Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Novel Framework for Predictive Modeling and Optimization of Powder Bed Fusion Process

Version 1 : Received: 12 October 2021 / Approved: 13 October 2021 / Online: 13 October 2021 (10:20:29 CEST)

How to cite: Malekipour, E.; Marrey, M.; El-Mounayri, H.; Faierson, E.; Agarwal, M. A Novel Framework for Predictive Modeling and Optimization of Powder Bed Fusion Process. Preprints 2021, 2021100192 (doi: 10.20944/preprints202110.0192.v1). Malekipour, E.; Marrey, M.; El-Mounayri, H.; Faierson, E.; Agarwal, M. A Novel Framework for Predictive Modeling and Optimization of Powder Bed Fusion Process. Preprints 2021, 2021100192 (doi: 10.20944/preprints202110.0192.v1).

Abstract

Powder bed fusion (PBF) process is a metal additive manufacturing process which can build parts with any complexity from a wide range of metallic materials. PBF process research has predominantly focused on the impact of only a few parameters on product properties due to the lack of a systematic approach for optimizing a large set of process parameters simultaneously. The pivotal challenges regarding this process require a quantitative approach for mapping the material properties and process parameters onto the ultimate quality; this will then enable the optimization of those parameters. In this study, we propose a two-phase framework for optimizing the process parameters and developing a predictive model for 316L stainless steel material. We also discuss the correlation between process parameters -- i.e., laser specifications -- and mechanical properties and how to achieve parts with high density (> 98%) as well as better ultimate mechanical properties. In this paper, we introduce and test an innovative approach for developing AM predictive models, with a relatively low error percentage of 10.236% that are used to optimize process parameters in accordance with user or manufacturer requirements. These models use support vector regression, random forest regression, and neural network techniques. It is shown that the intelligent selection of process parameters using these models can achieve an optimized density of up to 99.31% with uniform microstructure, which improves hardness, impact strength, and other mechanical properties.

Keywords

Additive manufacturing; powder bed fusion; optimization framework; predictive models; neural network; intelligent parameters selection; energy density optimization; mechanical properties optimization

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