Abstract
The determination of process parameters to build additively-manufactured parts with desired properties remains a challenge, especially as we move from machine to machine or process new materials. In this chapter, we show how we can combine simple simulations and experiments to iteratively constrain the design space of parameters, and quickly and efficiently identify parameters to create parts with \(>\)99 % density. Our approach is based on techniques from statistics and data mining, including design of physical and computational experiments, feature selection to identify important variables, and data-driven predictive models that can act as surrogates for the simulations.
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Acknowledgments
The author acknowledges the contributions of Wayne King (Eagar-Tsai model), Paul Alexander (operation of the Concept Laser M2), Mark Pearson and Cheryl Evans (metallographic preparation, measurement, and data reporting).
LLNL-MI-667267: This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. This work was funded by the Laboratory Directed Research and Development Program at LLNL under project tracking code 13-SI-002.
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Kamath, C. (2016). On the Use of Data Mining Techniques to Build High-Density, Additively-Manufactured Parts. In: Lookman, T., Alexander, F., Rajan, K. (eds) Information Science for Materials Discovery and Design. Springer Series in Materials Science, vol 225. Springer, Cham. https://doi.org/10.1007/978-3-319-23871-5_7
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