基于改进 SSA 优化 SVM 的超窄间隙焊接质量评估

超窄间隙焊接坡口较窄且深，难以直接通过视觉来评估焊接质量，针对上述问题，本文提出了一种基于混沌多策略扰动麻雀搜索算法(CMDSSA)优化支持向量机(SVM)的超窄间隙焊接质量评估模型。首先对麻雀搜索算法(SSA)进行改进，引入Logistic-Tent混沌映射和多扰动策略来提高麻雀搜索算法的寻优性能；然后通过与SSA、CSSOA、PSO、GA和WOA算法进行寻优测试对比，验证了CMDSSA算法的优越性；最后利用CMDSSA对SVM的惩罚因子C和核参数g进行寻优，构建CMDSSA-SVM质量评估模型对焊接质量进行评估。结果表明CMDSSA-SVM评估准确率为97.541%,验证了提出的超窄间隙焊接质量评估方法的高精度与可行性。

Quality evaluation of ultra-narrow gap welding based on improved SSA optimizing SVM

The groove of ultra-narrow gap welding is narrow and deep, so it is difficult to evaluate the welding quality directly through vision. To solve the above problems, this paper proposed an ultra-narrow gap welding quality evaluation model based on chaotic multi-strategy disturbed sparrow search algorithm ( CMDSSA) to optimize support vector machine ( SVM). Firstly, the sparrow search algorithm ( SSA) is improved, and the Logistic-Tent chaotic mapping and multi-disturbance strategy are introduced to improve the optimization performance of the sparrow search algorithm. Then, the superiority of CMDSSA algorithm is verified by comparing with SSA, CSSOA, PSO, GA and WOA algorithms. Finally, CMDSSA was used to optimize the penalty factor C and the kernel parameter g of SVM, and a CMDSSA-SVM quality evaluation model was constructed to evaluate the welding quality. The results show that the evaluation accuracy of CMDSSA-SVM is 97. 541%, which verifies the high accuracy and feasibility of the proposed method for ultra-narrow gap welding quality evaluation.