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An Empirical Approach for the Development of Process Parameters for Laser Powder Bed Fusion
For certain additive manufacturing technologies the choice of available materials is currently limited. The development of process parameters is especially elaborate for powder bed technologies. Currently, there is no common approach concerning the procedure and documentation. This work proposes a m...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7730909/ https://www.ncbi.nlm.nih.gov/pubmed/33261091 http://dx.doi.org/10.3390/ma13235400 |
Sumario: | For certain additive manufacturing technologies the choice of available materials is currently limited. The development of process parameters is especially elaborate for powder bed technologies. Currently, there is no common approach concerning the procedure and documentation. This work proposes a methodology for the initial development of process parameters for new L-PBF (laser powder bed fusion) alloys. Key elements are the examination of the laser–powder-bed interaction by single laser track experiments and an iterative design of experiment (DoE) approach for the development of volumetric parameters. Two types of single laser track experiments are presented and provide information regarding the laser track width and depth as well as the resulting surface roughness and melt pool classification. Based on the information gained, suitable process windows for a DoE study can be defined by avoiding parameter settings unsuitable for production or measurement. Gradually, input variables are identified and iterative steps reduce the process window in order to optimize the desired target values. Near-surface exposure parameters are developed by a one-dimensional parameter variation and metallographic investigations. The approach is primarily designed for the initial development of process parameters for new L-PBF alloys. However, the information gained can also be used to optimize established parameter sets regarding new target values (productivity, mechanical properties), optimize process parameters for specific components or for a microstructural design. |
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