present invention have the following beneficial effects compared with the prior art:

[0048] (1) By establishing a component database containing component model index data, component assembly schemes, and 3D models of electromechanical products, and using a deep learning network to establish a hybrid design optimization model, it is possible to automatically generate a 3D model of an electromechanical product that meets the user's electromechanical product design requirements, thus realizing intelligent design of electromechanical products based on product life cycle data;

[0049] (2) The long short-term memory network is used in the first sub-network layer to effectively process the temporal performance variation characteristics of the components, extract key information and update the hidden state, and obtain the temporal performance variation characteristics of the components to reflect the performance variation trend of the components over time;

[0050] (3) Using a convolutional neural network in the second sub-network layer, the structural features of the parts are effectively processed, key information is extracted, and structural features are summarized to obtain the structural features of the parts, which are used to reflect the static characteristics of the parts;

[0051] (4) By assembling and optimizing the feature parameter mapping function, the fused features are mapped to a set of 3D models of electromechanical products, thereby realizing the output of multiple 3D models of electromechanical products. During the design process, multiple possible product design schemes can be obtained, providing designers with more choices. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 This is a flow chart of the electromechanical product design method based on deep learning of parts database of the present invention;

[0054] Figure 2 This is a structural diagram of the electromechanical product design system based on deep learning of parts database of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] See also Figure 1The present invention provides a method for designing electromechanical products based on deep learning of a parts database, comprising the following steps:

[0057] S1, establishing a parts database, wherein the parts database includes a plurality of parts model index data, a plurality of parts assembly schemes, and a plurality of electromechanical product 3D models. When the types and quantities of parts are determined, all combinations of the part model index data and any part assembly scheme correspond to a unique electromechanical product 3D model;

[0058] S2, establishing an initial hybrid design optimization model, training and testing the initial hybrid design optimization model through the parts database to obtain a hybrid design optimization model;

[0059] S3, obtaining electromechanical product design requirements, obtaining multiple required component model index data corresponding to the electromechanical product design requirements based on the electromechanical product design requirements and the component assembly requirement analysis model, inputting the multiple required component model index data into the hybrid design optimization model, and outputting multiple electromechanical product 3D models corresponding to the electromechanical product design requirements;

[0060] S4, producing a corresponding electromechanical product according to the electromechanical product 3D model.

[0061] Specifically, this embodiment provides a method for designing electromechanical products based on deep learning of a parts database, which establishes a comprehensive parts database containing part model index data, assembly plans, and 3D models of electromechanical products, thereby realizing systematic management of part information.

[0062] A hybrid design optimization model was established through deep learning, which can automatically generate 3D models of electromechanical products that meet product design requirements.

[0063] By utilizing component data throughout the product life cycle, data-driven intelligent design is achieved, solving the problem of difficulty in capturing the patterns of product performance changes in existing technologies.

[0064] The automation of the entire process from design requirements to physical model production improves the efficiency and pertinence of electromechanical product design.

[0065] In this embodiment, a method for designing electromechanical products based on deep learning of a parts database is implemented to realize intelligent design of electromechanical products based on data of the entire product life cycle by establishing a parts database and a hybrid design optimization model, thereby improving design efficiency and targeting and solving the shortcomings of the existing technology.

[0066] Step S1 includes:

[0067] The component model index data includes component geometric properties, component material properties, and component timing performance change properties, and the component assembly plan includes assembly sequence and combination method.

[0068] Specifically, step S1 helps to optimize product design and improve product characteristics by comprehensively describing the geometric properties, material properties and timing performance change properties of parts, as well as the assembly sequence and combination methods, including the geometric properties, material properties and timing performance change properties of parts, which comprehensively reflect the physical properties and time-varying performance of parts. At the same time, it records the assembly information of parts, including the assembly sequence and combination method, and generates a unique corresponding 3D model of the electromechanical product, which not only has static geometric material properties, but also dynamic timing performance changes, covering the entire life cycle data of the electromechanical product.

[0069] The component geometric properties are used to describe the shape, size, geometric features, etc. of the component, including but not limited to length, width, height, curvature, angle, etc.;

[0070] The component material properties are used to describe the type of material used in the component, including but not limited to physical and chemical properties such as density, strength, hardness, thermal conductivity, as well as the manufacturing process and characteristics of the material.

