Feature Engineering for Surrogate Models of Consolidation Degree in Additive Manufacturing
Surrogate models (SM) serve as a proxy to the physics- and experiment-based models to significantly lower the cost of prediction while providing high accuracy. Building an SM for additive manufacturing (AM) process suffers from high dimensionality of inputs when part geometry or tool-path is conside...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/14/9/2239 |
| _version_ | 1797536200861417472 |
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| author | Mriganka Roy Olga Wodo |
| author_facet | Mriganka Roy Olga Wodo |
| author_sort | Mriganka Roy |
| collection | DOAJ |
| description | Surrogate models (SM) serve as a proxy to the physics- and experiment-based models to significantly lower the cost of prediction while providing high accuracy. Building an SM for additive manufacturing (AM) process suffers from high dimensionality of inputs when part geometry or tool-path is considered in addition to the high cost of generating data from either physics-based models or experiments. This paper engineers features for a surrogate model to predict the consolidation degree in the fused filament fabrication process. Our features are informed by the physics of the underlying thermal processes and capture the characteristics of the part’s geometry and the deposition process. Our model is learned from medium-size data generated using a physics-based thermal model coupled with the polymer healing theory to determine the consolidation degree. Our results demonstrate high accuracy (>90%) of consolidation degree prediction at a low computational cost (four orders of magnitude faster than the numerical model). |
| first_indexed | 2024-03-10T11:56:16Z |
| format | Article |
| id | doaj.art-b69b3263f10540cf9cff0356755903fe |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-10T11:56:16Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-b69b3263f10540cf9cff0356755903fe2023-11-21T17:19:19ZengMDPI AGMaterials1996-19442021-04-01149223910.3390/ma14092239Feature Engineering for Surrogate Models of Consolidation Degree in Additive ManufacturingMriganka Roy0Olga Wodo1Mechanical and Aerospace Engineering Department, University at Buffalo, Buffalo, NY 14260, USAMaterials Design and Innovation Department, University at Buffalo, Buffalo, NY 14260, USASurrogate models (SM) serve as a proxy to the physics- and experiment-based models to significantly lower the cost of prediction while providing high accuracy. Building an SM for additive manufacturing (AM) process suffers from high dimensionality of inputs when part geometry or tool-path is considered in addition to the high cost of generating data from either physics-based models or experiments. This paper engineers features for a surrogate model to predict the consolidation degree in the fused filament fabrication process. Our features are informed by the physics of the underlying thermal processes and capture the characteristics of the part’s geometry and the deposition process. Our model is learned from medium-size data generated using a physics-based thermal model coupled with the polymer healing theory to determine the consolidation degree. Our results demonstrate high accuracy (>90%) of consolidation degree prediction at a low computational cost (four orders of magnitude faster than the numerical model).https://www.mdpi.com/1996-1944/14/9/2239additive manufacturingdata-driven approachfused filament fabrication |
| spellingShingle | Mriganka Roy Olga Wodo Feature Engineering for Surrogate Models of Consolidation Degree in Additive Manufacturing Materials additive manufacturing data-driven approach fused filament fabrication |
| title | Feature Engineering for Surrogate Models of Consolidation Degree in Additive Manufacturing |
| title_full | Feature Engineering for Surrogate Models of Consolidation Degree in Additive Manufacturing |
| title_fullStr | Feature Engineering for Surrogate Models of Consolidation Degree in Additive Manufacturing |
| title_full_unstemmed | Feature Engineering for Surrogate Models of Consolidation Degree in Additive Manufacturing |
| title_short | Feature Engineering for Surrogate Models of Consolidation Degree in Additive Manufacturing |
| title_sort | feature engineering for surrogate models of consolidation degree in additive manufacturing |
| topic | additive manufacturing data-driven approach fused filament fabrication |
| url | https://www.mdpi.com/1996-1944/14/9/2239 |
| work_keys_str_mv | AT mrigankaroy featureengineeringforsurrogatemodelsofconsolidationdegreeinadditivemanufacturing AT olgawodo featureengineeringforsurrogatemodelsofconsolidationdegreeinadditivemanufacturing |