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

T. Mukherjee, Tarasankar Debroy

Research output: Contribution to journalArticle

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.

LanguageEnglish (US)
Pages59-65
Number of pages7
JournalApplied Materials Today
Volume14
DOIs
StatePublished - Mar 1 2019

Fingerprint

Printing machinery
3D printers
Industrial research
Machine components
Aerospace industry
Cost effectiveness
Artificial intelligence
Learning systems
Printing
Defects
Health care
Automotive industry
Costs
Big data
Testing

All Science Journal Classification (ASJC) codes

  • Materials Science(all)

Cite this

@article{7c7f1dcf61434ba6a072768ded39e5c6,
title = "A digital twin for rapid qualification of 3D printed metallic components",
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.",
author = "T. Mukherjee and Tarasankar Debroy",
year = "2019",
month = "3",
day = "1",
doi = "10.1016/j.apmt.2018.11.003",
language = "English (US)",
volume = "14",
pages = "59--65",
journal = "Applied Materials Today",
issn = "2352-9407",
publisher = "Elsevier BV",

}

A digital twin for rapid qualification of 3D printed metallic components. / Mukherjee, T.; Debroy, Tarasankar.

In: Applied Materials Today, Vol. 14, 01.03.2019, p. 59-65.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A digital twin for rapid qualification of 3D printed metallic components

AU - Mukherjee, T.

AU - Debroy, Tarasankar

PY - 2019/3/1

Y1 - 2019/3/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85056629776&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85056629776&partnerID=8YFLogxK

U2 - 10.1016/j.apmt.2018.11.003

DO - 10.1016/j.apmt.2018.11.003

M3 - Article

VL - 14

SP - 59

EP - 65

JO - Applied Materials Today

T2 - Applied Materials Today

JF - Applied Materials Today

SN - 2352-9407

ER -