review-article

Directed Energy Deposition via Artificial Intelligence-Enabled Approaches

Authors:

Utkarsh Chadha,

Senthil Kumaran Selvaraj,

Aakrit Sharma Lamsal,

Yashwanth Maddini,

Abhishek Krishna Ravinuthala,

Bhawana Choudhary,

Anirudh Mishra,

Deepesh Padala,

Addisalem Adefris,

Dhanalakshmi S Academic Editor:

Yu ZhouAuthors Info & Claims

Complexity, Volume 2022

https://doi.org/10.1155/2022/2767371

Published: 01 January 2022 Publication History

Abstract

Additive manufacturing (AM) has been gaining pace, replacing traditional manufacturing methods. Moreover, artificial intelligence and machine learning implementation has increased for further applications and advancements. This review extensively follows all the research work and the contemporary signs of progress in the directed energy deposition (DED) process. All types of DED systems, feed materials, energy sources, and shielding gases used in this process are also analyzed in detail. Implementing artificial intelligence (AI) in the DED process to make the process less human-dependent and control the complicated aspects has been rigorously reviewed. Various AI techniques like neural networks, gradient boosted decision trees, support vector machines, and Gaussian process techniques can achieve the desired aim. These models implemented in the DED process have been trained for high-precision products and superior quality monitoring.

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Cited By

Chadha USelvaraj SAbraham AKhanna MMishra ASachdeva IKashyap SDev SSwatish RJoshi AAnand SAdefris ALokesh Kumar RKaliappan JDhanalakshmi S(2023)Powder Bed Fusion via Machine Learning-Enabled ApproachesComplexity10.1155/2023/94817902023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/9481790

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cover image Complexity

Complexity Volume 2022, Issue

2022

8840 pages

ISSN:1076-2787

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Copyright © 2022 Utkarsh Chadha et al.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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United States

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Published: 01 January 2022

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Chadha USelvaraj SAbraham AKhanna MMishra ASachdeva IKashyap SDev SSwatish RJoshi AAnand SAdefris ALokesh Kumar RKaliappan JDhanalakshmi S(2023)Powder Bed Fusion via Machine Learning-Enabled ApproachesComplexity10.1155/2023/94817902023Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1155/2023/9481790

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Utkarsh Chadha

Department of Manufacturing EngineeringSchool of Mechanical Engineering (SMEC)Vellore Institute of Technology (VIT)Vellore 632014Tamil NaduIndiavit.ac.in

https://orcid.org/0000-0002-5044-3761

Senthil Kumaran Selvaraj

Department of Manufacturing EngineeringSchool of Mechanical Engineering (SMEC)Vellore Institute of Technology (VIT)Vellore 632014Tamil NaduIndiavit.ac.in

https://orcid.org/0000-0001-9994-9424

Aakrit Sharma Lamsal

School of Computer Science and Engineering (SCOPE)Vellore Institute of Technology (VIT)Vellore 632014Tamil NaduIndiavit.ac.in

https://orcid.org/0000-0003-3105-8056

Yashwanth Maddini

Department of Manufacturing EngineeringSchool of Mechanical Engineering (SMEC)Vellore Institute of Technology (VIT)Vellore 632014Tamil NaduIndiavit.ac.in

Abhishek Krishna Ravinuthala

Department of Manufacturing EngineeringSchool of Mechanical Engineering (SMEC)Vellore Institute of Technology (VIT)Vellore 632014Tamil NaduIndiavit.ac.in

https://orcid.org/0000-0001-8496-8411

Bhawana Choudhary

School of Computer Science and Engineering (SCOPE)Vellore Institute of Technology (VIT)Vellore 632014Tamil NaduIndiavit.ac.in

https://orcid.org/0000-0002-3047-6435

Anirudh Mishra

School of Computer Science and Engineering (SCOPE)Vellore Institute of Technology (VIT)Vellore 632014Tamil NaduIndiavit.ac.in

https://orcid.org/0000-0001-8214-6203

Deepesh Padala

School of Electronics Engineering (SENSE)Vellore Institute of TechnologyVellore 632014Tamil NaduIndiavit.ac.in

https://orcid.org/0000-0001-9550-1155

Shashank M

School of Electronics Engineering (SENSE)Vellore Institute of TechnologyVellore 632014Tamil NaduIndiavit.ac.in

https://orcid.org/0000-0003-1948-8790

Vedang Lahoti

Department of Manufacturing EngineeringSchool of Mechanical Engineering (SMEC)Vellore Institute of Technology (VIT)Vellore 632014Tamil NaduIndiavit.ac.in

https://orcid.org/0000-0002-9550-5145

Addisalem Adefris

School of Mechanical and Automotive EngineeringCollege of Engineering and TechnologyDilla UniversityP.O. Box 419DillaEthiopiadu.edu.et

https://orcid.org/0000-0002-7097-2353

Dhanalakshmi S

Combat Vehicles Research & Development Establishment (CVRDE)Defence Research & Development Organization (DRDO)Ministry of Defence, Government of IndiaAvadiChennai 600054Tamil NaduIndia

Yu Zhou

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