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Prediction of Dimensional Error in Down-Facing Surfaces for Laser Powder Bed Fusion Parts [in press]

Charles, A. P.; Elkaseer, A.; Hagenmeyer, V.; Scholz, S.

Abstract (englisch):
The growing rise of popularity of Additive Manufacturing technologies and its increased adoption for manufacturing has created a requirement for its fast development and maturity. However, it still lags far behind conventional manufacturing in terms of predictability, quality and robustness. Statistical modelling has proven to be an excellent tool to develop process knowledge and to optimize different processes efficiently and effectively. This paper establishes a methodology to predict dimensional errors in hard to print down-facing surfaces. Using the process parameters – laser power, scan speed, scan spacing, scan pattern and layer thickness, a predictive process model is developed. An ANOVA analysis concluded the laser power to be the most significant process parameter, followed by layer thickness and scan speed. This paper also discusses some of the interaction effects between parameters. Some thoughts on the next steps to be taken for validation of the model are discussed.



Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Proceedingsbeitrag
Jahr 2019
Sprache Englisch
Identifikator KITopen-ID: 1000098792
HGF-Programm 43.22.03 (POF III, LK 01)
Erschienen in 3RD World Congress on Micro and Nano Manufacturing, Raleigh WCMNM 2019, North Carolina, USA, 10th - 12th September
Veranstaltung 3rd World Congress on Micro and Nano Manufacturing (WCMNM 2019), Raleigh, NC, USA, 10.09.2019 – 12.09.2019
Schlagworte additive manufacturing, process modelling, laser powder bed fusion, ANOVA
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