Surface Defect Detection using Convolutional Neural Network Model Architecture

Abstract

: With the dominance of a technical and volatile environment with enormous consumer demands, this study aims to investigate the advancements in quality assurance in the era of Industry 4.0. For better production efficiency, rapid and robust automated quality visual inspection is developing rapidly in product quality control. Deep neural network architecture is built for a real-world industrial case study to achieve automatic quality inspection built on image processing to replace the manual inspection, and its capacity to detect quality defects is analysed to minimise the errors. The primary goal is to understand the developments in quality inspection and their implications regarding finances, time expenditure, flexibility, and the model's optimum accuracy-precision compared to manual inspection. As an innovative technology, machine vision inspection offers reliable and rapid inspections and assists producers in improving quality inspection efficiency. The research provides a deep learning-based method for extended target recognition that uses visual data acquired in real-time for neural network training, validation, and predictions. The data made available by machine vision setup is utilised to evaluate error patterns and enable prompt quality inspection to achieve defect-free products. The proposed model uses all data provided by integrated technologies to find trends in data and recommend corrective measures to assure final product quality. As a result, the work in this study focuses on developing a deep convolutional neural networks (CNN) model architecture for defect identification that is also highly accurate and precise and suggests the machine vision inspection setup.