Anomaly detection in laser powder bed fusion using machine learning: A review
Metal Additive Manufacturing (MAM) applications are growing rapidly in high-tech industries such as biomedical and aerospace, and in many other industries including tooling, casting, automotive, oil and gas for production and prototyping. The onset of Laser Powder Bed Fusion (L-PBF) technology prove...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S259012302200473X |
| _version_ | 1811209451599822848 |
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| author | Tayyaba Sahar Muhammad Rauf Ahmar Murtaza Lehar Asip Khan Hasan Ayub Syed Muslim Jameel Inam Ul Ahad |
| author_facet | Tayyaba Sahar Muhammad Rauf Ahmar Murtaza Lehar Asip Khan Hasan Ayub Syed Muslim Jameel Inam Ul Ahad |
| author_sort | Tayyaba Sahar |
| collection | DOAJ |
| description | Metal Additive Manufacturing (MAM) applications are growing rapidly in high-tech industries such as biomedical and aerospace, and in many other industries including tooling, casting, automotive, oil and gas for production and prototyping. The onset of Laser Powder Bed Fusion (L-PBF) technology proved to be an efficient technique that can convert metal additive manufacturing into a reformed process if anomalies occurred during this process are eliminated. Industrial applications demand high accuracy and risk-free products whereas prototyping using MAM demand lower process and product development time. In order to address these challenges, Machine Learning (ML) experts and researchers are trying to adopt an efficient method for anomaly detection in L-PBF so that the MAM process can be optimized and desired final part properties can be achieved. This review provides an overview of L-PBF and outlines the ML methods used for anomaly detection in L-PBF. The paper also explains how ML methods are being used as a step forward toward enabling the real-time process control of MAM and the process can be optimized for higher accuracy, lower production time, and less material waste. Authors have a strong believe that ML techniques can reform MAM process, whereas research concerned to the anomaly detection using ML techniques is limited and needs attention.This review has been done with a hope that ML experts can easily find a direction and contribute in this field. |
| first_indexed | 2024-04-12T04:39:38Z |
| format | Article |
| id | doaj.art-03e327d18c2e459c808dc16c3540525c |
| institution | Directory Open Access Journal |
| issn | 2590-1230 |
| language | English |
| last_indexed | 2024-04-12T04:39:38Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj.art-03e327d18c2e459c808dc16c3540525c2022-12-22T03:47:42ZengElsevierResults in Engineering2590-12302023-03-0117100803Anomaly detection in laser powder bed fusion using machine learning: A reviewTayyaba Sahar0Muhammad Rauf1Ahmar Murtaza2Lehar Asip Khan3Hasan Ayub4Syed Muslim Jameel5Inam Ul Ahad6Department of Electronic Engineering, Dawood University Engineering and Technology, Karachi, PakistanDepartment of Electronic Engineering, Dawood University Engineering and Technology, Karachi, Pakistan; Corresponding author.I-Form, Advanced Manufacturing Research Centre and Advanced Processing Technology Research Centre, School of Mechanical and Manufacturing Engineering, Dublin City University, Glasnevin, Dublin 9, IrelandI-Form, Advanced Manufacturing Research Centre and Advanced Processing Technology Research Centre, School of Mechanical and Manufacturing Engineering, Dublin City University, Glasnevin, Dublin 9, IrelandI-Form, Advanced Manufacturing Research Centre and Advanced Processing Technology Research Centre, School of Mechanical and Manufacturing Engineering, Dublin City University, Glasnevin, Dublin 9, IrelandMaREI Centre, Ryan Institute, School of Engineering, National University of Ireland Galway, IrelandI-Form, Advanced Manufacturing Research Centre and Advanced Processing Technology Research Centre, School of Mechanical and Manufacturing Engineering, Dublin City University, Glasnevin, Dublin 9, IrelandMetal Additive Manufacturing (MAM) applications are growing rapidly in high-tech industries such as biomedical and aerospace, and in many other industries including tooling, casting, automotive, oil and gas for production and prototyping. The onset of Laser Powder Bed Fusion (L-PBF) technology proved to be an efficient technique that can convert metal additive manufacturing into a reformed process if anomalies occurred during this process are eliminated. Industrial applications demand high accuracy and risk-free products whereas prototyping using MAM demand lower process and product development time. In order to address these challenges, Machine Learning (ML) experts and researchers are trying to adopt an efficient method for anomaly detection in L-PBF so that the MAM process can be optimized and desired final part properties can be achieved. This review provides an overview of L-PBF and outlines the ML methods used for anomaly detection in L-PBF. The paper also explains how ML methods are being used as a step forward toward enabling the real-time process control of MAM and the process can be optimized for higher accuracy, lower production time, and less material waste. Authors have a strong believe that ML techniques can reform MAM process, whereas research concerned to the anomaly detection using ML techniques is limited and needs attention.This review has been done with a hope that ML experts can easily find a direction and contribute in this field.http://www.sciencedirect.com/science/article/pii/S259012302200473XMetal additive manufacturing (MAM)Laser powder bed fusion (L-PBF)Machine learning (ML)Process parameter optimizationAnomaly detection |
| spellingShingle | Tayyaba Sahar Muhammad Rauf Ahmar Murtaza Lehar Asip Khan Hasan Ayub Syed Muslim Jameel Inam Ul Ahad Anomaly detection in laser powder bed fusion using machine learning: A review Results in Engineering Metal additive manufacturing (MAM) Laser powder bed fusion (L-PBF) Machine learning (ML) Process parameter optimization Anomaly detection |
| title | Anomaly detection in laser powder bed fusion using machine learning: A review |
| title_full | Anomaly detection in laser powder bed fusion using machine learning: A review |
| title_fullStr | Anomaly detection in laser powder bed fusion using machine learning: A review |
| title_full_unstemmed | Anomaly detection in laser powder bed fusion using machine learning: A review |
| title_short | Anomaly detection in laser powder bed fusion using machine learning: A review |
| title_sort | anomaly detection in laser powder bed fusion using machine learning a review |
| topic | Metal additive manufacturing (MAM) Laser powder bed fusion (L-PBF) Machine learning (ML) Process parameter optimization Anomaly detection |
| url | http://www.sciencedirect.com/science/article/pii/S259012302200473X |
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