| 冯延鹏,张爱华,梁婷婷,马强杰,马 晶,王 平.基于改进 SSA 优化 SVM 的超窄间隙焊接质量评估[J].电子测量与仪器学报,2023,37(6):195-205 |
| 基于改进 SSA 优化 SVM 的超窄间隙焊接质量评估 |
| Quality evaluation of ultra-narrow gap welding based on improved SSA optimizing SVM |
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| DOI： |
| 中文关键词: 麻雀搜索算法 焊接质量评估 超窄间隙焊接 莱维飞行 Logistic-Tent 混沌映射 支持向量机 |
| 英文关键词:sparrow search algorithm evaluation of welding quality ultra-narrow gap welding Levy flights Logistic-Tent chaotic mapping support vector machine |
| 基金项目:国家自然科学基金(62173170, 61866021)、辽宁省自然基金(2020-KF-21-04, 2021-KF-21-04)项目资助 |
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| Author | Institution |
| Feng Yanpeng | 1. College of Electrical and Information Engineering, Lanzhou University of Technology |
| Zhang Aihua | 1. College of Electrical and Information Engineering, Lanzhou University of Technology,2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology,3. National Demonstration Center for Experimental Electrical and Control Engineering Education,
Lanzhou University of Technology |
| Liang Tingting | 1. College of Electrical and Information Engineering, Lanzhou University of Technology |
| Ma Qiangjie | 1. College of Electrical and Information Engineering, Lanzhou University of Technology |
| Ma Jing | 1. College of Electrical and Information Engineering, Lanzhou University of Technology,2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology,3. National Demonstration Center for Experimental Electrical and Control Engineering Education,
Lanzhou University of Technology |
| Wang Ping | 1. College of Electrical and Information Engineering, Lanzhou University of Technology,2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology,3. National Demonstration Center for Experimental Electrical and Control Engineering Education,
Lanzhou University of Technology |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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 multistrategy 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 ultranarrow gap welding quality evaluation. |
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