Boosting Defect Detection in Manufacturing using Tensor Convolutional Neural Networks
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Defect DetectionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DeepInspect: An AI-Powered Defect Detection for Manufacturing Industries
Utilizing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), our system introduces an innovative approach to defect detection in manufacturing. This techno…
Defect DetectionTinyDefectNet: Highly Compact Deep Neural Network Architecture for High-Throughput Manufacturing Visual Quality Inspection
A critical aspect in the manufacturing process is the visual quality inspection of manufactured components for defects and flaws. Human-only visual inspection can be very time-consuming and laborious, and is a significan…
Decision MakingDefect DetectionAutomatic Defect Detection of Print Fabric Using Convolutional Neural Network
Automatic defect detection is a challenging task because of the variability in texture and type of fabric defects. An effective defect detection system enables manufacturers to improve the quality of processes and produc…
Defect DetectionDetecting Manufacturing Defects in PCBs via Data-Centric Machine Learning on Solder Paste Inspection Features
Automated detection of defects in Printed Circuit Board (PCB) manufacturing using Solder Paste Inspection (SPI) and Automated Optical Inspection (AOI) machines can help improve operational efficiency and significantly re…
Solar Cell Surface Defect Inspection Based on Multispectral Convolutional Neural Network
Similar and indeterminate defect detection of solar cell surface with heterogeneous texture and complex background is a challenge of solar cell manufacturing. The traditional manufacturing process relies on human eye det…
Defect DetectionMulti-Document Summarization