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Robust Detection of Defective Parts Using Pattern Matching for Online Machine Vision System

  • Namita Singh
  • Abhishek Jaju
  • Sanjeev SharmaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1022)

Abstract

Reliable detection of defective parts is an essential step for ensuring high-quality assurance standards. This requirement is of primary importance for online vision-based automated pellet stacking system. During nuclear fuel pin manufacturing, the image of a single row (consecutively placed components with no gap) is processed and analyzed to extract meaningful edges. Generally, these edges follow a regular pattern; however, the presence of surface cracks and chips can alter this pattern. In this paper, we formalize the detection of defective parts as a pattern matching problem. Three different patterns are proposed and evaluated for sensitivity, specificity, and accuracy. An experiment performed with the proposed pattern matching techniques show that multi-pattern matching is the most effective method for identifying defective parts.

Keywords

Multi-pattern matching Aho–Corasick algorithm Finite state automata Fuel pellet Stack Defects 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Division of Remote Handling and RoboticsBhabha Atomic Research CentreMumbaiIndia

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