过程(计算)
融合
航程(航空)
计算机科学
过程建模
人工神经网络
过程变量
材料科学
工艺工程
工艺优化
机器学习
工程类
哲学
语言学
环境工程
复合材料
操作系统
作者
Mallikharjun Marrey,Ehsan Malekipour,Hazim El-Mounayri,Eric J. Faierson,Mangilal Agarwal
出处
期刊:3D printing and additive manufacturing
[Mary Ann Liebert]
日期:2024-02-01
卷期号:11 (1): 179-196
标识
DOI:10.1089/3dp.2021.0255
摘要
The 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 predictive modeling of 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 studying the process parameters and developing a predictive model for 316L stainless steel material. We also discuss the correlation between process parameters that is, laser specifications and mechanical properties, and how to obtain an optimum range of volumetric energy density for producing parts with high density (>99%), as well as better ultimate mechanical properties. In this article, we introduce and test an innovative approach for developing AM predictive models, with a relatively low error percentage (i.e., around 10%), which are used for process parameter selection in accordance with user or manufacturer part performance requirements. These models are based on techniques such as support vector regression, random forest regression, and neural network. It is shown that the intelligent selection of process parameters using these models can achieve a high density of up to 99.31% with uniform microstructure, which improves hardness, impact strength, and other mechanical properties.
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