Powder Bed Fusion via Machine Learning-Enabled Approaches
Powder bed fusion (PBF) applies to various metallic materials used in the metal printing process of building a wide range of complex parts compared to other AM technologies. PBF process has several variants such as DMLS (direct metal laser sintering), EBM (electron beam melting), SHS (selective heat...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi-Wiley
2023-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2023/9481790 |
| _version_ | 1797831780144775168 |
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| author | Utkarsh Chadha Senthil Kumaran Selvaraj Abel Saji Abraham Mayank Khanna Anirudh Mishra Isha Sachdeva Swati Kashyap S. Jithin Dev R. Srii Swatish Ayushma Joshi Simar Kaur Anand Addisalem Adefris R. Lokesh Kumar Jayakumar Kaliappan S. Dhanalakshmi |
| author_facet | Utkarsh Chadha Senthil Kumaran Selvaraj Abel Saji Abraham Mayank Khanna Anirudh Mishra Isha Sachdeva Swati Kashyap S. Jithin Dev R. Srii Swatish Ayushma Joshi Simar Kaur Anand Addisalem Adefris R. Lokesh Kumar Jayakumar Kaliappan S. Dhanalakshmi |
| author_sort | Utkarsh Chadha |
| collection | DOAJ |
| description | Powder bed fusion (PBF) applies to various metallic materials used in the metal printing process of building a wide range of complex parts compared to other AM technologies. PBF process has several variants such as DMLS (direct metal laser sintering), EBM (electron beam melting), SHS (selective heat sintering), SLM (selective laser melting), and SLS (selective laser sintering). For PBF to reach its maximum potential, machine learning (ML) algorithms are used with suitable materials to achieve goals cost-effectively. Various applications of neural networks, including ANNs, CNNs, RNNs, and other popular techniques such as KNN, SVM, and GP were reviewed, and future challenges were discussed. Some special-purpose algorithms were listed as follows: GAN, SeDANN, SCNN, K-means, PCA, etc. This review presents the evolution, current status, challenges, and prospects of these technologies in terms of material, features, process parameters, applications, advantages, disadvantages, etc., to explain their significance and provide an in-depth understanding of the same. |
| first_indexed | 2024-04-09T13:58:16Z |
| format | Article |
| id | doaj.art-d5dd2637fb0f4fef8508f239974efba0 |
| institution | Directory Open Access Journal |
| issn | 1099-0526 |
| language | English |
| last_indexed | 2024-04-09T13:58:16Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj.art-d5dd2637fb0f4fef8508f239974efba02023-05-08T00:25:40ZengHindawi-WileyComplexity1099-05262023-01-01202310.1155/2023/9481790Powder Bed Fusion via Machine Learning-Enabled ApproachesUtkarsh Chadha0Senthil Kumaran Selvaraj1Abel Saji Abraham2Mayank Khanna3Anirudh Mishra4Isha Sachdeva5Swati Kashyap6S. Jithin Dev7R. Srii Swatish8Ayushma Joshi9Simar Kaur Anand10Addisalem Adefris11R. Lokesh Kumar12Jayakumar Kaliappan13S. Dhanalakshmi14Department of Materials Science and EngineeringDepartment of Manufacturing EngineeringDepartment of Manufacturing EngineeringDepartment of Manufacturing EngineeringSchool of Computer Science and Engineering (SCOPE)School of Information Technology & Engineering (SITE)School of Electronics Engineering (SENSE)Department of Manufacturing EngineeringDepartment of Manufacturing EngineeringSchool of Computer Science and Engineering (SCOPE)School of Computer Science and Engineering (SCOPE)School of Mechanical and Automotive EngineeringSchool of Computer Science and Engineering (SCOPE)School of Computer Science and Engineering (SCOPE)Combat Vehicles Research & Development Establishment (CVRDE)Powder bed fusion (PBF) applies to various metallic materials used in the metal printing process of building a wide range of complex parts compared to other AM technologies. PBF process has several variants such as DMLS (direct metal laser sintering), EBM (electron beam melting), SHS (selective heat sintering), SLM (selective laser melting), and SLS (selective laser sintering). For PBF to reach its maximum potential, machine learning (ML) algorithms are used with suitable materials to achieve goals cost-effectively. Various applications of neural networks, including ANNs, CNNs, RNNs, and other popular techniques such as KNN, SVM, and GP were reviewed, and future challenges were discussed. Some special-purpose algorithms were listed as follows: GAN, SeDANN, SCNN, K-means, PCA, etc. This review presents the evolution, current status, challenges, and prospects of these technologies in terms of material, features, process parameters, applications, advantages, disadvantages, etc., to explain their significance and provide an in-depth understanding of the same.http://dx.doi.org/10.1155/2023/9481790 |
| spellingShingle | Utkarsh Chadha Senthil Kumaran Selvaraj Abel Saji Abraham Mayank Khanna Anirudh Mishra Isha Sachdeva Swati Kashyap S. Jithin Dev R. Srii Swatish Ayushma Joshi Simar Kaur Anand Addisalem Adefris R. Lokesh Kumar Jayakumar Kaliappan S. Dhanalakshmi Powder Bed Fusion via Machine Learning-Enabled Approaches Complexity |
| title | Powder Bed Fusion via Machine Learning-Enabled Approaches |
| title_full | Powder Bed Fusion via Machine Learning-Enabled Approaches |
| title_fullStr | Powder Bed Fusion via Machine Learning-Enabled Approaches |
| title_full_unstemmed | Powder Bed Fusion via Machine Learning-Enabled Approaches |
| title_short | Powder Bed Fusion via Machine Learning-Enabled Approaches |
| title_sort | powder bed fusion via machine learning enabled approaches |
| url | http://dx.doi.org/10.1155/2023/9481790 |
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