Machine Learning for Additive Manufacturing of Functionally Graded Materials

Karimzadeh, Mohammad; Basvoju, Deekshith; Vakanski, Aleksandar; Charit, Indrajit; Xu, Fei; Zhang, Xinchang

doi:10.3390/ma17153673

Machine Learning for Additive Manufacturing of Functionally Graded Materials

Journal Article · Thu Jul 25 00:00:00 EDT 2024 · Materials

DOI:https://doi.org/10.3390/ma17153673· OSTI ID:2470413

Karimzadeh, Mohammad ^[1]; Basvoju, Deekshith ^[2]; ^[2]; Charit, Indrajit ^[2]; Xu, Fei ^[3]; ^[3]

Univ. of Idaho, Moscow, ID (United States)
Univ. of Idaho, Idaho Falls, ID (United States)
Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Additive Manufacturing (AM) is a transformative manufacturing technology enabling direct fabrication of complex parts layer-by-layer from 3D modeling data. Among AM applications, the fabrication of Functionally Graded Materials (FGMs) has significant importance due to the potential to enhance component performance across several industries. FGMs are manufactured with a gradient composition transition between dissimilar materials, enabling the design of new materials with location-dependent mechanical and physical properties. This study presents a comprehensive review of published literature pertaining to the implementation of Machine Learning (ML) techniques in AM, with an emphasis on ML-based methods for optimizing FGMs fabrication processes. Through an extensive survey of the literature, this review article explores the role of ML in addressing the inherent challenges in FGMs fabrication and encompasses parameter optimization, defect detection, and real-time monitoring. The article also provides a discussion of future research directions and challenges in employing ML-based methods in the AM fabrication of FGMs.

View Accepted Manuscript (DOE)

Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 2470413

Alternate ID(s):: OSTI ID: 3012165

Journal Information:: Materials, Journal Name: Materials Journal Issue: 15 Vol. 17; ISSN MATEG9; ISSN 1996-1944

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

36 MATERIALS SCIENCE
Additive manufacturing
Chemistry
Directed energy deposition
Functionally graded materials
Machine learning
Materials Science
Metallurgy & Metallurgical Engineering
Physics

Machine Learning for Additive Manufacturing of Functionally Graded Materials

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