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Digital Twins in Additive Manufacturing: A Systematic Review

Md Manjurul Ahsan, Yingtao Liu, Shivakumar Raman, Zahed Siddique

TL;DR

This work aims to provide a comprehensive overview of DTs in AM by addressing the following four research questions: What are the key types of DTs used in AM and their specific applications, and how are DTs integrated with Industry 4.0 technologies?

Abstract

Digital Twins (DTs) are becoming popular in Additive Manufacturing (AM) due to their ability to create virtual replicas of physical components of AM machines, which helps in real-time production monitoring. Advanced techniques such as Machine Learning (ML), Augmented Reality (AR), and simulation-based models play key roles in developing intelligent and adaptable DTs in manufacturing processes. However, questions remain regarding scalability, the integration of high-quality data, and the computational power required for real-time applications in developing DTs. Understanding the current state of DTs in AM is essential to address these challenges and fully utilize their potential in advancing AM processes. Considering this opportunity, this work aims to provide a comprehensive overview of DTs in AM by addressing the following four research questions: (1) What are the key types of DTs used in AM and their specific applications? (2) What are the recent developments and implementations of DTs? (3) How are DTs employed in process improvement and hybrid manufacturing? (4) How are DTs integrated with Industry 4.0 technologies? By discussing current applications and techniques, we aim to offer a better understanding and potential future research directions for researchers and practitioners in AM and DTs.

Digital Twins in Additive Manufacturing: A Systematic Review

TL;DR

This work aims to provide a comprehensive overview of DTs in AM by addressing the following four research questions: What are the key types of DTs used in AM and their specific applications, and how are DTs integrated with Industry 4.0 technologies?

Abstract

Digital Twins (DTs) are becoming popular in Additive Manufacturing (AM) due to their ability to create virtual replicas of physical components of AM machines, which helps in real-time production monitoring. Advanced techniques such as Machine Learning (ML), Augmented Reality (AR), and simulation-based models play key roles in developing intelligent and adaptable DTs in manufacturing processes. However, questions remain regarding scalability, the integration of high-quality data, and the computational power required for real-time applications in developing DTs. Understanding the current state of DTs in AM is essential to address these challenges and fully utilize their potential in advancing AM processes. Considering this opportunity, this work aims to provide a comprehensive overview of DTs in AM by addressing the following four research questions: (1) What are the key types of DTs used in AM and their specific applications? (2) What are the recent developments and implementations of DTs? (3) How are DTs employed in process improvement and hybrid manufacturing? (4) How are DTs integrated with Industry 4.0 technologies? By discussing current applications and techniques, we aim to offer a better understanding and potential future research directions for researchers and practitioners in AM and DTs.
Paper Structure (14 sections, 11 equations, 11 figures, 2 tables)

This paper contains 14 sections, 11 equations, 11 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Motivation and uniqueness of this survey
  3. Search strategy
  4. DTs in AM
  5. Steps to Implement DTs in AM
  6. DTs with Artificial Intelligence (AI)
  7. Development and Implementation of DTs
  8. Process Improvement and Hybrid Manufacturing
  9. Integration with Industry 4.0 Technologies
  10. Discussions
  11. Current Techniques Used and Feasibility
  12. Challenges
  13. Future Scope
  14. Conclusion

Figures (11)

  • Figure 1: The multiscale nature of the powder-bed metal AM process, showing various properties and interactions across different length and time scales ladani2021additive.
  • Figure 2: Different types of AM techniques francis2018additive.
  • Figure 3: Different stages of the AM process in bio-material domains tarfaoui2020additive.
  • Figure 4: An efficient product data management framework employs a multi-layered structure within a DT. This approach uses an iterative loop, allowing developers to continuously refine and enhance the model throughout different development stages pang2021developing.
  • Figure 5: DTs in AM: a framework based on the reference literature; Digital Twins in AM (DTAM), Digital Twins in Industry Technologies (DIT), Process Improvement and Hybrid Manufacturing (PIHM), and Integration of Industry Technologies (IIT), Digital Part File (DPF), Augmented Reality (AR), Time-Driven Activity-Based Costing (TDABC), Adaptive Online Simulation Models (AOSM).
  • ...and 6 more figures