Defect Recognition in High-Pressure Die-Casting Parts Using Neural Networks and Transfer Learning

Abstract

The quality control of discretely manufactured parts typically involves defect recognition activities, which are time-consuming, repetitive tasks that must be performed by highly trained and/or experienced personnel. However, in the context of the fourth industrial revolution, the pertinent goal is to automate such procedures in order to improve their accuracy and consistency, while at the same time enabling their application in near real-time. In this light, the present paper examines the applicability of popular deep neural network types, which are widely employed for object detection tasks, in recognizing surface defects of parts that are produced through a die-casting process. The data used to train the networks belong to two different datasets consisting of images that contain various types of surface defects and for two different types of parts. The first dataset is freely available and concerns pump impellers, while the second dataset has been created during the present study and concerns an automotive part. For the first dataset, Faster R-CNN and YOLOv5 detection networks were employed yielding satisfactory detection of the various surface defects, with mean average precision (mAP) equal to 0.77 and 0.65, respectively. Subsequently, using transfer learning, two additional detection networks of the same type were trained for application on the second dataset, which included considerably fewer images, achieving sufficient detection capabilities. Specifically, Faster R-CNN achieved mAP equal to 0.70, outperforming the corresponding mAP of YOLOv5 that equalled 0.60. At the same time, experiments were carried out on four different computational resources so as to investigate their performance in terms of inference times and consumed power and draw conclusions regarding the feasibility of making predictions in real time. The results show that total inference time varied from 0.82 to 6.61 s per image, depending on the computational resource used, indicating that this methodology can be integrated in a real-life industrial manufacturing system.

Study on the detection technology for inner-wall outer surface defects of the automotive ABS brake master cylinder based on BM-YOLOv8

“…Anti-lock Brake Systems (ABS) are one of the most important safety devices in automobiles [1]. The manufacturing process, influenced by factors like sand molds, sand core expansion, and inadequate mold design, can lead to a variety of defects [2], including tiny sand pits and long scratches on the inner-wall outer surface of automotive ABS brake master cylinder. Currently, the quality inspection of the inner-wall testing, classical machine vision detection, and deep learning detection [3].…”