基于L1/L2极限学习机的熔池熔透状态识别

ELM with L1/L2 regularization constraints

针对电弧焊熔池变化过程中非线性因素导致的熔透状态识别准确率低的问题,利用极限学习机(ELM)网络框架,提出一种基于L1/L2范数约束的ELM熔透状态识别模型.通过高速视觉传感系统获取熔池图像,利用主成分分析来进行特征提取.采用结构简单、训练简便的ELM算法来训练熔透识别模型,并利用L1范数约束抑制ELM输出权重中的异常值以改善ELM算法的泛化能力,同时利用L2范数约束来平滑ELM输出权重以获取熔池图像中的团块特征,提高熔池熔透状态的识别准确率.结果表明,基于L1/L2-ELM的熔透状态识别模型能够快速有效地判别熔池的全熔透、未熔透、过熔透三种状态.

An L1/L2 constrained ELM penetration identification model is proposed to solve the low accuracy problem for penetration state identification,caused by nonlinear factors during arc welding.Images of the molten pools are obtained by high speed visual sensing system.Then,feature extraction and dimensionality reduction are carried out by principal component analysis.ELM algorithm is used to train the penetration identification model for identification.To obtain generalization ability,L1 norm constraint is imposed on ELM optimization process to constraint suppresses outliers in ELM output weights.L2 norm constraint is introduced to obtain cluster features and smooth ELM output weights to improve the identification accuracy of the weld penetration.The results show that the weld penetration state recognition model based on L1/L2-ELM can quickly and effectively distinguish the three states of full penetration,partialpenetration and over-penetration.