MODELING OF IOT-BASED ADDITIVE MANUFACTURING MACHINE’S DIGITAL TWIN FOR ERROR DETECTION

Abstract

Additive Manufacturing technology is one of the technologies that is changing the manufacturing industry. It has revealed some advantages over traditional manufacturing methods with this technology. With the advancement of information technologies, new approaches focusing on cost and improvement have begun to be adopted in the manufacturing industry. One such method is digital twin technology. A digital twin is frequently referred to as a digital replication of a physical system. Digital twins provide data and models to support the operation of design and manufacturing processes, as well as troubleshooting, diagnostics, and problem-solving. Various sensors are required to monitor the status of physical systems and transfer data to digital systems. Some of these Internet of Things-compatible sensors are already in production machines, but others can be added later. In the study, an Internet of Things-based system was proposed for the creation of digital twins using a virtual environment, and a digital twin simulation was created in order to bring the benefits of digitalization to production systems. The digital twin is modeled in the Matlab Simulink environment to perform binary classification to detect abnormal physical conditions that have the potential to disrupt the operation of the additive manufacturing machine and affect the quality of the manufacturing part. By generating a digital twin from real machine data, the proposed system will be able to detect errors.