Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (22): 225-230.

Intelligent Recognition Method for Geometric Features of Parts Based on Supervised Machine Learning

WANG Yuyuan, XU Jie, JI Weixi

1. School of Mechanical Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
• Online:2019-11-15 Published:2019-11-13

基于监督式机器学习的零件几何特征智能识别

1. 江南大学 机械工程学院，江苏 无锡 214122

Abstract: For the detection of geometric parameters of shell-type parts without fixture positioning in machine vision, it is necessary to identify the geometric features of parts in order to plan the detection path. Therefor this paper proposes an intelligent recognition method for geometric features based on supervised machine learning. Firstly, the feature matrix is constructed according to the relation between features to be identified of the shell parts, and then the supervised machine learning algorithm is used to identify these features. An error correction method based on feature uniqueness is proposed to correct the identification errors generated in the classification process. For the research case involved in this paper, there are 4 holes to be identified in the part, and the accuracy of intelligent recognition is up to 100% after 5 supervised trainings.