Complex part design method and system based on digital-real fusion test

Abstract

The invention discloses a complex part design method and system based on a digital-real fusion test, and belongs to the field of electronic engineering and computer science, and the method comprises the steps: designing a model construction module of a complex part, then, physical constraints in the entity environment and additive manufacturing constraints in the manufacturing process are introduced, iteration and verification of the model are completed based on the additive manufacturing technology and user requirements, and a complex part model with data and real fusion is obtained; a complex part model topological optimization module is designed, the module carries out finite element division on an original model, optimization parameters are set based on multiple topological optimization algorithms and an artificial neural network algorithm, and a group of topological optimization result models are obtained; and a detection and correction module of the complex part model is designed, the module constructs an evaluation system based on user requirements and model constraints, the optimization model is evaluated through digital testing, and an optimal design scheme or correction suggestion is given.

Description

Technical Field

[0001] The present invention belongs to the field of electronic engineering and computer science, and specifically relates to a complex parts design method and system based on digital-real fusion testing. Background Art

[0002] Complex parts are widely used in various mechanical equipment, such as fuel nozzles, artificial bones, and aeroengine blades. Due to their complex structures and high performance requirements, these parts are difficult to design and produce using traditional methods, and are often costly and of poor quality. In view of these difficulties, the advantages of additive manufacturing technology can be well reflected, but at the same time, it also puts forward higher requirements for the digital-physical integration of the entire process of parts production. Summary of the invention

[0003] The technical problem to be solved by the present invention is: to provide a complex part design method and system based on digital-real fusion testing, which covers the design of a complex part model construction module, a complex part model topology optimization module and a complex part model detection and correction module. With the help of digital-real fusion testing technology, the degree of fusion between the part model and the entity can be enhanced to a certain extent, and the adaptability of the part model to additive manufacturing technology and the performance of the part entity can be improved.

[0004] The present invention solves the technical problem by adopting the following technical solution: a complex parts design method based on digital-real fusion testing, comprising the following steps:

[0005] Step (1) Design a complex part model building module. This module builds a model based on the geometric structure of the part requirements, introduces physical constraints in the physical environment and additive manufacturing constraints in the manufacturing process, and completes model iteration and verification based on the additive manufacturing process and user requirements to obtain a complex part model that integrates digital and physical elements.

[0006] Step (2) designing a complex part model topology optimization module, which performs finite element division on the complex part model and completes the setting of optimization parameters based on a variety of topology optimization algorithms and artificial neural network algorithms to obtain a set of topology optimization result models;

[0007] Step (3) Design a detection and correction module for complex part models. This module builds an evaluation system based on user needs and model constraints, evaluates the optimized result models through digital testing, and gives the best design solution or correction suggestions.

[0008] The present invention also proposes a complex parts design system based on digital-real fusion testing, comprising:

[0009] Complex part model building module, which builds models based on the geometric structure of part requirements, introduces physical constraints in the physical environment and additive manufacturing constraints in the manufacturing process, completes model iteration and verification based on additive manufacturing technology and user requirements, and obtains a complex part model that integrates digital and physical elements;

[0010] Complex part model topology optimization module, which performs finite element division on the complex part model and sets the optimization parameters based on a variety of topology optimization algorithms and artificial neural network algorithms to obtain a set of topology optimization result models;

[0011] The module for detecting and correcting complex part models builds an evaluation system based on user requirements and model constraints, evaluates the optimized result models through digital testing, and gives the best design solution or correction suggestions.

[0012] The advantages of the present invention compared with the prior art are: by using digital-real fusion testing technology and combining the topology optimization algorithm to optimize the structure of the model, an optimization model of complex parts suitable for additive manufacturing is constructed, and at the same time, the setting of the optimization algorithm and optimization parameters is realized based on machine learning technology. On this premise, by constructing a model optimization evaluation system, the optimization results are evaluated in combination with model performance indicators, model constraint characteristics and model lightweight degree, and the problem is traced and corrected when the requirements are not met. This can strengthen the degree of fusion between the part model and the entity to a certain extent, improve the adaptability of the part model to additive manufacturing technology and the performance of the part entity. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a flowchart of a complex parts design method based on digital-real fusion testing of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined with each other as long as they do not conflict with each other. To achieve the above-mentioned purpose, the present invention adopts the following technical scheme.

