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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.26586 |
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| _version_ | 1866908637496082432 |
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| author | Basterrech, Sebastian Shan, Shuo Adhikari, Debabrata Mohanty, Sankhya |
| author_facet | Basterrech, Sebastian Shan, Shuo Adhikari, Debabrata Mohanty, Sankhya |
| contents | In this study, we leverage a mixture model learning approach to identify defects in laser-based Additive Manufacturing (AM) processes. By incorporating physics based principles, we also ensure that the model is sensitive to meaningful physical parameter variations. The empirical evaluation was conducted by analyzing real-world data from two AM processes: Directed Energy Deposition and Laser Powder Bed Fusion. In addition, we also studied the performance of the developed framework over public datasets with different alloy type and experimental parameter information. The results show the potential of physics-guided mixture models to examine the underlying physical behavior of an AM system. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_26586 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Physics-Informed Mixture Models and Surrogate Models for Precision Additive Manufacturing Basterrech, Sebastian Shan, Shuo Adhikari, Debabrata Mohanty, Sankhya Mathematical Physics Machine Learning In this study, we leverage a mixture model learning approach to identify defects in laser-based Additive Manufacturing (AM) processes. By incorporating physics based principles, we also ensure that the model is sensitive to meaningful physical parameter variations. The empirical evaluation was conducted by analyzing real-world data from two AM processes: Directed Energy Deposition and Laser Powder Bed Fusion. In addition, we also studied the performance of the developed framework over public datasets with different alloy type and experimental parameter information. The results show the potential of physics-guided mixture models to examine the underlying physical behavior of an AM system. |
| title | Physics-Informed Mixture Models and Surrogate Models for Precision Additive Manufacturing |
| topic | Mathematical Physics Machine Learning |
| url | https://arxiv.org/abs/2510.26586 |