Machine vision systems using machine learning for industrial product inspection

Yi Lu; Tie Qi Chen; Jie Chen; Jian Zhang; Anthony Tisler

doi:10.1117/12.455253

11 February 2002 Machine vision systems using machine learning for industrial product inspection

Yi Lu, Tie Qi Chen, Jie Chen, Jian Zhang, Anthony Tisler

Proceedings Volume 4567, Machine Vision and Three-Dimensional Imaging Systems for Inspection and Metrology II; (2002) https://doi.org/10.1117/12.455253
Event: Intelligent Systems and Advanced Manufacturing, 2001, Boston, MA, United States

Abstract

Machine vision inspection requires efficient processing time and accurate results. In this paper, we present a machine vision inspection architecture, SMV (Smart Machine Vision). SMV decomposes a machine vision inspection problem into two stages, Learning Inspection Features (LIF), and On-Line Inspection (OLI). The LIF is designed to learn visual inspection features from design data and/or from inspection products. During the OLI stage, the inspection system uses the knowledge learnt by the LIF component to inspect the visual features of products. In this paper we will present two machine vision inspection systems developed under the SMV architecture for two different types of products, Printed Circuit Board (PCB) and Vacuum Florescent Displaying (VFD) boards. In the VFD board inspection system, the LIF component learns inspection features from a VFD board and its displaying patterns. In the PCB board inspection system, the LIF learns the inspection features from the CAD file of a PCB board. In both systems, the LIF component also incorporates interactive learning to make the inspection system more powerful and efficient. The VFD system has been deployed successfully in three different manufacturing companies and the PCB inspection system is the process of being deployed in a manufacturing plant.

Citation Download Citation

Yi Lu, Tie Qi Chen, Jie Chen, Jian Zhang, and Anthony Tisler "Machine vision systems using machine learning for industrial product inspection", Proc. SPIE 4567, Machine Vision and Three-Dimensional Imaging Systems for Inspection and Metrology II, (11 February 2002); https://doi.org/10.1117/12.455253

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