Quality monitoring and fault detection on stamped parts using DCA and LDA image recognition techniques
Document Type
Conference Proceeding
Publication Date
12-1-2009
Abstract
New vision technologies provide an opportunity for fast detection and diagnosis of quality problems compared with traditional dimensional measurement techniques. This paper proposes a new use of image processing to detect quality faults using images traditionally obtained to guide manufacturing processes. The proposed method utilizes face recognition tools to eliminate the need of specific feature detection on determining out-of-specification parts. The algorithm is trained with previously classified images. New images are then classified into two groups, healthy and unhealthy. This paper proposes a method that combines Discrete Cosine Transform (DCT) with either Principal Component Analysis (PCA) or Linear Discriminant Analysis (LDA) to detect faults, such as cracks, directly from sheet metal parts. Copyright © 2008 by ASME.
Publication Title
Proceedings of the ASME International Manufacturing Science and Engineering Conference, MSEC2008
Recommended Citation
Zhao, Q.,
&
Camelio, J.
(2009).
Quality monitoring and fault detection on stamped parts using DCA and LDA image recognition techniques.
Proceedings of the ASME International Manufacturing Science and Engineering Conference, MSEC2008,
1, 495-502.
http://doi.org/10.1115/MSEC_ICMP2008-72218
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/11932