A Neuro-Fuzzy Approach to a Machine Vision-based Parts Inspection Problem 2006-01-0378
This paper describes a research project whose objective is to improve the parts inspection component of the automotive manufacturing process through the application of neuro-fuzzy systems. The basic methodology is to circulate case studies of industrial inspection problems among 5 universities and challenge the researchers to find more robust analysis algorithms. This paper presents initial work on one of the case studies whose subject is the application of a machine vision-based system to identify missing fasteners in a cross-car beam.
Citation: Norman, T., Surgenor, B., Killing, J., Mechefske, C. et al., "A Neuro-Fuzzy Approach to a Machine Vision-based Parts Inspection Problem," SAE Technical Paper 2006-01-0378, 2006, https://doi.org/10.4271/2006-01-0378. Download Citation
Author(s):
Thomas Norman, Brian Surgenor, Jonathan Killing, Chris Mechefske, Gary Bone, Kudret Demirli, Qiao Sun, Fengfeng Xi
Affiliated:
Queen’s University
Pages: 9
Event:
SAE 2006 World Congress & Exhibition
ISSN:
0148-7191
e-ISSN:
2688-3627
Related Topics:
Manufacturing processes
Fasteners
Inspections
Parts
Mathematical models
Research and development
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