Modeling and experimental validation of an immersed thermo-mechanical part-scale analysis for laser powder bed fusion processes
| Autor: | Alessandro Reali, Stefan Kollmannsberger, J. Jomo, Massimo Carraturo, Ernst Rank, Ferdinando Auricchio |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Fusion Materials science Additive Manufacturing Biomedical Engineering Mechanical engineering 02 engineering and technology 021001 nanoscience & nanotechnology Laser Industrial and Manufacturing Engineering Finite element method ddc law.invention 020901 industrial engineering & automation Approximation error law Deflection (engineering) Powder bed General Materials Science Process simulation 0210 nano-technology Engineering (miscellaneous) Thermo mechanical |
| Zdroj: | Additive Manufacturing. 36:101498 |
| ISSN: | 2214-8604 |
| Popis: | The capability of correctly predicting part deflections after support removal is important to assess the quality of a final artifact produced by laser powder bed fusion (LPBF) technology. The finite element method is usually employed to perform part-scale thermo-mechanical analysis to estimate the final distortion of 3D printed parts. Due to the high flexibility of LPBF additive manufacturing, most of the components produced by means of such a technology have an optimized shape and complex geometrical features. Consequently, the process of generating an analysis suitable mesh starting from the original 3D virtual model turns out to be a non-trivial task. Immersed boundary methods represent a possible solution to perform accurate process simulation without the meshing burden. In this work, an immersed numerical framework to perform thermo-mechanical part-scale analysis is experimentally validated by means of part deflection measurements obtained for a single-cantilever structure after support removal. The comparison between simulation and experiment shows that the proposed numerical framework is able to deliver results with an almost perfect correlation to the measured data and a maximum relative error below 5%. |
| Databáze: | OpenAIRE |
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