Development of a 3D printer based digital twin for smart manufacturing

• Main Author: Sivabalan, Abinav Shankar
• Other Authors: Chen Chun-Hsien
• Format: Theses and Dissertations
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Manufacturing; DRNTU::Engineering::Mechanical engineering
• Online Access: http://hdl.handle.net/10356/76809
• Institution: Nanyang Technological University

Smart manufacturing leverages massive in-context data from manufacturing systems to bestow problem solving and decision-making intelligence to the systems for flexible and reliable manufacturing. Cyber-Physical Systems (CPS) play a key role in digitizing manufacturing systems, collecting data and integrating multiple systems together for collocative work. Among different levels of smartness and connectedness of CPS, Digital Twin, an exact digital copy of a physical object or system including its properties and its relationship with the environment, has a significant rank. A digital twin constantly synchronises with its physical system and provides real time ultra-realistic simulations of the system and offers ubiquitous control over the system. It also delivers ambient intelligence providing better collaboration amongst the connected systems for improved efficiency. Despite its great advantages, only few works have been discussed about the concept, let alone a generic manner to establish the digital twin for smart manufacturing. Aiming to fill the gap, this project aims to create a generic framework for digital twin development that can be used as a reference for future digital twin developments. A methodology to create unique digital twins from a knowledge-source based reference model, imparting physical attributes to the twin for exact digital replication and commercializing them as a service are also presented. Furthermore, to validate the proposed framework, a 3D printer based digital twin was developed in an Internet of Things (IoT) enabled CAD environment as a case study. The digital twin creation process and the advantages of its implementation for smart manufacturing are elaborated. At last, discussions and future works in other potential fields are also highlighted to provide insightful knowledge to both academia and industries.