In this article, we discuss the backgrounds and technical details about several smart manufacturing projects in a tier-one electronics manufacturing facility. We devise a process to manage logistic forecast and inventory preparation for electronic parts using historical data and a recurrent neural network to achieve significant improvement over current methods. We present a system for automatically qualifying laptop software for mass production through computer vision and automation technology. The result is a reliable system that can save hundreds of man-years in the qualification process. Finally, we create a deep learning-based algorithm for visual inspection of product appearances, which requires significantly less defect training data compared to traditional approaches. For production needs, we design an automatic optical inspection machine suitable for our algorithm and process. We also discuss the issues for data collection and enabling smart manufacturing projects in a factory setting, where the projects operate on a delicate balance between process innovations and cost-saving measures.

A number of vision-based methods for detecting laser-induced defects on optical components have been implemented to replace the time-consuming manual inspection. While deep-learning-based methods have achieved state-of-the-art performances in many visual recognition tasks, their success often hinges on the availability of a large number of labeled training sets. In this paper, we propose a surface defect detection method based on image segmentation with a U-shaped convolutional network (U-Net). The designed network was trained on paired sets of online and offline images of optics from a large laser facility. We show in our experimental evaluation that our approach can accurately locate laser-induced defects on the optics in real time. The main advantage of the proposed method is that the network can be trained end to end on small samples, without the requirement for manual labeling or manual feature extraction. The approach can be applied to the daily inspection and maintenance of optical components in large laser facilities.

In this work, we approach the possibilities of infrared thermography for non-destructive testing of metallic materials. We show that the passive alternative of the method makes it possible to improve detection of surface defects existing in deep-drawn parts and visible to the naked eye. We then show that active infrared thermography, in particular coupled with induction excitation, can replace in certain cases the magnetic particle inspection, allowing the detection of very fine defects in forged parts. Finally, the study of a stepped block confirms the possibilities of active infrared thermography to detect defects not open to the surface and underlying defects.