[0071] The component timing performance change attribute is used to describe the performance characteristics of the component over time, including but not limited to the dynamic performance parameters such as the component's operating speed change, temperature change, pressure change, current change, etc.;

[0072] In a specific embodiment, the timing performance change attributes of the components include changes in the speed, current, and power of the motor over time, changes in the strength, hardness, and fracture toughness of metal components over time, changes in the hardness, elasticity, and wear resistance of rubber and plastic over time, and other dynamic performance parameters.

[0073] Step S2 includes:

[0074] The initial hybrid design optimization model includes an input layer, a first sub-network layer, a second sub-network layer, a feature fusion layer, an assembly optimization layer, and an output layer. The input layer is connected to the first sub-network layer and the second sub-network layer respectively. The first sub-network layer and the second sub-network layer are connected to the feature fusion layer respectively. The feature fusion layer is connected to the assembly optimization layer. The assembly optimization layer is connected to the output layer.

[0075] The input layer is used to receive input features; the first sub-network layer is used to extract the timing performance change features of components; the second sub-network layer is used to extract the structural features of components; the feature fusion layer is used to fuse the timing performance change features of components and the structural features of components to obtain fused features; the assembly optimization layer is used to apply multiple component assembly schemes to the fused features to obtain multiple corresponding 3D models of electromechanical products.

[0076] Specifically, step S2 extracts the timing performance variation characteristics and structural characteristics of the components through the first sub-network layer and the second sub-network layer respectively, thereby realizing multi-level feature extraction, which helps to describe the characteristics of the components more comprehensively.

[0077] The feature fusion layer fuses the temporal performance variation characteristics with the structural characteristics to generate a fused feature, which comprehensively considers the characteristics of different components and improves the overall performance of the model. The assembly optimization layer applies various component assembly schemes to the fused feature to generate multiple corresponding 3D models of electromechanical products. This hybrid design optimization model can automatically generate multiple corresponding 3D models of electromechanical products, improving design efficiency and accuracy.

[0078] In a specific embodiment, all generated electromechanical product 3D models can be ranked by performance, and a preset number of electromechanical product 3D models can be selected to optimize and compare different assembly schemes, thereby improving design flexibility and diversity.

[0079] Step S2 effectively extracts and fuses features through a hybrid design optimization model and applies multiple assembly schemes for optimization, thereby achieving comprehensive optimization and automatic generation of electromechanical product design and improving design accuracy and efficiency.

[0080] The first sub-network layer adopts a long short-term memory network. The first sub-network layer includes an input gate, a forget gate, a unit state, an output gate, and a hidden state. The characteristic function expression of the component timing performance change of the first sub-network layer is:

[0081] m r =Sigmoid(α1·[h t-1 ,x t ]+τ1);

[0082] m y =Sigmoid(α2·[h t-1 ,x t ]+τ2);

[0083] d t =m y ⊙d t-1 +m r ⊙tanh(α3[ht-1 ,x t ]+τ3);

[0084] m c =Sigmoid(α4·[h t-1 ,x t ]+τ4);

[0085] h t =m c ⊙tanh(d t );

[0086]

[0087] F s =h n ;

[0088] Among them, m r 、m y 、m c are the outputs of the input gate, forget gate, and output gate respectively, d t is the cell state at the current time step, h t is the hidden state of the current time step, h t-1 is the hidden state of the previous time step, h n is the hidden state of the last time step, x t is the input of the current time step, α1, α2, α3, α4 are the weight matrices of the input gate, forget gate, unit state update and output gate respectively, τ1, τ2, τ3, τ4 are the bias vectors of the input gate, forget gate, unit state update and output gate respectively, T s is the input feature of the input layer, ⊙ is the Hadamard product, x n is the input of the last time step, F s is the output of the first sub-network layer.

[0089] Specifically, the first sub-network layer adopts a long short-term memory network, which is suitable for processing sequence data, can effectively capture the timing performance change characteristics of components, and helps to model and analyze the dynamic characteristics of components.

[0090] The first sub-network layer includes structures such as input gate, forget gate, unit state, output gate and hidden state, which can effectively control the input, forgetting and output of information, and realize the accurate extraction and processing of the timing performance change characteristics of components.

[0091] By updating the unit state and hidden state, the key features of the component timing performance changes can be effectively captured, providing important information support for subsequent feature fusion and assembly optimization.

[0092] The first sub-network layer of this embodiment adopts a long short-term memory network, which can effectively process the timing performance change characteristics of components, extract key information and update hidden states, and obtain the timing performance change characteristics of components, which are used to reflect the performance change trend of components over time.

