Boosting Defect Detection in Manufacturing using Tensor Convolutional
  Neural Networks
Pablo Martin-Ramiro Unai Sainz de la Maza Roman Orus Samuel Mugel
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.