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ANN-based modelling of dimensional accuracy in L-PBF

Charles, A.P.; Elkaseer, A.; Salem, M.

Abstract:
Laser-based powder bed fusion (L-PBF) is one of the AM techniques that has continued to gain an increased market acceptance and penetration, particularly in a wide range of industrial applications that include automotive, aerospace, medical/dental and robotics. However, in spite of its unstoppable rise in popularity, the L-PBF process still poses some technological challenges that need addressing in order to improve the robustness and repeatability of parts produced. In particular, when compared to other conventional manufacturing technologies, AM and L-PBF in particular lags behind when it comes to being able to predict different quality marks of the parts produced, such as dimensional accuracy and surface quality. In this context, the aim within this paper is to implement a systematic approach to improve precision of printed parts through predictive process modelling and experimental-based study. The methodology of a comprehensive Design of Experiments (DoE) study to improve process knowledge of down-facing surfaces is presented along with the methodology used to generate the dataset with regards to dimensional inaccuracy. The acquired results are used to build process models based on Artificial Neural network (ANN). ... mehr



Zugehörige Institution(en) am KIT Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Proceedingsbeitrag
Jahr 2019
Sprache Englisch
Identifikator KITopen-ID: 1000098789
HGF-Programm 43.22.03 (POF III, LK 01)
Erschienen in Conference Proceedings - Special Interest Group : Advancing Precision in Additive Manufacturing 2019, 16th - 18th September 2019, Nantes, France
Veranstaltung ASPE and euspen Summer Topical Meeting Advancing Precision in Additive Manufacturing (2019), Nantes, Frankreich, 16.09.2019 – 18.09.2019
Verlag EUSPEN
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