Engineering

Transfer of learning

Exploit

Computer security

Real-time computing

Production line

Computer science

Condition monitoring

Mechanical engineering

Artificial intelligence

Deep learning

Welding

Artificial neural network

Feature extraction

Electrical engineering

IEEE Transactions on Industrial Electronics

This paper introduces an intelligent system able to perform quality control assessment in an industrial production line. Deep learning techniques are employed and proved successful in a real application for the inspection of welding defects on an assembly line of fuel injectors. Starting from state-of-the-art deep architectures and using the transfer learning technique, it is possible to train a network with about 7 million parameters using a reduced number of injector's images, obtaining an accuracy of 97.22%. The system is also configured in order to exploit new data, collected during operation, to extend the existing dataset and to improve further its performance. The developed system shows that deep neural networks can successfully perform quality inspection tasks that are usually demanded to humans.

A Smart Monitoring System for Automatic Welding Defect Detection