POEAM – A Method for the Part Orientation Evaluation for Additive Manufacturing
In the industrial application of additive manufacturing processes, a significant amount of time and resources are dedicated to the orientation and pre-print setup of the geometry. Steps such as the generation of support structures and the process simulation are among the most time-consuming. For the thorough assessment of an orientation of a given geometry, even more criteria, like print time or surface quality, need to be considered. POEAM proposes a method for an efficient assessment of a set of orientations, by means of well formulated criteria and an early elimination of insufficient orientations. The goal is to narrow the search field, so costly preparation steps will only be performed on orientations that promise a superior end result. Furthermore, POEAM is an automated process, which means it can be performed with minimal human interaction, resulting in an optimum regarding cost-efficiency and evaluation time. The method was applied to a representative geometry and has shown results that confirm the above-mentioned advantages.
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Leutenecker-Twelsiek, B., Klahn, C., & Meboldt, M. (2016). Considering Part Orientation in Design for Additive Manufacturing. Procedia CIRP, 50, 408–413. doi:10.1016/j.procir.2016.05.016.
Pant, P., Proper, S., Luzin, V., Sjöström, S., Simonsson, K., Moverare, J, S. Hosseini, V. Pacheco, Peng, R. L. (2020). Mapping of residual stresses in as-built Inconel 718 fabricated by laser powder bed fusion: A neutron diffraction study of build orientation influence on residual stresses. Additive Manufacturing, 36, 101501. doi:10.1016/j.addma.2020.101501.
Das, P., Chandran, R., Samant, R., & Anand, S. (2015). Optimum Part Build Orientation in Additive Manufacturing for Minimizing Part Errors and Support Structures. Procedia Manufacturing, 1, 343–354. doi:10.1016/j.promfg.2015.09.041.
Delfs, P., T̈ows, M., & Schmid, H.-J. (2016). Optimized build orientation of additive manufactured parts for improved surface quality and build time. Additive Manufacturing, 12, 314–320. doi:10.1016/j.addma.2016.06.003.
Matos, M. A., Rocha, A. M. A. C., & Pereira, A. I. (2020). Improving additive manufacturing performance by build orientation optimization. The International Journal of Advanced Manufacturing Technology, 107(5-6), 1993–2005. doi:10.1007/s00170-020-04942-6.
Moroni, G., Syam, W. P., & Petrò, S. (2015). Functionality-based Part Orientation for Additive Manufacturing. Procedia CIRP, 36, 217–222. doi:10.1016/j.procir.2015.01.015.
Cook, P. S., & Murphy, A. B. (2020). Simulation of melt pool behaviour during additive manufacturing: Underlying physics and progress. Additive Manufacturing, 31, 100909. doi:10.1016/j.addma.2019.100909.
Lindgren, L.-E., Lundbäck, A., Fisk, M., Pederson, R., & Andersson, J. (2016). Simulation of additive manufacturing using coupled constitutive and microstructure models. Additive Manufacturing, 12, 144–158. doi:10.1016/j.addma.2016.05.005.
Song, X., Feih, S., Zhai, W., Sun, C.-N., Li, F., Maiti, R., J. Wei, Y. Yang, V. Oancea, L. R. Brandt, Korsunsky, A. M. (2020). Advances in additive manufacturing process simulation: Residual stresses and distortion predictions in complex metallic components. Materials & Design, 193, 108779. doi:10.1016/j.matdes.2020.108779.
Rodgers, T. M., Madison, J. D., & Tikare, V. (2017). Simulation of metal additive manufacturing microstructures using kinetic Monte Carlo. Computational Materials Science, 135, 78–89. doi:10.1016/j.commatsci.2017.03.053.
Additive Works GmbH, (2020). Simulation-based process preparation software for Laser Beam Melting, https://additive.works/.
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