A digital twin for rapid qualification of 3D printed metallic components. (March 2019)

• Record Type: Journal Article
• Authors: Mukherjee, T.; DebRoy, T.
• Is Part Of: Applied materials today. Volume 14(2019)
• Page Start: 59
• Page End: 65
• Publication Date: 2019-03
• Subjects: Additive manufacturing -- Digital twin -- Machine learning -- Big data -- Mechanistic model; Materials science -- Periodicals; Materials -- Research -- Periodicals; 620.1105
• DOI: 10.1016/j.apmt.2018.11.003
• Languages: English
• ISSNs: 2352-9407

Abstract

Graphical abstract: Highlights: Digital twin will reduce trial and error testing and accelerate the product qualification. Twin consists of mechanistic, control & statistical models, machine learning & big data. Twin will be used to obtain desired product attributes and to make printing cost effective. 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.