Welding Seam Classification in the Automotive Industry using Deep Learning Algorithms
- Resource Type
- Conference
- Authors
- El Hachem, Charbel; Perrot, Gilles; Painvin, Loic; Ernst-Desmulier, Jean-Baptiste; Couturier, Raphael
- Source
- 2021 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT) Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT), 2021 IEEE International Conference on. :235-240 Jul, 2021
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Deep learning
Industries
Visualization
Welding
Inspection
Feature extraction
Classification algorithms
Deep Learning
Image Classification
Convolutional Neural Network
Data Augmentation
Industry Automation
- Language
Welding seam inspection is key process in the automotive industry and should guarantee the quality required by the client. Visual inspection is achieved by the operator who checks each part manually, making the reliability highly improvable. That's why automating the visual inspection is needed in today's production process. Collecting data from inside the plant may not provide a balanced number of images between good welding seams and bad welding seams. In this article, we will compare a standard deep learning algorithm applied on raw data with data augmentation approaches. Our target is to reach an accuracy of 97 % on the defected reference parts. This target is reached on some welds, while it remains a challenge on other welds.