[0015] The present invention relates to a complex part design method and system based on digital-physical fusion testing, including the design of a complex part model building module, a complex part model topology optimization module and a complex part model detection and correction module, which can enhance the degree of fusion between the part model and the entity to a certain extent, improve the adaptability of the part model to additive manufacturing technology and the performance of the part entity.

[0016] The flowchart of the present invention is as follows Figure 1 As shown, the specific implementation is as follows:

[0017] Step (1) Design a complex part model building module. This module builds a model based on the geometric structure of the part requirements, introduces physical constraints in the physical environment and additive manufacturing constraints in the manufacturing process, and completes model iteration and verification based on the additive manufacturing process and user requirements to obtain a complex part model that integrates digital and physical elements. The specific implementation is as follows;

[0018] Step (1.1) Preliminarily construct a digital model based on the geometric structure required by the complex part, and use its various geometric parameters as geometric data of the digital model;

[0019] Step (1.2) introduces physical constraints in the physical environment and additive manufacturing constraints in the manufacturing process, that is, adding the electromagnetic, thermal and other multi-physical field effects of the parts in the real environment on the basis of the digital model, as well as the specific geometric and physical conditions that the parts must meet in the additive manufacturing process, to construct a complex part model that integrates digital and physical elements;

[0020] Step (1.3) Iterate and verify the model based on the additive manufacturing process and user needs. Place the digital model in a simulation environment to determine whether it can meet user needs. Combined with the specific additive manufacturing process, determine whether the added constraints are reasonable. If qualified, complete the construction of the complex part model. Otherwise, rebuild the geometric model or introduce constraints according to the problem.

[0021] Step (2), designing a complex part model topology optimization module, which performs finite element division on the complex part model, and completes the setting of optimization parameters based on a variety of topology optimization algorithms and artificial neural network algorithms to obtain a set of topology optimization result models, which is specifically implemented as follows;

[0022] Step (2.1) divides the complex part model constructed in step (1) into finite elements, and the number of divided units is recorded as ;

[0023] Step (2.2) Based on the model attributes and constraint characteristics, an artificial neural network model is built, and the characteristics and requirements of the parts themselves are used as the feature input of the neural network. By introducing the cases and applicable scopes of different topology optimization algorithms as training sets, the applicable topology optimization algorithm is selected for the parts and requirements, and then the corresponding topology optimization method examples are used as training sets to achieve the setting of optimization parameters;

[0024] Step (2.3) Based on the topology optimization algorithm and its corresponding optimization parameters, the part model is optimized and calculated to obtain a set of optimization result models to be tested;

[0025] Step (3), designing a detection and correction module for complex part models. Designing a detection and correction module for complex part models. This module builds an evaluation system based on user requirements and model constraints, evaluates the optimized result models through digital testing, and gives the optimal design solution or correction suggestions. The specific implementation is as follows;

[0026] Step (3.1) constructs a model optimization evaluation system based on model performance indicators, model constraint characteristics and model lightweight degree. The model performance indicators evaluate the optimization results by comparing the model performance difference before and after optimization. The model constraint characteristics evaluate the optimization results by calculating the constraint additional cost and complexity. The constraint additional cost mainly considers the amount of support material used, while the complexity mainly considers the additional printing time. The model lightweight degree is evaluated by calculating the quality of the optimized model. Model quality before optimization Ratio Evaluate the optimization results, It is the sequence number within the optimization result model group;

[0027] Step (3.2) Based on the model optimization evaluation system and user needs, digital testing technology is used to test and obtain various indicators of the evaluation system. By comparing the indicators of the evaluation system with the indicators of user needs, it is determined whether the optimization results meet the needs. If there are optimization results that meet the needs, the best design solution is given. Otherwise, the problem is traced to determine whether the model needs to be modified, whether the optimization algorithm and parameters need to be reset, and correction suggestions are given.

[0028] In summary, the present invention discloses a complex part design method based on digital-real fusion testing, including the design of a complex part model construction module, a complex part model topology optimization module and a complex part model detection and correction module, which can enhance the degree of fusion between the part model and the entity to a certain extent, improve the adaptability of the part model to additive manufacturing technology and the performance of the part entity.

[0029] The contents not described in detail in the specification of the present invention belong to the prior art known to the professional and technical personnel in this field.

