基于鲸鱼优化算法的焊缝尺寸预测  

Prediction of weld size prediction based on Whale Optimization Algorithm

作　　者：姚屏 李文强 陈威 何日恒 张佩美 张广潮 YAO Ping;LI Wenqiang;CHEN Wei;HE Riheng;ZHANG Peimei;ZHANG Guangchao(College of Automation,Guangdong Polytechnic Normal University,Guangzhou 510630,China;College of Intelligent Manufacturing and Electrical Engineering,Guangzhou Institute of Science and Technology,Guangzhou 510540,China)

机构地区：[1]广东技术师范大学自动化学院,广州510630 [2]广州理工学院智能制造与电气工程学院,广州510540

出　　处：《焊接学报》2024年第11期133-139,共7页Transactions of The China Welding Institution

基　　金：国家自然科学基金资助项目(51805099);广东省教育厅重大专项资助项目(2020ZDZX2019);广东省高等教育学会实验室管理专业委员会基金项目(GDJ2022032)。

摘　　要：在机器人电弧焊过程中,准确预测焊缝尺寸对于控制焊接成形质量具有重要意义.文中提出了一种融合鲸鱼优化算法(whale optimization algorithm,WOA)与深度信念网络(deep belief network,DBN)的预测模型,简称WOA-DBN.该模型以电流、频率、占空比和焊接速度作为输入参数,进行了四因素十水平正交试验,构建了针对电弧焊焊缝尺寸的预测模型.为提升算法的搜索效率、增强收敛性能并避免陷入局部最优解,引入了混沌反向学习初始化种群、非线性收敛因子以及模拟退火操作和自适应变异扰动等策略,建立了一种混沌鲸鱼优化算法优化的深度信念网络模型,即AAMCWOA-DBN.通过试验对比,AAMCWOA-DBN模型在预测精度和性能指标方面均优于传统的WOA-DBN模型,熔宽预测的MAPE仅有1.85%,余高预测的MAPE仅有0.47%.文中利用人工智能算法对电弧焊的焊缝尺寸进行预测,为焊接成形控制和焊接质量的智能化提供了新的研究视角和方法,有望在相关领域得到应用.In the robotic arc welding manufacturing process,accurate prediction of weld seam size is important for controlling the quality of weld formation.In this study,a prediction model fusing whale optimization algorithm(WOA)and deep belief network(DBN),referred to as WOA-DBN,is proposed,which constructs a prediction model of weld size for arc welding manufacturing,using current,frequency,duty cycle,and welding speed as the input parameters.Welding seam size prediction model.In order to improve the search efficiency of the algorithm,enhance the convergence performance and avoid falling into the local optimal solution,this paper introduces the strategies of chaotic inverse learning initialization population,nonlinear convergence factor,as well as the simulated annealing operation and adaptive mutation perturbation,etc.,and establishes a deep belief network model optimized by the chaotic whale optimization algorithm,i.e.,AAMCWOA-DBN.Through experimental comparison,the AAMCWOA-DBN model outperforms the traditional WOA-DBN model in terms of prediction accuracy and performance metrics,the MAPE of the fused wide forecast is only 1.85%and the MAPE of the remaining high forecast is only 0.47%.This study utilizes artificial intelligence algorithms to predict the weld seam size of arc welding manufacturing,which provides new research perspectives and methods for the intelligent control of weld shaping and weld quality,and is expected to be applied in related fields.

关 键 词：人工智能 机器人电弧焊 焊缝尺寸预测 WOA-DBN AAMCWOA-DBN 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]