Directed Energy Deposition via Artificial Intelligence-Enabled Approaches
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 si...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/2767371 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832552954128760832 |
|---|---|
| author | Utkarsh Chadha Senthil Kumaran Selvaraj Aakrit Sharma Lamsal Yashwanth Maddini Abhishek Krishna Ravinuthala Bhawana Choudhary Anirudh Mishra Deepesh Padala Shashank M Vedang Lahoti Addisalem Adefris Dhanalakshmi S |
| author_facet | Utkarsh Chadha Senthil Kumaran Selvaraj Aakrit Sharma Lamsal Yashwanth Maddini Abhishek Krishna Ravinuthala Bhawana Choudhary Anirudh Mishra Deepesh Padala Shashank M Vedang Lahoti Addisalem Adefris Dhanalakshmi S |
| author_sort | Utkarsh Chadha |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f371b1eb48ea4cc6ba6dde1dcbb3df82 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-f371b1eb48ea4cc6ba6dde1dcbb3df822025-02-03T05:57:30ZengWileyComplexity1099-05262022-01-01202210.1155/2022/2767371Directed Energy Deposition via Artificial Intelligence-Enabled ApproachesUtkarsh Chadha0Senthil Kumaran Selvaraj1Aakrit Sharma Lamsal2Yashwanth Maddini3Abhishek Krishna Ravinuthala4Bhawana Choudhary5Anirudh Mishra6Deepesh Padala7Shashank M8Vedang Lahoti9Addisalem Adefris10Dhanalakshmi S11Department of Manufacturing EngineeringDepartment of Manufacturing EngineeringSchool of Computer Science and Engineering (SCOPE)Department of Manufacturing EngineeringDepartment of Manufacturing EngineeringSchool of Computer Science and Engineering (SCOPE)School of Computer Science and Engineering (SCOPE)School of Electronics Engineering (SENSE)School of Electronics Engineering (SENSE)Department of Manufacturing EngineeringSchool of Mechanical and Automotive EngineeringCombat Vehicles Research & Development Establishment (CVRDE)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.http://dx.doi.org/10.1155/2022/2767371 |
| spellingShingle | Utkarsh Chadha Senthil Kumaran Selvaraj Aakrit Sharma Lamsal Yashwanth Maddini Abhishek Krishna Ravinuthala Bhawana Choudhary Anirudh Mishra Deepesh Padala Shashank M Vedang Lahoti Addisalem Adefris Dhanalakshmi S Directed Energy Deposition via Artificial Intelligence-Enabled Approaches Complexity |
| title | Directed Energy Deposition via Artificial Intelligence-Enabled Approaches |
| title_full | Directed Energy Deposition via Artificial Intelligence-Enabled Approaches |
| title_fullStr | Directed Energy Deposition via Artificial Intelligence-Enabled Approaches |
| title_full_unstemmed | Directed Energy Deposition via Artificial Intelligence-Enabled Approaches |
| title_short | Directed Energy Deposition via Artificial Intelligence-Enabled Approaches |
| title_sort | directed energy deposition via artificial intelligence enabled approaches |
| url | http://dx.doi.org/10.1155/2022/2767371 |
| work_keys_str_mv | AT utkarshchadha directedenergydepositionviaartificialintelligenceenabledapproaches AT senthilkumaranselvaraj directedenergydepositionviaartificialintelligenceenabledapproaches AT aakritsharmalamsal directedenergydepositionviaartificialintelligenceenabledapproaches AT yashwanthmaddini directedenergydepositionviaartificialintelligenceenabledapproaches AT abhishekkrishnaravinuthala directedenergydepositionviaartificialintelligenceenabledapproaches AT bhawanachoudhary directedenergydepositionviaartificialintelligenceenabledapproaches AT anirudhmishra directedenergydepositionviaartificialintelligenceenabledapproaches AT deepeshpadala directedenergydepositionviaartificialintelligenceenabledapproaches AT shashankm directedenergydepositionviaartificialintelligenceenabledapproaches AT vedanglahoti directedenergydepositionviaartificialintelligenceenabledapproaches AT addisalemadefris directedenergydepositionviaartificialintelligenceenabledapproaches AT dhanalakshmis directedenergydepositionviaartificialintelligenceenabledapproaches |