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Article Citation - WoS: 39Citation - Scopus: 49Focus Variation Measurement and Prediction of Surface Texture Parameters Using Machine Learning in Laser Powder Bed Fusion(Asme, 2020) Ozel, Tugrul; Altay, Ayca; Kaftanoglu, Bilgin; Leach, Richard; Senin, Nicola; Donmez, AlkanThe powder bed fusion-based additive manufacturing process uses a laser to melt and fuse powder metal material together and creates parts with intricate surface topography that are often influenced by laser path, layer-to-layer scanning strategies, and energy density. Surface topography investigations of as-built, nickel alloy (625) surfaces were performed by obtaining areal height maps using focus variation microscopy for samples produced at various energy density settings and two different scan strategies. Surface areal height maps and measured surface texture parameters revealed the highly irregular nature of surface topography created by laser powder bed fusion (LPBF). Effects of process parameters and energy density on the areal surface texture have been identified. Machine learning methods were applied to measured data to establish input and output relationships between process parameters and measured surface texture parameters with predictive capabilities. The advantages of utilizing such predictive models for process planning purposes are highlighted.Article Citation - WoS: 46Citation - Scopus: 50Investigations on Microstructural Changes in Machining of Inconel 100 Alloy Using Face Turning Experiments and 3d Finite Element Simulations(Pergamon-elsevier Science Ltd, 2016) Arisoy, Yigit M.; Guo, Changsheng; Kaftanoglu, Bilgin; Oezel, Tugrul; Ozel, Tugrul; Kaftanoʇlu, BilginNickel-base IN100 alloy is a choice of material for components requiring high strength at elevated temperatures. Machining processes applied to these components affect the microstructure, grain size, and microhardness of the finished surface. This research investigates the effects of tool micro-geometry, coating, and cutting speed on the microstructural changes during machining. 3D customized finite element simulations have been performed to predict the average grain size by implementing modified temperature dependent flow softening based material and Johnson-Mehl-Avrami-Kolmogorov crystallization models. Simulation predictions on the average grain sizes, phase fractions, and resultant microhardness are compared against experimental measurements revealing good agreements. (C) 2016 Elsevier Ltd. All rights reserved.
