Simulation-based process optimization of laser-based powder bed fusion by means of beam shaping

被引:5
|
作者
Zenz, Constantin [1 ]
Buttazzoni, Michele [1 ]
Ceniceros, Mario Martinez [2 ]
Vazquez, Rodrigo Gomez [1 ]
Puchades, Jose Ramon Blasco [2 ]
Grinan, Luis Portoles [2 ]
Otto, Andreas [1 ]
机构
[1] TU Wien, Inst Prod Engn & Photon Technol, Getreidemarkt 9, A-1060 Vienna, Austria
[2] AIDIMME, Leonardo da Vinci 38, Paterna 46980, Spain
基金
欧盟地平线“2020”;
关键词
Laser powder bed fusion; Process optimization; Ni-based superalloys; Multiphysical simulation; Beam shaping; MULTIPHYSICAL SIMULATION; NUMERICAL-ANALYSIS; SURFACE-TENSION; ABLATION; METAL;
D O I
10.1016/j.addma.2023.103793
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Laser powder bed fusion of metals (PBF-LB/M) is an additive manufacturing technique which has recently been growing in popularity in industrial use cases. However, several challenges persist, including the issue of solidification cracking observed in widely used Ni-based superalloys. Through retrofitting an existing PBF machine with a dual beam system capable of dynamic beam shaping, it is possible to overcome this issue. The appropriate process parameters for the laser beam need to be determined prior to manufacturing the system. In this regard we propose a methodology that utilizes a numerical simulation tool to identify optimized parameters. To demonstrate the effectiveness of this approach, two example processes are presented. Initially the numerical model is validated by comparing its results against experimental data obtained from single track scans of two metal powders, CM247LC and IN713LC. Subsequently, an optimization study is conducted to identify optimal combinations of differently shaped and sized primary and secondary beams. The goal is to reduce the cooling rates within certain critical temperature ranges, thus mitigating the likelihood of solidification cracking, while avoiding the occurrence of other process defects such as balling, porosity, or lack of fusion. The effectiveness of these beam shapes is then verified through the production of physical samples. Through this example, a methodology for leveraging physics-based, model-driven process optimization is presented. Additionally, insights into the potential application of the same model for large-scale simulations are provided.
引用
收藏
页数:14
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