IoT Real-Time Production Monitoring and Automated Process Transformation in Smart Manufacturing

Abstract

Conventional automobile manufacturing plants involve intricate assembly, testing, and debugging processes heavily reliant on manual operations. This study aims to explore the application of industrial internet of things (IIoT) and deep learning algorithms to achieve process automation in manufacturing. Firstly, utilizing IIoT technology, OPC UA, and point cloud fitting techniques, a comprehensive modeling of most equipment and materials within the factory is conducted, constructing a digital twin (DT) model as a virtual representation of actual equipment. Subsequently, the study innovatively introduces the deep Q network algorithm, facilitating the automatic transition of the production process and improving production efficiency. Through comparison with ten baseline models, the proposed model demonstrates an improvement in production efficiency of at least four percentage points compared to other models. Experimental validation confirms the effectiveness of the proposed model in the smart factory for electric vehicle manufacturing.

An Intelligent Manufacturing Management System for Enhancing Production in Small-Scale Industries

“…These drawings contain essential information about the product being manufactured, including dimensions, materials, assembly instructions, and tolerances, which are crucial for ensuring that each component is produced accurately and fits together correctly with other parts. This distribution can be achieved through several methods, including physical paper copies, digital files sent via email or shared over a network, or through a centralized digital management system that allows for real-time access and updates [1,2]. In traditional factory production modes, the distribution of manufacturing drawings is typically carried out by production planners based on fixed rules and experience, which presents numerous issues.…”