Issue |
MATEC Web Conf.
Volume 388, 2023
2023 RAPDASA-RobMech-PRASA-AMI Conference Advanced Manufacturing Beyond Borders - The 24th Annual International RAPDASA Conference joined by RobMech, PRASA and AMI, hosted by CSIR and CUT
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Article Number | 10001 | |
Number of page(s) | 14 | |
Section | AM Post Processing & Qualification | |
DOI | https://doi.org/10.1051/matecconf/202338810001 | |
Published online | 15 December 2023 |
Implementing digital twinning in an additive manufacturing process chain
1 Department of Mechanical and Mechatronics Engineering, Faculty of Engineering, Built Environment and Information Technology, Central University of Technology, Free State, South Africa
2,3 Centre for Rapid Prototyping and Manufacturing, Faculty of Engineering, Built Environment and Information Technology, Central University of Technology, Free State, Bloemfontein, South Africa
* Corresponding author: kthejane@cut.ac.za
Additive manufacturing (AM) is defined as the process of joining materials layer by layer to produce a part. The Centre for Rapid Prototyping and Manufacturing (CRPM) of the Central University of Technology, Free State has successfully established an AM process chain to produce qualified medical implants. The process chain complies with the international standards ISO 13485:2016 and ISO 14971:2012, which certify the repeatability and reliability of processes. Despite the significant achievements CRPM has obtained in medical applications, efficiency in some areas can be improved through digital twinning approaches. The digital twin has comprehensive capabilities and allows real-time monitoring that can be used to improve different phases of AM processes. In this study, areas that need further improvement in the process chain, such as management information that flows between different users and processes for design and setting up AM machines, will be discussed. The study will further indicate how these areas can benefit from available advanced technologies such as augmented reality (AR) technology and digital twin data management. Further implementation of this approach is expected to confirm the potential of easy self-learning approaches and managing data more effectively for improving the AM process chain.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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