[0093] The second sub-network layer adopts a convolutional neural network, including a convolution layer, a sigmoid activation layer and a pooling layer. The component structure characteristic function expression of the second sub-network layer is:

[0094]

[0095] Among them, F j is the output of the second sub-network layer, Pooling(·) is the pooling function, exp(·) is the exponential function, T s is the input feature of the input layer, ω1 is the weight matrix of the component structure feature, and δ1 is the bias vector of the component structure feature.

[0096] Specifically, the second sub-network layer adopts a convolutional neural network, which is suitable for processing images and structured data, can effectively extract the structural features of parts, and helps to model and analyze the static characteristics of parts.

[0097] The second sub-network layer includes a convolutional layer, a sigmoid activation layer and a pooling layer. The convolutional layer is used to extract features, the activation layer is used to introduce nonlinearity, and the pooling layer is used to reduce dimensionality and retain key information, thereby achieving effective processing and extraction of component structural features.

[0098] Through operations such as convolution, activation, and pooling, the structural features of parts can be effectively captured, providing important information support for subsequent feature fusion and assembly optimization.

[0099] The second sub-network layer of this embodiment adopts a convolutional neural network, which can process the structural features of parts, extract key information and summarize the structural features to obtain the structural features of parts, which are used to reflect the static characteristics of parts.

[0100] The feature fusion function expression of the feature fusion layer is:

[0101] F r =λF s +(1-λ)F j ;

[0102] Among them, F r is the fusion feature, λ is the fusion weight of the first sub-network layer, F s is the output of the first sub-network layer, F j is the output of the second sub-network layer.

[0103] Specifically, the feature fusion layer of this embodiment fuses the outputs of the first and second sub-network layers using a feature fusion function to generate fused features. This fusion comprehensively considers the features extracted by different sub-networks, making the final feature representation more comprehensive and rich. The design of the feature fusion layer effectively fuses the features extracted by different sub-networks, improving feature expression capabilities.

[0104] The assembly optimization layer includes a fully connected layer, and the function expression of the output of the assembly optimization layer is:

[0105]

[0106] in, A collection of 3D models of electromechanical products, ys (·) is the assembly optimization feature parameter mapping function, F r It is a fusion feature;

[0107] f ys (·) is | F r | and the mapping function of the 3D model of the electromechanical product, where |F r |Corresponding to multiple 3D models of electromechanical products.

[0108] Specifically, the assembly optimization layer of this embodiment maps fused features to a collection of 3D mechatronic product models by assembling optimized feature parameter mapping functions, thereby outputting multiple 3D mechatronic product models. This allows designers to obtain multiple possible product design options during the design process, providing them with more options.

[0109] Step S3 includes:

[0110] The component assembly demand analysis model includes a demand input layer, a demand analysis layer, a component model layer and a component data output layer, wherein the demand input layer is connected to the demand analysis layer, the demand analysis layer is connected to the component model layer, and the component model layer is connected to the component data output layer;

[0111] The demand input layer is used to input the design requirements of electromechanical products, wherein the design requirements of electromechanical products include the size, functional requirements and service life of the electromechanical products;

[0112] The demand analysis layer is used to determine the types of components required for the electromechanical product design based on the electromechanical product design requirements;

[0113] The component model layer is used to search for multiple required component model index data of the required components from the component database according to the required component type;

[0114] The component data output layer connection is used to output the plurality of required component model indicator data.

[0115] Specifically, step S3 of this embodiment converts the user's electromechanical product design requirements into specific required component model indicator data through the component assembly requirement analysis model.

[0116] The component assembly demand analysis model includes a demand input layer, a demand analysis layer, a component model layer and a component data output layer.

[0117] The demand input layer is used to receive the design requirements of electromechanical products, including information on dimensions, functional requirements, and service life, to ensure that all necessary design parameters are taken into account during the design process.

[0118] The demand analysis layer can accurately determine the required component types based on the input electromechanical product design requirements, and is used to clarify the types and functions of the required components during the design process.

[0119] The component model layer searches for multiple required component model indicator data of the required components from the component database based on the determined component type, so as to help designers obtain detailed information and characteristics of the required components.

[0120] The component data output layer is used to output the index data of the multiple required component models found. This data is used to input the hybrid design optimization model trained in the previous step to help generate multiple 3D models of electromechanical products that meet the design requirements.

[0121] Step S4 includes:

[0122] Based on the 3D model of the electromechanical product, the corresponding rapid manufacturing technology is selected, a manufacturing process flow is formulated, the corresponding electromechanical product is produced, and appearance inspection and performance testing are carried out. The rapid manufacturing technology includes 3D printing, CNC machining, numerical control machining and injection molding.

