UTILIZING MACHINE LEARNING IN INDUSTRY 4.0 FOR ADDITIVE MANUFACTURING

Abstract

The shift from traditional industrial automation to smart manufacturing systems, commonly known as Industry 4.0, has revolutionized the production landscape. This transformation is characterized by high levels of automation and the integration of information technology into manufacturing processes. Smart manufacturing systems utilize machines and robots with the ability to process information, improve production yields, provide real-time performance visualization, enable intelligent predictive maintenance, and facilitate the alignment of service providers with customer demands. While various control systems have been proposed for smart factories, challenges remain in coordinating the actions of individual machines with the broader objectives of the system. Most control systems incorporate a combination of centralized and decentralized architectures, which may not fully meet the requirements of Industry 4.0. Furthermore, there is a scarcity of information on how to manage, ensure interoperability, and control data within a smart factory. Prior research has predominantly focused on the control system and communication aspects, often overlooking the technical aspects of machine enablement in a smart factory. This paper explores the dynamic landscape of smart manufacturing, discussing the challenges and opportunities in implementing effective control systems and enabling seamless data management within Industry 4.0.