基于激光散斑图像和卷积神经网络-支持向量回归的表面粗糙度预测

Surface Roughness Prediction Based on Laser Speckle Images and Convolutional Neural Network-Support Vector Regression

目前,基于激光散斑图像对表面粗糙度进行测量的视觉方法主要有两类:建立人工设计的散斑图像特征参数与表面粗糙度之间的关系,或建立深度学习网络预测模型。这两类方法均存在不足,前者特征参数设计阶段过程复杂,后者需要大量的样本图像。针对以上问题,本文提出一种基于激光散斑图像和卷积神经网络-支持向量回归(CNN-SVR)的表面粗糙度预测方法。该方法在预训练的卷积神经网络基础上引入迁移学习,将卷积神经网络池化层的深度特征输入支持向量回归网络进行表面粗糙度值预测,不仅能实现激光散斑图像特征的自动提取,而且在少量样本时就能实现对表面粗糙度值较高精度的预测。实验结果表明,建立的模型对平磨、卧铣、立铣试件表面粗糙度预测的平均绝对百分比误差分别为3.46%、3.20%和3.53%,表现出较高的精度。

Two visual methods are mainly used for measuring surface roughness based on laser speckle images.One method involves establishing the relationship between artificially designed speckle image feature parameters and surface roughness,and the other requires building a deep learning network prediction model.Both methods have limitations.The former involves a complex process in the feature parameter design,whereas the latter requires many sample images.This study proposes a method for predicting surface roughness based on laser speckle images and convolutional neural networksupport vector regression(CNNSVR).The proposed method incorporates transfer learning into a pretrained CNN,in which the deep features from the pooling layer of the network are input into an SVR network for surface roughness prediction.This approach automates the extraction of laser speckle image features and achieves highprecision predictions of surface roughness values with a few samples.Experimental results have demonstrated that the established model exhibits high accuracy in predicting the average absolute percentage errors of the surface roughness for plane grinding,horizontal milling,and vertical milling specimens,which are 3.46%,3.20%,and 3.53%,respectively.