Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866929328332210176 |
|---|---|
| author | Martin-Ramiro, Pablo de la Maza, Unai Sainz Singh, Sukhbinder Orus, Roman Mugel, Samuel |
| author_facet | Martin-Ramiro, Pablo de la Maza, Unai Sainz Singh, Sukhbinder Orus, Roman Mugel, Samuel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_01373 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks Martin-Ramiro, Pablo de la Maza, Unai Sainz Singh, Sukhbinder Orus, Roman Mugel, Samuel Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Quantum Physics 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. |
| title | Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Quantum Physics |
| url | https://arxiv.org/abs/2401.01373 |