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Optimal Powder Deposition Process to Develop a New Direct-Write Additive Manufacturing System
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Publication Year
2019-06-01
Publisher
SpringerOpen
Citation
International Journal of Precision Engineering and Manufacturing, Vol.20, pp.1057-1067
Keyword
Additive manufacturingDirect-writeFunctionally graded materialsPowder depositionProcess optimizationStatistical analysis
Mesh Keyword
Direct writeFunctionally graded material (FGM)Material compositionsNovel materialsOptimal combinationPoly lactic acidPowder depositionProcess condition
All Science Classification Codes (ASJC)
Mechanical EngineeringIndustrial and Manufacturing EngineeringElectrical and Electronic Engineering
Abstract
In functionally graded materials (FGM), material property gradually changes within a product. To manufacture FGM by additive manufacturing (AM) using polymer powders, precise deposition of different powder materials is crucial. The powder deposition, however, is challenging, because process control and material choices are complicated. This paper presents a newly developed laser-based AM system using the direct deposit of poly-lactic acid powders on the target surface. This direct-writing AM system can facilitate material change even within a layer for superior material property variation. This study characterizes the optimal process conditions for deposition consistency by statistical methods. This study also identifies suitable statistical models by examining the model characteristics such as lack-of-fit and curvature. In addition, this study finds an appropriate statistical method to handle process abnormality such as no powder flow. Through these analyses, this study characterizes the optimal combination of process conditions and material choices for stable powder deposition, and verifies the best conditions for the new AM system. This study will help develop a new AM system with the optimal deposition for each material composition to produce novel material structure for FGM.
Language
eng
URI
https://dspace.ajou.ac.kr/dev/handle/2018.oak/30735
DOI
https://doi.org/10.1007/s12541-019-00129-6
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Type
Article
Funding
Acknowledgements This work was supported in part by the US National Science Foundation (NSF) (CMMI #1331633), National Research Foundation of Korea (NRF) Grants funded by the Korea government (MSIP) (NRF-2017R1D1A1B03035703, NRF-2014R1A2A2A03006993, NRF-2014R1A1A2058955, NRF-2011-0011932), Hongik University and Ajou University Research Fund.
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Ko, Jeong Han Image
Ko, Jeong Han고정한
Department of Industrial Engineering
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