Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks

Autor:	Martin-Ramiro, Pablo, de la Maza, Unai Sainz, Singh, Sukhbinder, Orus, Roman, Mugel, Samuel
Rok vydání:	2023
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Artificial Intelligence Computer Science - Machine Learning Quantum Physics
Druh dokumentu:	Working Paper
Popis:	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. Comment: 12 pages, 4 figures, 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2401.01373 Zobrazit plný text záznamu View this record from Arxiv