Digital twin-driven additive manufacturing : advancements and future prospects

P. M., Abhilash and Boban, Jibin and Ahmed, Afzaal and Luo, Xichun; (2023) Digital twin-driven additive manufacturing : advancements and future prospects. In: Hybrid Metal Additive Manufacturing. Taylor & Francis, Milton Park, Oxon., pp. 196-221. ISBN 9781003803249 (https://doi.org/10.1201/9781003406488-12)

[thumbnail of Puthanveettil-Madathil-etal-CRC-2023-Digital-twin-driven-additive-manufacturing-advancements] Text. Filename: Puthanveettil-Madathil-etal-CRC-2023-Digital-twin-driven-additive-manufacturing-advancements.pdf
Accepted Author Manuscript
Restricted to Repository staff only until 5 December 2024.
License: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 logo
Download (1MB) | Request a copy

Abstract

A digital twin (DT) is a virtual replica of a physical system with two-way data communication between the cyber and physical domains. It is an innovative concept in smart manufacturing, envisioning improved efficiency, productivity, flexibility and quality control through real-time data-driven and collaborative digital systems. DT systems are enabled through technologies such as artificial intelligence (AI), human-machine interface (HMI), simulation models, augmented reality and virtual reality, online sensing, big data analytics and the Internet of Things (IoT). Additive manufacturing (AM) is a fast-growing and revolutionary technology with immense applications in aerospace, biomedical, automobile and marine applications. Some of the fundamental challenges of AM processes are part irregularities, indispensable post-processing, unanticipated process anomalies, design and metrology disintegration and a lack of standardization. Currently, considerable time and cost are being devoted to offline metrology, defect identification and process optimization. DT-driven AM is still in its nascent stage; however, the technology has demonstrated its immense potential to transform the emerging world of AM. A DT system looks to systematically address these fundamental shortcomings through computational intelligence and real-time data communication. Key application domains include anomaly detection, online condition monitoring, feed-forward process control, intelligent post-processing and process optimization. The chapter critically analyses the current state-of-the-art of DTs in AM processes and further discusses their future prospects and research directions. The chapter highlights the capacity of a DT to broaden the acceptability of AM in various industrial applications by improving robustness and efficiency.

ORCID iDs

P. M., Abhilash ORCID logoORCID: https://orcid.org/0000-0001-5655-6196, Boban, Jibin, Ahmed, Afzaal and Luo, Xichun ORCID logoORCID: https://orcid.org/0000-0002-5024-7058;
CORE (COnnecting REpositories)