School of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, 212013, China.
Sci Rep. 2023 Jan 23;13(1):1252. doi: 10.1038/s41598-023-28538-8.
A prediction method based on an improved salp swarm algorithm (ISSA) and extreme learning machine (ELM) was proposed to improve line heating and forming. First, a three-dimensional transient numerical simulation of line heating and forming was carried out by applying a finite element simulation, and the influence of machining parameters on deformation was studied. Second, a prediction model for the ELM network was established based on simulation data, and the deformation of hull plate was predicted by the training network. Additionally, swarm intelligence optimization, particle swarm optimization (PSO), the seagull optimization algorithm (SOA), and the salp swarm algorithm (SSA) were studied while considering the shortcomings of the ELM, and the ISSA was proposed. Input weights and hidden layer biases of the ELM model were optimized to increase the stability of prediction results from the PSO, SOA, SSA and ISSA approaches. Finally, it was shown that the prediction effect of the ISSA-ELM model was superior by comparing and analyzing the prediction effect of each prediction model for line heating and forming.
提出了一种基于改进沙鱼群算法（ISSA）和极限学习机（ELM）的预测方法，以提高线性加热和成型的质量。首先，通过有限元模拟进行了线性加热和成型的三维瞬态数值模拟，并研究了加工参数对变形的影响。其次，基于仿真数据建立了 ELM 网络的预测模型，并通过训练网络对船体板的变形进行预测。此外，考虑到 ELM 的缺点，研究了群智能优化、粒子群优化（PSO）、海鸥优化算法（SOA）和沙鱼群算法（SSA），并提出了 ISSA。通过优化 ELM 模型的输入权重和隐含层偏差，从 PSO、SOA、SSA 和 ISSA 方法提高了预测结果的稳定性。最后，通过比较和分析各预测模型对线性加热和成型的预测效果，表明了 ISSA-ELM 模型的预测效果更优。
