Detailed description of the preferred embodiments
The technical solutions in the present invention will be described clearly and completely with reference to the accompanying drawings, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be understood that the step numbers used herein are for convenience of description only and are not used as limitations on the order in which the steps are performed.
It is to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
The terms "comprises" and "comprising" indicate the presence of the described features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
The term "and/or" refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, the present invention provides a method for optimizing a beam unit structure based on a PSO-BP neural network, including:
s101, determining initial structure parameters, and extracting a part of parameter combinations to serve as initialization particles of a PSO algorithm, so as to achieve the purpose of optimizing optimal BP initial weight and threshold.
Please refer to fig. 2, a joint optimization method based on the PSO-BP neural network and ABAQUS. Firstly, parameterization design is carried out on a beam unit structure, and programming is carried out by using a jupyter programming function of anaconda, so that batch generation of a pre-processing file and batch extraction of post-processing finite element analysis data are realized. Meanwhile, the aim of batch submission of the preprocessed files can be achieved through the bat file format. The beam unit structure can be rapidly modeled through parametric design, a pre-processing file is generated and submitted to a CAE solver for analysis, the operation can be rapidly realized, the time of repeated operation is saved, and the efficiency is improved.
S102, beam unit structure parametric modeling, ABAQUS secondary development, preprocessing batch generation and simulation data extraction. The secondary development can quickly realize the batch generation of the inp file, the structural parameters of the beam unit and the modification of the preprocessing parameters are modified in the file, and a large amount of time wasted on repeated operation can be saved after the secondary development. The post-processing process is developed for the second time, particularly for the part of extracted data, a large amount of data can be generated after the ABAQUS solver is used for solving, only a part of data is needed, the most data, such as stress, strain, overall displacement, plastic deformation and the like, of the maximum joint are obtained, specifically, the needed data can be obtained and used as a training set of a BP neural network for training, a simulated cloud picture can be obtained, and the result of simulation analysis can be checked more intuitively.
The macro recording function of the ABAQUS can record each step of operation in the form of codes, complete codes can be obtained through a complete finite element analysis process, and codes are subjected to parameter modification and partial deletion to obtain codes for secondary development.
After the initial structure parameters of the beam unit structure are determined, three-dimensional modeling is carried out in SW, the parameters can be associated through the equation function of the SW, the model can be quickly updated by modifying the parameters after the model is built, and the three views and the engineering drawing of the updated beam unit structure can be obtained while the time is saved.
S103, training the extracted data as bp neural network training data and acquiring trained parameters
Firstly, the number of input layers, hidden layers and output layers of the neural network is determined according to design parameters, and the relation between neurons of the hidden layers and neurons of the input layers in the network is n2=2×n1+1。
Meanwhile, the weight and the threshold value after PSO optimization are used for training in the BP neural network, so that the convergence speed of the neural network can be greatly increased. Training can also be directly carried out to check the training effect of the BP neural network which is not optimized and compare with the training effect. After the predicted parameters are obtained, the design parameters are updated in SW.
And S104, carrying out secondary development in SW, connecting related parameters by using self-contained equation functions, updating the model after updating design parameters, and generating a three-view and an engineering drawing of the new model for later use.
Also provided is a computer terminal device including one or more processors and memory. A memory is coupled to the processor for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement a PSO-BP based beam cell structure optimization method as in any one of the above embodiments.
The processor is used for controlling the overall operation of the computer terminal equipment so as to complete all or part of the steps of the ABAQUS-based beam unit structure optimization method. The memory is used to store various types of data to support the operation at the computer terminal device, which data may include, for example, instructions for any application or method operating on the computer terminal device, as well as application-related data. The Memory may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk.
In an exemplary embodiment, the computer terminal Device may be implemented by one or more Application Specific 1 integrated circuits (AS 1C), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components, and is configured to perform the PSO-BP based beam unit structure optimization method and achieve technical effects consistent with the above methods.
In another exemplary embodiment, there is also provided a computer readable storage medium comprising program instructions which, when executed by a processor, implement the steps of the PSO-BP based beam cell structure optimization method in any of the above embodiments. For example, the computer readable storage medium may be the above-mentioned memory including program instructions executable by a processor of a computer terminal device to perform the above-mentioned PSO-BP based beam cell structure optimization method, and achieve the technical effects consistent with the above-mentioned method.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.