Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks

TL;DR

This paper presents a quantum-inspired Tensor Convolutional Neural Network that enhances defect detection in manufacturing by reducing model parameters and training time while maintaining high accuracy, outperforming human inspection.

Contribution

Introduction of a T-CNN model that significantly reduces parameters and training time without sacrificing accuracy in defect detection tasks.

Findings

T-CNN achieves similar accuracy to classical CNNs.

T-CNN uses up to 15 times fewer parameters.

T-CNN trains 4% to 19% faster than traditional CNNs.

Abstract

Defect detection is one of the most important yet challenging tasks in the quality control stage in the manufacturing sector. In this work, we introduce a Tensor Convolutional Neural Network (T-CNN) and examine its performance on a real defect detection application in one of the components of the ultrasonic sensors produced at Robert Bosch's manufacturing plants. Our quantum-inspired T-CNN operates on a reduced model parameter space to substantially improve the training speed and performance of an equivalent CNN model without sacrificing accuracy. More specifically, we demonstrate how T-CNNs are able to reach the same performance as classical CNNs as measured by quality metrics, with up to fifteen times fewer parameters and 4% to 19% faster training times. Our results demonstrate that the T-CNN greatly outperforms the results of traditional human visual inspection, providing value in a current real application in manufacturing.