Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks
This work addresses quality control challenges in manufacturing, specifically for ultrasonic sensor components at Bosch, offering a practical improvement over human inspection.
The paper tackled defect detection in manufacturing by introducing a Tensor Convolutional Neural Network (T-CNN), which achieved the same performance as classical CNNs with up to 15 times fewer parameters and 4% to 19% faster training times.
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.