2 research outputs found
Big data, machine learning, and digital twin assisted additive manufacturing: a review
This is the final version. Available from Elsevier via the DOI in this record. Data availability:
The data supporting the findings of this study are available within the article.Additive manufacturing (AM) has undergone significant development over the past decades, resulting in vast
amounts of data that carry valuable information. Numerous research studies have been conducted to extract
insights from AM data and utilize it for optimizing various aspects such as the manufacturing process, supply
chain, and real-time monitoring. Data integration into proposed digital twin frameworks and the application of
machine learning techniques is expected to play pivotal roles in advancing AM in the future. In this paper, we
provide an overview of machine learning and digital twin-assisted AM. On one hand, we discuss the research
domain and highlight the machine-learning methods utilized in this field, including material analysis, design
optimization, process parameter optimization, defect detection and monitoring, and sustainability. On the other
hand, we examine the status of digital twin-assisted AM from the current research status to the technical approach
and offer insights into future developments and perspectives in this area. This review paper aims to examine
present research and development in the convergence of big data, machine learning, and digital twin-assisted
AM. Although there are numerous review papers on machine learning for additive manufacturing and others
on digital twins for AM, no existing paper has considered how these concepts are intrinsically connected and
interrelated. Our paper is the first to integrate the three concepts big data, machine learning, and digital twins
and propose a cohesive framework for how they can work together to improve the efficiency, accuracy, and
sustainability of AM processes. By exploring latest advancements and applications within these domains, our
objective is to emphasize the potential advantages and future possibilities associated with integration of these
technologies in AM.Research Grants Council, Hong Kong Special Administrative Region, ChinaChinese University of Hong Kon