Automatic quality inspection of percussion cap mass production by means of 3D machine vision and machine learning techniques

  • A. Tellaeche*
  • , R. Arana
  • , A. Ibarguren
  • , J. M. Martínez-Otzeta
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

The exhaustive quality control is becoming very important in the world's globalized market. One of these examples where quality control becomes critical is the percussion cap mass production. These elements must achieve a minimum tolerance deviation in their fabrication. This paper outlines a machine vision development using a 3D camera for the inspection of the whole production of percussion caps. This system presents multiple problems, such as metallic reflections in the percussion caps, high speed movement of the system and mechanical errors and irregularities in percussion cap placement. Due to these problems, it is impossible to solve the problem by traditional image processing methods, and hence, machine learning algorithms have been tested to provide a feasible classification of the possible errors present in the percussion caps.

Original languageEnglish
Title of host publicationHybrid Artificial Intelligence Systems - 5th International Conference, HAIS 2010, Proceedings
Pages270-277
Number of pages8
EditionPART 1
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010 - San Sebastian, Spain
Duration: 23 Jun 201025 Jun 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume6076 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2010
Country/TerritorySpain
CitySan Sebastian
Period23/06/1025/06/10

Keywords

  • 3D imaging
  • high speed inspection
  • machine learning classifiers

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