Deep learning-based defect inspection in sheet metal stamping parts

Singh, Aru Ranjan; Bashford-Rogers, Thomas; Hazra, Sumit; Debattista, Kurt

Abstract

Defect inspection is a crucial step in sheet metal stampingmanufacturing. However, current inspection methods largely consist of visual inspection by trained operatives but are unreliable and prone to error. Computer vision techniques have the potential advantage of utilising low cost hardware to enable accurate classification of defects particularly through using techniques such as deep learning. Currently, the use of convolutional neural networks (CNN) is one of the best methods in the field of computer vision for classification tasks. Despite the advantages, vision-based deep learningmodels for detecting defects in sheet material are currently limited to flat sheet materials and certain classes of surface defects such as scratches and delamination. This research proposes a practical deep learningapproach for classification of cracks in realistically formed sheet metal stampingcomponents and suggests a route towards reliable and automated inspections in sheet metal stamping. This study used ResNet18, a state-of-the-art deep learningmodel to classify split defects in “Nakajima” stamped components. The model was able to achieve a 99.9 % accuracy on validation set, which implies that this technique could be suitable for automated defect detection on stamped metal parts.

• Presentation Conference Type: Conference Paper (published)
• Conference Name: NUMISHEET 2022 12th International Conference and Workshop on Numerical Simulation of 3D Sheet Metal Forming Processes
• Start Date: Jul 10, 2022
• End Date: Jul 14, 2022
• Acceptance Date: Apr 7, 2022
• Online Publication Date: Jul 1, 2022
• Publication Date: 2022
• Deposit Date: Sep 21, 2022
• Publicly Available Date: Jul 2, 2024
• Publisher: Springer
• Pages: 411-419
• Series Title: The Minerals, Metals & Materials Series
• Series ISSN: 2367-1181
• Book Title: NUMISHEET 2022 Proceedings of the 12th International Conference and Workshop on Numerical Simulation of 3D Sheet Metal Forming Processes
• Chapter Number: 38
• ISBN: 9783031062117; 9783031062124
• DOI: https://doi.org/10.1007/978-3-031-06212-4_38
• Keywords: Deep learning, Sheet metal stamping, Industrial inspection, Image classification
• Public URL: https://uwe-repository.worktribe.com/output/9997140
• Publisher URL: https://link.springer.com/chapter/10.1007/978-3-031-06212-4_38
• Related Public URLs: https://www.springer.com/series/15240; ; https://www.tms.org/portal/Meetings___Events/2021/NUMISHEET2021/default.aspx
• Additional Information: First Online: 1 July 2022