This research discusses a framework for automating process model realization for additive manufacturing. The models map relationships from design requirements to process variables and can be utilized for future process planning. A repository is employed to collect data and contains previous process plans and corresponding design requirements. The framework organizes data through a statistical clustering method and builds regression models using a multi-layer neural network. Hierarchical and k-means clustering methods are employed in series to manage the data. A two layer neural network and augmented training algorithm are employed to build process models. The framework has been tested with Stereolithography and Selective Laser Sintering process planning problems to demonstrate its usefulness.
- Design Engineering Division and Computers and Information in Engineering Division
A Framework for Self-Realizing Process Models for Additive Manufacturing
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Yim, S, & Rosen, DW. "A Framework for Self-Realizing Process Models for Additive Manufacturing." Proceedings of the ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 2: 31st Computers and Information in Engineering Conference, Parts A and B. Washington, DC, USA. August 28–31, 2011. pp. 1099-1109. ASME. https://doi.org/10.1115/DETC2011-47425
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