[0123] Specifically, step S4 of this embodiment selects applicable rapid manufacturing technology, including 3D printing, CNC machining, numerical control machining, and injection molding, based on the 3D model of the electromechanical product. According to the designed 3D model of the electromechanical product, the most suitable manufacturing technology can be selected to realize the production of the product.

[0124] After selecting the rapid manufacturing technology, the corresponding manufacturing process will be formulated, including determining the production steps, process parameters, material selection, etc., to ensure that the product can be produced according to the design requirements.

[0125] According to the established manufacturing process, the production line will begin to manufacture the corresponding electromechanical products. Through rapid manufacturing technology, the designed 3D model can be converted into an actual product prototype.

[0126] Finally, the electromechanical products produced are subjected to appearance inspection and performance testing to ensure that the appearance quality of the products meets the requirements and that the performance indicators reach the design standards, thereby verifying the quality and reliability of the products.

[0127] See also Figure 2 Based on the above method embodiments, the present invention further provides a mechatronic product design system based on deep learning of a parts database, the system comprising:

[0128] a database establishment module for establishing a parts database, wherein the parts database includes a plurality of parts model index data, a plurality of parts assembly schemes, and a plurality of electromechanical product 3D models. When the types and quantities of parts are determined, all combinations of the parts model index data and any parts assembly scheme correspond to a unique electromechanical product 3D model;

[0129] A hybrid design optimization module is used to establish an initial hybrid design optimization model, train and test the initial hybrid design optimization model through a parts database, and obtain a hybrid design optimization model;

[0130] A product demand analysis module is configured to obtain electromechanical product design requirements, obtain multiple required component model indicator data corresponding to the electromechanical product design requirements based on the electromechanical product design requirements and the component assembly requirement analysis model, input the multiple required component model indicator data into the hybrid design optimization model, and output multiple electromechanical product 3D models corresponding to the electromechanical product design requirements;

[0131] The electromechanical product generation module is used to produce the corresponding electromechanical product according to the electromechanical product 3D model.

[0132] The above system embodiments and method embodiments correspond one to one. For a brief description of the system embodiments, please refer to the method embodiments.

[0133] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for designing electromechanical products based on deep learning of component database, characterized in that: The following steps are involved: S1, establishing a parts database, wherein the parts database includes a plurality of parts model index data, a plurality of parts assembly schemes, and a plurality of electromechanical product 3D models. When the types and quantities of parts are determined, all combinations of the part model index data and any part assembly scheme correspond to a unique electromechanical product 3D model; S2, establishing an initial hybrid design optimization model, training and testing the initial hybrid design optimization model through the parts database to obtain a hybrid design optimization model; Step S2 includes: The initial hybrid design optimization model includes an input layer, a first sub-network layer, a second sub-network layer, a feature fusion layer, an assembly optimization layer, and an output layer. The input layer is connected to the first sub-network layer and the second sub-network layer respectively. The first sub-network layer and the second sub-network layer are connected to the feature fusion layer respectively. The feature fusion layer is connected to the assembly optimization layer. The assembly optimization layer is connected to the output layer. The input layer is used to receive input features; the first sub-network layer is used to extract component timing performance change features; the second sub-network layer is used to extract component structural features; the feature fusion layer is used to fuse component timing performance change features and component structural features to obtain fused features; the assembly optimization layer is used to apply multiple component assembly schemes to the fused features to obtain multiple corresponding electromechanical product 3D models; S3, obtaining electromechanical product design requirements, obtaining multiple required component model index data corresponding to the electromechanical product design requirements based on the electromechanical product design requirements and the component assembly requirement analysis model, inputting the multiple required component model index data into the hybrid design optimization model, and outputting multiple electromechanical product 3D models corresponding to the electromechanical product design requirements; S4, producing a corresponding electromechanical product according to the electromechanical product 3D model.

2. The electromechanical product design method based on deep learning of parts database according to claim 1, characterized in that: The first sub-network layer adopts a long short-term memory network. The first sub-network layer includes an input gate, a forget gate, a unit state, an output gate, and a hidden state. The characteristic function expression of the component timing performance change of the first sub-network layer is: ； ； ； ； ； ； ； in, 、 、 are the outputs of the input gate, forget gate, and output gate respectively. is the cell state at the current time step, is the hidden state of the current time step, is the hidden state of the previous time step, is the hidden state at the last time step, is the input of the current time step, 、 、 、 are the weight matrices of the input gate, forget gate, unit state update, and output gate, respectively. 、 、 、 are the bias vectors for the input gate, forget gate, unit state update, and output gate, respectively. is the input feature of the input layer, is the Hadamard product, is the input of the last time step, is the output of the first sub-network layer.