[0030] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A complex parts design method based on digital-real fusion testing, characterized in that: The following steps are involved: Step (1) Design a complex part model building module. This module builds a model based on the geometric structure of the part requirements, introduces physical constraints in the physical environment and additive manufacturing constraints in the manufacturing process, and completes model iteration and verification based on the additive manufacturing process and user requirements to obtain a complex part model that integrates digital and physical elements. Step (2) designing a complex part model topology optimization module, which performs finite element division on the complex part model and completes the setting of optimization parameters based on a variety of topology optimization algorithms and artificial neural network algorithms to obtain a set of topology optimization result models; Step (3) Design a detection and correction module for complex part models. This module builds an evaluation system based on user needs and model constraints, evaluates the optimized result models through digital testing, and gives the best design solution or correction suggestions.

2. A complex parts design method based on digital-real fusion testing according to claim 1, characterized in that: The specific implementation of step (1) is as follows: Step (1.1) Preliminarily construct a digital model based on the geometric structure required by the complex part, and use its various geometric parameters as geometric data of the mathematical model; Step (1.2) introduces physical constraints in the physical environment and additive manufacturing constraints in the manufacturing process, that is, adding the electromagnetic, thermal and multi-physical field effects of the parts in the real environment on the basis of the digital model, as well as the specific geometric and physical conditions that the parts must meet in the additive manufacturing process, to construct a complex part model that integrates digital and physical elements; Step (1.3) completes the iteration and verification of the model based on the additive manufacturing process and user needs, places the digital model in a simulation environment to determine whether it can meet user needs, and determines whether the added constraints are reasonable based on the specific additive manufacturing process. If qualified, complete the construction of the complex part model. Otherwise, rebuild the digital model or introduce constraints based on the problem.

3. A complex parts design method based on digital-real fusion testing according to claim 2, characterized in that: The specific implementation of step (2) is as follows: Step (2.1) divides the complex part model constructed in step (1) into finite elements, and the number of divided units is recorded as ; Step (2.2) Based on the model attributes and constraint characteristics, an artificial neural network model is built, and the characteristics and requirements of the parts themselves are used as the feature input of the neural network. By introducing the cases and applicable scopes of different topology optimization algorithms as training sets, the applicable topology optimization algorithm is selected according to the parts and requirements, and then the corresponding topology optimization method examples are used as training sets to achieve the setting of optimization parameters; In step (2.3), based on the topology optimization algorithm and its corresponding optimization parameters, the part model is optimized and calculated to obtain a set of optimization result models to be tested.

4. A complex parts design method based on digital-real fusion testing according to claim 3, characterized in that: The specific implementation of step (3) is as follows: Step (3.1) constructs a model optimization evaluation system based on part model performance indicators, model constraint characteristics, and model lightweight degree. The model performance indicators evaluate the optimization results by comparing the model performance difference before and after optimization. The model constraint characteristics evaluate the optimization results by calculating the constraint additional cost and complexity. The constraint additional cost considers the amount of support material used, while the complexity considers the additional printing time. The model lightweight degree is evaluated by calculating the quality of the optimized model. Model quality before optimization Ratio Evaluate the optimization results, where It is the sequence number within the optimization result model group; Step (3.2) Based on the model optimization evaluation system and user needs, digital testing technology is used to test and obtain various indicators of the evaluation system. By comparing the indicators of the evaluation system with the indicators of user needs, it is determined whether the optimization results meet the needs. If there are optimization results that meet the needs, the best design solution is given. Otherwise, the problem is traced to determine whether the model needs to be modified, whether the optimization algorithm and parameters need to be reset, and correction suggestions are given.

5. A complex parts design system based on digital-real fusion testing, characterized in that: include: Complex part model building module, which builds models based on the geometric structure of part requirements, introduces physical constraints in the physical environment and additive manufacturing constraints in the manufacturing process, completes model iteration and verification based on additive manufacturing technology and user requirements, and obtains a complex part model that integrates digital and physical elements; Complex part model topology optimization module, which performs finite element division on the complex part model and sets the optimization parameters based on a variety of topology optimization algorithms and artificial neural network algorithms to obtain a set of topology optimization result models; The module for detecting and correcting complex part models builds an evaluation system based on user requirements and model constraints, evaluates the optimized result models through digital testing, and gives the best design solution or correction suggestions.

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