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.

翻译：缺陷检测是制造业质量控制阶段最重要且最具挑战性的任务之一。本研究引入了张量卷积神经网络（T-CNN），并在博世制造工厂生产的超声波传感器组件的实际缺陷检测应用中评估其性能。受量子启发的T-CNN在约简模型参数空间上运行，显著提升了等效CNN模型的训练速度和性能，且不牺牲精度。具体而言，我们证明了T-CNN在质量指标衡量下能达到与经典CNN相同性能，同时参数数量减少高达十五倍，训练速度提升4%至19%。实验结果表明，T-CNN在性能上大幅超越传统人工视觉检测结果，在制造业当前实际应用中具有重要价值。