Automatic detection of faults in industrial production of sandwich panels using Deep Learning techniques

Abstract

Abstract The use of technologies like artificial intelligence can drive productivity growth, efficiency and innovation. The goal of this study is to develop an anomaly detection method for locating flaws on the surface of sandwich panels using YOLOv5. The proposed algorithm extracts information locally from an image through a prediction system that creates bounding boxes and determines whether the sandwich panel surface contains flaws. It attempts to reject or accept a product based on quality levels specified in the standard. To evaluate the proposed method, a comparison was made with a sandwich panel damage detection method based on a convolutional neural network and methods based on thresholding. The findings show that the proposed method, which is based on an object detector, is more accurate than the alternatives. The characteristics of the model, which can reject or accept a product according to the standard and limit allowable manufacturing flaws to obtain a quality product, also enable this system to improve industrial standards for producing sandwich panels while increasing speed.