3. The electromechanical product design method based on deep learning of parts database according to claim 2, characterized in that: The second sub-network layer adopts a convolutional neural network, including a convolution layer, a sigmoid activation layer and a pooling layer. The component structure characteristic function expression of the second sub-network layer is: ； in, is the output of the second sub-network layer, is the pooling function, is an exponential function, is the input feature of the input layer, is the weight matrix of component structural features, is the offset vector of the component structure feature.

4. The electromechanical product design method based on deep learning of parts database according to claim 3, characterized in that: The feature fusion function expression of the feature fusion layer is: ； in, To fusion features, is the fusion weight of the first sub-network layer, is the output of the first sub-network layer, is the output of the second sub-network layer.

5. The electromechanical product design method based on deep learning of parts database according to claim 4, characterized in that: The assembly optimization layer includes a fully connected layer, and the function expression of the output of the assembly optimization layer is: ； in, A collection of 3D models of electromechanical products. Optimize feature parameter mapping function for assembly, It is a fusion feature; yes and the mapping function of the 3D model of the electromechanical product, where one Corresponding to multiple 3D models of electromechanical products.

6. The electromechanical product design method based on deep learning of parts database according to claim 1, characterized in that: Step S3 includes: The component assembly demand analysis model includes a demand input layer, a demand analysis layer, a component model layer and a component data output layer, wherein the demand input layer is connected to the demand analysis layer, the demand analysis layer is connected to the component model layer, and the component model layer is connected to the component data output layer; The demand input layer is used to input the design requirements of electromechanical products, wherein the design requirements of electromechanical products include the size, functional requirements and service life of the electromechanical products; The demand analysis layer is used to determine the types of components required for the electromechanical product design based on the electromechanical product design requirements; The component model layer is used to search for multiple required component model index data of the required components from the component database according to the required component type; The component data output layer connection is used to output the plurality of required component model indicator data.

7. The electromechanical product design method based on deep learning of parts database according to claim 1, characterized in that: Step S1 includes: The component model index data includes component geometric properties, component material properties, and component timing performance change properties, and the component assembly plan includes assembly sequence and combination method.

8. The electromechanical product design method based on deep learning of parts database according to claim 1, characterized in that: Step S4 includes: Based on the 3D model of the electromechanical product, the corresponding rapid manufacturing technology is selected, a manufacturing process flow is formulated, the corresponding electromechanical product is produced, and appearance inspection and performance testing are carried out. The rapid manufacturing technology includes 3D printing, CNC machining, numerical control machining and injection molding.

9. A mechatronic product design system based on deep learning of parts database, characterized in that: The system comprises: a database establishment module for establishing a parts database, wherein the parts database includes a plurality of parts model index data, a plurality of parts assembly schemes, and a plurality of electromechanical product 3D models. When the types and quantities of parts are determined, all combinations of the parts model index data and any parts assembly scheme correspond to a unique electromechanical product 3D model; A hybrid design optimization module is used to establish an initial hybrid design optimization model, train and test the initial hybrid design optimization model through a parts database, and obtain a hybrid design optimization model; The initial hybrid design optimization model includes an input layer, a first sub-network layer, a second sub-network layer, a feature fusion layer, an assembly optimization layer, and an output layer. The input layer is connected to the first sub-network layer and the second sub-network layer respectively. The first sub-network layer and the second sub-network layer are connected to the feature fusion layer respectively. The feature fusion layer is connected to the assembly optimization layer. The assembly optimization layer is connected to the output layer. The input layer is used to receive input features; the first sub-network layer is used to extract component timing performance change features; the second sub-network layer is used to extract component structural features; the feature fusion layer is used to fuse component timing performance change features and component structural features to obtain fused features; the assembly optimization layer is used to apply multiple component assembly schemes to the fused features to obtain multiple corresponding electromechanical product 3D models; A product demand analysis module is configured to obtain electromechanical product design requirements, obtain multiple required component model indicator data corresponding to the electromechanical product design requirements based on the electromechanical product design requirements and the component assembly requirement analysis model, input the multiple required component model indicator data into the hybrid design optimization model, and output multiple electromechanical product 3D models corresponding to the electromechanical product design requirements; The electromechanical product generation module is used to produce the corresponding electromechanical product according to the electromechanical product 3D model.

Images