计算机科学
人工神经网络
焊接
群体智能
失真(音乐)
序列(生物学)
群体行为
算法
人工智能
粒子群优化
机械工程
电信
生物
遗传学
工程类
放大器
带宽(计算)
作者
Chunbiao Wu,Chao Wang,Jae-Woong Kim
标识
DOI:10.1016/j.engappai.2022.105142
摘要
This study aims to develop a welding sequence optimization (WSO) framework based on coupled artificial neural network (ANN) and swarm intelligence algorithm for minimizing welding distortion of thin-walled squared Al–Mg–Si alloy tube components. This framework is mainly composed of two critical computer programs. Firstly, a multilayer feedforward backpropagation neural network (BPNN) system was established to rapidly estimate residual distortion for an arbitrary welding sequence so that welding sequence can be optimized for achieving desired welding quality. For this purpose, a series of nonlinear thermo-elastic–plastic finite element (FE) simulations were conducted and verified with experiments to generate the input database of the neural network. Subsequently, a reliable BPNN model was successfully created and trained within an acceptable error. Secondly, a novel swarm intelligence algorithm, namely, bees algorithm (BA) was proposed to solve the complicated WSO problems. In this optimization process, the trained BPNN model was implanted into this proposed BA for computing the fitness value of arbitrary welding sequences. Moreover, welding experiments were also performed to confirm the performance of the proposed optimization method. Comparing the results from experimental measurements, FE simulations, and proposed WSO framework, it is demonstrated that this proposed BPNN-and-BA-based WSO framework can be successfully applied in practical engineering to obtain an optimal welding sequence for minimizing final welding distortion.
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