Academic Journal
Perspectives of using machine learning in laser powder bed fusion for metal additive manufacturing
| العنوان: | Perspectives of using machine learning in laser powder bed fusion for metal additive manufacturing |
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| المؤلفون: | S. L. Sing, C. N. Kuo, C. T. Shih, C. C. Ho, C. K. Chua |
| المصدر: | Virtual and Physical Prototyping, Vol 16, Iss 3, Pp 372-386 (2021) |
| بيانات النشر: | Taylor & Francis Group, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Science LCC:Manufactures |
| مصطلحات موضوعية: | additive manufacturing, 3d printing, powder bed fusion, selective laser melting, artificial intelligence, machine learning, Science, Manufactures, TS1-2301 |
| الوصف: | The adoption of laser powder bed fusion (L-PBF) for metals by the industry has been limited despite the significant progress made in the development of the process chain. One of the key obstacles is the inconsistency of the parts obtained from L-PBF. Due to its complexity, there are many potential fluctuations that can occur within the process chain which can lead to quality inconsistency in L-PBF parts. Machine learning (ML) has the possibility to overcome this obstacle by utilising datasets obtained at various stages of the L-PBF process chain. In this perspective article, the integration of ML into the different stages of L-PBF process chain, which potentially lead to better quality control, is explored. Prior to L-PBF, ML can be used for part designs and file preparation. Then, ML algorithms can be applied in the process parameter optimisation and in situ monitoring. Finally, ML can also be integrated into the post-processing. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1745-2759 1745-2767 17452759 |
| Relation: | https://doaj.org/toc/1745-2759; https://doaj.org/toc/1745-2767 |
| DOI: | 10.1080/17452759.2021.1944229 |
| URL الوصول: | https://doaj.org/article/1ce5d7cdefee439b968158c5247c8ad5 |
| رقم الانضمام: | edsdoj.1ce5d7cdefee439b968158c5247c8ad5 |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 17452759 17452767 |
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| DOI: | 10.1080/17452759.2021.1944229 |