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

Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks