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Cited by 339

The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.

A digital twin for rapid qualification of 3D printed metallic components

Open access
Mukherjee, T., DebRoy, T.·2019·Applied Materials Today

Abstract

Abstract The customized production of complex components by 3D printing has been hailed as a potentially transformative tool in manufacturing with important applications in health care, automotive and aerospace industries. However, after about a quarter of a century of research and development, only a handful of commercial alloys can be printed and the market value of all 3D printed products now amounts to a negligible portion of the manufacturing economy. This difficulty is attributable to a remarkable diversity in structure and properties of the printed components and susceptibility to defects. In addition, the current practice of qualifying components by prolonged trial and error with expensive printing equipment and feed stock material confine the printed products to a niche market where the high product cost and the delay in the qualification are not critical factors. Here we explain how a digital twin or a digital replica of the printing machine will reduce the number of trial and error tests to obtain desired product attributes and reduce the time required for part qualification to make the printed components cost effective. It is shown that a comprehensive digital twin of 3D printing machine consisting of mechanistic, control and statistical models of 3D printing, machine learning and big data can reduce the volume of trial and error testing, reduce defects and shorten time between the design and production.

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Metadata

Authors
Mukherjee, T., & DebRoy, T.
Venue
Applied Materials Today
Publisher
Elsevier BV
Published
2019-03
Volume
14
Pages
59-65
ISSN
2352-9407 (ISSN-L)
OpenAlex
W2900615132
Cited by
339
References
45
FWCI
13.91
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: A digital twin for rapid qualification of 3D printed metallic components. https://app.cochat.ai/papers/10.1016/j.apmt.2018.11.003
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Mukherjee, T., & DebRoy, T. (2019). A digital twin for rapid qualification of 3D printed metallic components. Applied Materials Today. https://doi.org/10.1016/j.apmt.2018.11.003

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.