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An adaptable automated visual inspection scheme through online learning

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Abstract

In the manufacturing industry, there is a growing need for an adaptable automated visual inspection (AVI) system that can be used to perform different inspection tasks without excessive retuning or retraining efforts. This paper presents an automated visual inspection scheme to improve the adaptability of an AVI system. In doing so, we propose the design of an adaptable inspection model composed of two sub-models: one for localizing the region of useful features and the other for defect classification. The localization sub-model contains invariant features common to all inspection samples. Through an edge-based geometric template-matching process, the localization sub-model is used to locate a verification region containing the subject of inspection such as a clip or a screw in an assembly piece. Through principal component analysis (PCA), the verification sub-model is constructed based on the reconstruction error distribution of non-defective samples. Consequently, this sub-model can be used to identify defective samples. In addition, an efficient online training algorithm is proposed for the construction of the verification sub-model during system operation. This algorithm allows minimum manual inspection effort while ensuring model training sufficiency. Through case study, the proposed AVI scheme demonstrates its capability of self-tuning while inspecting different parts or under different operating conditions in an assembly process. The feature of adaptability will help increase the benefit and functionality of an AVI technique to the manufacturing industry.

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Authors and Affiliations

  1. Department of Mechanical and Manufacturing Engineering, University of Calgary, Calgary, AB, Canada, T2N1N4

    Jun Sun & Qiao Sun

  2. Alberta Innovates–Technology Futures, Calgary, AB, Canada, T2L2A6

    Jun Sun

  3. Department of Mechanical and Materials Engineering, Queen’s University, Kingston, ON, Canada, K7L3N6

    Brian W. Surgenor

Authors
  1. Jun Sun
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  2. Qiao Sun
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  3. Brian W. Surgenor
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Corresponding author

Correspondence to Qiao Sun.

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Sun, J., Sun, Q. & Surgenor, B.W. An adaptable automated visual inspection scheme through online learning. Int J Adv Manuf Technol 59, 655–667 (2012). https://doi.org/10.1007/s00170-011-3524-y

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  • DOI: https://doi.org/10.1007/s00170-011-3524-y

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