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Cited by 13Industrial Vision Systems and Defect DetectionFault Detection and Control SystemsManagement and Optimization Techniques

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

Poka Yoke system based on image analysis and object recognition

Open access
Belu, N, Ionescu, L M, Misztal, A, Mazăre, A·2015·IOP Conference Series: Materials Science and Engineering
DOI 10.1088/1757-899x/95/1/012138
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Abstract

Poka Yoke is a method of quality management which is related to prevent faults from arising during production processes. It deals with "fail-sating" or "mistake-proofing". The Poka-yoke concept was generated and developed by Shigeo Shingo for the Toyota Production System. Poka Yoke is used in many fields, especially in monitoring production processes. In many cases, identifying faults in a production process involves a higher cost than necessary cost of disposal. Usually, poke yoke solutions are based on multiple sensors that identify some nonconformities. This means the presence of different equipment (mechanical, electronic) on production line. As a consequence, coupled with the fact that the method itself is an invasive, affecting the production process, would increase its price diagnostics. The bulky machines are the means by which a Poka Yoke system can be implemented become more sophisticated. In this paper we propose a solution for the Poka Yoke system based on image analysis and identification of faults. The solution consists of a module for image acquisition, mid-level processing and an object recognition module using associative memory (Hopfield network type). All are integrated into an embedded system with AD (Analog to Digital) converter and Zync 7000 (22 nm technology).

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Research works printed but not linked: 4

  1. Santos J, 2006, John Wiley& Son
  2. Butlewski M, 2014, Proceedings of the European Safety and Reliability Conference ESREL, 1101
  3. Ene A, 2013, Proceedings of the Electronics, Computers and Artificial Intelligence, 107
  4. Sadri R, 2011, Proc. Of CSIT

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Metadata

Authors
Belu, N., Ionescu, L. M., Misztal, A., & Mazăre, A.
Affiliations
  • University of Pitesti (RO)
  • Poznań University of Technology (PL)
Corresponding author
Belu, N
ORCID
Venue
IOP Conference Series: Materials Science and Engineering
Publisher
IOP Publishing
Published
2015-11-03
Volume
95
Pages
012138
ISSN
  • 1757-8981 (ISSN-L)
  • 1757-899X
DOI
10.1088/1757-899x/95/1/012138
OpenAlex
W2292368949
Cited by
13
References
8
Citations by year
2026: 1 · 2025: 1 · 2024: 1 · 2023: 5 · 2022: 2 · 2021: 1 · 2020: 1 · 2016: 1
FWCI
0.74
Type
conference paper
Language
en
Open access
Licence
Locations
IOP Conference Series Materials Science and Engineering (publishedVersion, cc-by)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
  • Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
  • Social Sciences › Business, Management and Accounting › Strategy and Management › Management and Optimization Techniques
Keywords
poka-yoke, fault detection, image analysis, object recognition, Hopfield neural network, associative memory, embedded systems, industrial process monitoring

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