About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Development of Recipe Optimization Method for Additive Manufacturing Process Parameter Determination |
Author(s) |
Steven James Osma, Jue Wang, Kousuke Kuwabara, Hyakka Nakada, Shinji Matsushita, Hirotsugu Kawanaka, Minseok Park, Yusuke Yasuda |
On-Site Speaker (Planned) |
Steven James Osma |
Abstract Scope |
Additive Manufacturing (AM) is a process that forms a three-dimensional structure directly from a CAD file. AM processes typically demand fine tuning of a large subset of process parameters (recipes, hereafter). Due to the many parameters, a trial-and-error approach has proven insufficient for determining recipes for new materials. In order to enable rapid recipe determination, we developed a recipe optimization method which is a set of machine learning algorithms including a kernel-ridge regression, multi-dimensional optimization, and feature selection algorithm. The developed method was applied to determine parameters for Selective Laser Melting process of ADMUSTER-C21P ®, a Ni-base alloy, ADMUSTER-C00P ®, a high-entropy alloy, and ADMUSTER-W285P ®, a maraging steel. Utilizing this method along with a thermal deformation measuring technique, the recipe determination process was expedited by 50%. Obtained recipes enable parts of superior quality compared with parts built using traditional recipes. |
Proceedings Inclusion? |
Undecided |