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.
- Manufacturing Engineering Division
Quality Monitoring and Fault Detection on Stamped Parts Using DCA and LDA Image Recognition Techniques
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Zhao, Q, & Camelio, JA. "Quality Monitoring and Fault Detection on Stamped Parts Using DCA and LDA Image Recognition Techniques." Proceedings of the ASME 2008 International Manufacturing Science and Engineering Conference collocated with the 3rd JSME/ASME International Conference on Materials and Processing. ASME 2008 International Manufacturing Science and Engineering Conference, Volume 1. Evanston, Illinois, USA. October 7–10, 2008. pp. 495-502. ASME. https://doi.org/10.1115/MSEC_ICMP2008-72218
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