تأثير العوامل البيئية على دقة نظام فحص جودة مبني على التعلم بالنقل

Abstract

In this research, a study is introduced on the effect of several environmental factors on the performance of an already constructed quality inspection system, which was designed using a transfer learning approach based on convolutional neural networks. The system comprised two sets of layers, transferred layers set from an already trained model (DenseNet121) and a custom classification layers set. It was designed to discriminate between damaged and undamaged helical gears according to the configuration of the gear regardless to its dimensions, and the model showed good performance discriminating between the two products at ideal conditions of high-resolution images.
 So, this study aimed at testing the system performance at poor settings of lighting, background, distance and camera resolution. Experimental results implied that the system was able to show high accuracies above 90% at very bad settings and around 99% at good settings, which assures that an inspection system with good performance can be built at low costs.

Tools and Process of Defect Detection in Automated Manufacturing Systems

“…The algorithms possess the capability to scrutinize vast amounts of data and detect patterns and irregularities that could potentially signify the existence of flaws. The implementation of AI and ML algorithms can facilitate defect detection in automated manufacturing systems [15]. The utilization of artificial intelligence and machine learning algorithms enables the analysis of product images to identify surface defects, including scratches, cracks, and other imperfections.…”