电子测量与仪器学报

陈斯睿,孙兴伟,杨赫然,董祉序,刘寅.基于改进支持向量机的砂带磨损程度识别[J].电子测量与仪器学报,2024,38(2):10-18

基于改进支持向量机的砂带磨损程度识别

Abrasion degree recognition of abrasive belt based onimproved support vector machine

DOI：

英文关键词:sand belt wear image recognition aquila optimizer optimization of parameters feature extraction

基金项目:2022年度辽宁省教育厅高等学校基本科研项目面上项目(LJKMZ20220459)、辽宁省应用基础研究计划项目（2022JH2/101300214）、国家自然科学基金项目（52005346）资助

作者	单位
陈斯睿	1.沈阳工业大学机械工程学院沈阳110870；2.辽宁省复杂曲面数控制造技术重点实验室沈阳110870
孙兴伟	1.沈阳工业大学机械工程学院沈阳110870；2.辽宁省复杂曲面数控制造技术重点实验室沈阳110870
杨赫然	1.沈阳工业大学机械工程学院沈阳110870；2.辽宁省复杂曲面数控制造技术重点实验室沈阳110870
董祉序	1.沈阳工业大学机械工程学院沈阳110870；2.辽宁省复杂曲面数控制造技术重点实验室沈阳110870
刘寅	1.沈阳工业大学机械工程学院沈阳110870；2.辽宁省复杂曲面数控制造技术重点实验室沈阳110870

Author	Institution
Chen Sirui	1.School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; 2.Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang 110870, China
Sun Xingwei	1.School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; 2.Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang 110870, China
Yang Heran	1.School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; 2.Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang 110870, China
Dong Zhixu	1.School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; 2.Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang 110870, China
Liu Yin	1.School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China; 2.Key Laboratory of Numerical Control Manufacturing Technology for Complex Surfaces of Liaoning Province, Shenyang 110870, China

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中文摘要:

为了准确判断砂带在磨削螺杆转子时的磨损程度，根据砂带磨损过程中表面图像颜色特征和纹理特征的变化规律，对砂带磨损程度进行识别。对磨削加工后砂带表面图像的纹理特征和颜色特征进行提取，根据不同磨削时间段螺杆转子表面粗糙度划分砂带磨损程度。支持向量机的分类性能受到自身核函数与惩罚函数的影响较大，因此提出利用天鹰优化算法对支持向量机的核参数与惩罚参数进行优化，建立AO-SVM砂带图像识别磨损程度模型。利用自主研发的螺杆转子专用砂带磨削装置完成实验。磨削参数设置如下：砂带线速度为10 m/s,工件轴向进给速度为50 mm/min，张紧轮的气缸压力为0.35 MPa，主动轮的气缸压力为0.5 MPa，磨削时间为25 min。AO-SVM对砂带磨损程度模型的识别准确率达到92.5%，比随机森林算法(RFC)和XGboost分类算法分别高出5.0%和3.6%，且收敛速度更快。AO-SVM模型可以通过砂带表面图像的颜色特征变化和纹理特征变化对砂带磨损程度进行识别，可以有效避免砂带磨损过度损伤工件，为砂带磨削螺杆转子时判断砂带的磨损程度和换带时间提供理论指导。

英文摘要:

In order to accurately determine the degree of abrasion of abrasive belts in grinding screw rotors, the degree of abrasion of abrasive belts is identified according to the law of change of the color characteristics and texture characteristics of the surface image in the abrasion process of abrasive belts. The texture features and color features of the surface image of the abrasive belt after the grinding process are extracted, and the abrasive belt wear degree is classified according to the surface roughness of the screw rotor in different grinding time periods. The classification performance of the support vector machine is greatly affected by its own kernel function and penalty function, so it is proposed to optimize the kernel parameter and penalty parameter of the support vector machine by using the aquila optimizer optimization algorithm, and to establish the wear degree model of AO-SVM sand belt image recognition. The experiment is completed by utilizing a self-developed special belt grinding device for screw rotors. The grinding parameters are set as follows: the linear speed of the grinding belt is 10 m/s, the axial feed speed of the workpiece is 50 mm/min, the cylinder pressure of the tensioning wheel is 0.35 MPa, the cylinder pressure of the active wheel is 0.5 MPa, and the grinding time is 25 min. The recognition accuracy of AO-SVM for the abrasive belt wear degree model reaches 92.5%, which is improved by 5.0% and 3.6% compared to the random forest algorithm (RFC) and the XGboost classification algorithm, respectively, and the convergence speed is fast. The degree of abrasive belt wear can be identified by the AO-SVM model through the color feature change and texture feature change of the surface image of the abrasive belt, which can effectively avoid excessive abrasive belt wear and damage to the workpiece, and provide theoretical guidance for judging the degree of abrasive belt wear and the time to change the abrasive belt when the abrasive belt is used to grind the screw rotor.

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