SMART RNN BASED SURFACE DEFECT DETECTION IN STEEL PLATES MANUFACTURING PLANTS

Abstract

 This research introduces a model for defect detection in smart surface manufacturing processes employing a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN).  Use the suggested strategy, which looks at both time and place, to find problems with production systems as they happen.       Convolutional neural networks (CNNs) can help you uncover exact spatial patterns, while recurrent neural networks (RNNs) can help you find the order of mistakes over time. Lab tests reveal that the technology is better than older versions in terms of accuracy, precision, and reliability. This proves that it can be utilised in smart manufacturing. The model can work in real time because of latency and performance metrics. This study introduces a versatile and customisable quality control system to meet the growing requirements of Industry 4.0 and the imminent progress in intelligent manufacturing.