Optimization of process parameters for powder bed fusion additive manufacturing by combination of machine learning and finite element method: A conceptual framework

被引：112
|
作者：
Baturynska, Ivanna [1 ]
Semeniuta, Oleksandr [1 ]
Martinsen, Kristian [1 ]
机构：
[1] Norwegian Univ Sci & Technol, Dept Mfg & Civil Engn, Technol 22, N-2815 Gjovik, Norway
来源：
11TH CIRP CONFERENCE ON INTELLIGENT COMPUTATION IN MANUFACTURING ENGINEERING | 2018年 / 67卷
关键词：
Additive Manufacturing; Polymer Powder Bed Fusion; Modeling; Artificial Neural network; Optimization; Material Parameters; Process Parameters; LASER; FABRICATION; SHRINKAGE; STRENGTH; DENSITY;
D O I：
10.1016/j.procir.2017.12.204
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In addition to prototyping, Powder Bed Fusion (PBF) AM processes have lately been more widely used to manufacture end-use parts. These changes lead to necessity of higher requirements to quality of a final product. Optimization of process parameters is one of the ways to achieve desired quality of a part. Finite Element Method (FEM) and machine learning techniques are applied to evaluate and optimize AM process parameters. While FEM requires specific information, Machine Learning is based on big amounts of data. This paper provides a conceptual framework on combination of mathematical modelling and Machine Learning to avoid these issues. (C) 2017 The Authors. Published by Elsevier B.V.
引用
收藏
页码：227 / 232
页数：6
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