Knowledge-based bidirectional thermal variable modelling for directed energy deposition additive manufacturing
| Autor: | Jian Qin, Pradeeptta Taraphdar, Yongle Sun, James Wainwright, Wai Jun Lai, Shuo Feng, Jialuo Ding, Stewart Williams |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Virtual and Physical Prototyping, Vol 19, Iss 1 (2024) |
| Druh dokumentu: | article |
| ISSN: | 17452759 1745-2767 1745-2759 |
| DOI: | 10.1080/17452759.2024.2397008 |
| Popis: | Directed energy deposition additive manufacturing (DED-AM) has gained significant interest in producing large-scale metallic structural components. In this paper, a knowledge-based machine learning (ML) approach, combining both physics-based simulation and data-driven modelling, is proposed for a study on thermal variables of DED-AM. This approach enables both forward and backward predictions, which breaks down the barriers between the basic process parameters and key process attributes. Process knowledge plays a critical role to enable the prediction and enhance the accuracy in both prediction directions. The proposed ML approach successfully predicted the thermal variables of wire arc based DED-AM for forward modelling and the process parameters for backward modelling, typically within 7% errors. This approach can be further generalised as a powerful modelling tool for design, control, and evaluation of DED-AM processes regarding build geometry and properties, as well as an essential constituent element in a digital twin of a DED-AM system. |
| Databáze: | Directory of Open Access Journals |
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