Journal
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume 59, Issue 16, Pages 4826-4835Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2021.1891318
Keywords
Smart manufacturing; cold forging; process monitoring; convolutional neural network; deep learning
Categories
Funding
- High-Potential Individuals Global Training Program of Institute for Information and Communication Technology Planning and Evaluation (IITP) [2019-0-01589]
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The study investigated the feasibility of using deep learning techniques for fault detection in industrial cold forging. Vibration data collected was classified using a Convolutional Neural Network, showing potential for fault detection but requiring further validation.
The feasibility of using deep learning techniques in industrial cold forging for fault detection was investigated. In this work, vibration data were collected from an industrial setting to detect machine conditions resulting in defective products (faults). After collecting data from several commonly encountered faults, a Convolutional Neural Network classifier detected fault conditions with 99.02% accuracy and further classified each fault with 92.66% accuracy. A decision tree (DT) model was also used in an attempt to detect and classify faults using time domain features. The model was able to detect faults with 92.5% accuracy but was unable to classify them. In addition, DT feature importance analysis was performed to understand how various faults impacted the machine signal for future refinement of the proposed system. The results suggest that the proposed deep learning method has the potential to detect faults in cold forging, but future work is required to validate the method.
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