基于图像信息的砂带磨削材料去除率预测模型

Image-based prediction model for material removal rate of abrasive belt grinding

砂带磨削广泛应用于工业领域,由于其与工件的柔性接触及砂带磨粒分布的非均匀性,导致砂带磨削中材料去除率难以从理论上精准预估,直接影响砂带磨削效率及其质量控制。基于此提出一种基于火花图像信息的砂带磨削材料去除率识别方法,给出了火花图像的分割处理算法,建立了火花图像的颜色、亮度、面积及轮廓特征的量化特征模型,基于皮尔逊系数分析了火花图像特征与砂带磨削材料去除率之间的相关性,分别建立了基于火花图像单特征的线性回归预测模型和基于支持向量回归(SVR)算法的多特征回归预测模型,采用最大误差、均方差及决定系数作为模型的评价参数,结果表明基于径向基核函数的多特征SVR模型的具有较高的预测精度,决定系数可达0.976。所提出的方法为砂带磨削材料去除率的有效控制提供了一种新途径。

Abrasive belt grinding is widely applied in the industry field.Due to its flexible contact with the workpiece and the non-uniformity of abrasive distribution on the belt,the material removal rate is difficult to be predicted accurately in theory.It directly affects the efficiency and quality control of the abrasive belt grinding.This study proposes a method for identifying the material removal rate of abrasive belt grinding based on spark images.A segmentation algorithm for spark images is presented.Quantitative feature models of the color,brightness,area,and contour features of the spark image are formulated.Pearson coefficient is used to analyze the correlation between the feature of the spark image and the material removal rate of abrasive belt grinding.A linear regression model based on the single feature of the spark image and a multi-feature regression model based on the support vector regression(SVR)are established,respectively.The maximum error,the mean square error,and the determination coefficient are used as the evaluation metrics.Experimental results show that the multi-feature SVR model based on the radial basis kernel function can achieve high prediction accuracy with the determination coefficient of 0.976.The proposed method in this paper provides a new way to effectively control the material removal rate of abrasive belt grinding.