Intelligent Defect Detection and Classification in Manufacturing Execution Systems with IoT

In today's industrial environment, competitiveness and customer happiness depend on product quality. One potential way to improve Manufacturing Execution System (MES) fault identification and categorization is to use Internet of Things (IoT) capabilities in conjunction with sophisticated machine learning algorithms. Using Convolutional Neural Networks (CNNs) and IoT devices, this research proposes a new method for intelligent flaw detection and classification. Throughout production, the proposed system's IoT sensors gather real-time data on product performance and quality. These sensors take images of the produced parts, which are then input into CNNs to identify and classify defects automatically. CNNs have a stellar reputation for their ability to analyze images. It can learn complex patterns and characteristics from simple pixel data, improving their accuracy in defect classification. It provides real-time defect detection, allowing prompt intervention to fix problems and reduce production losses. MES's built-in fault identification and categorization capabilities allow for more efficient coordination and optimization of production processes. This paper proves the effectiveness and feasibility of the proposed method in actual production settings using case studies and empirical validation. The data shows an increase in overall production quality, a decrease in scrap and rework, and an improvement in the precision of defect identification. It supports producers' commitment to providing customers with superior-quality goods and equips them with actionable information for continual process improvement.