Systematic approach to process parameter optimization for laser powder bed fusion of low-alloy steel based on melting modes

被引:14
作者
Bergmueller, Simon [1 ]
Gerhold, Lukas [1 ]
Fuchs, Lorenz [1 ]
Kaserer, Lukas [1 ]
Leichtfried, Gerhard [1 ]
机构
[1] Univ Innsbruck, Fac Engn Sci, Dept Mechatron, Mat Sci Technikerstr 13, A-6020 Innsbruck, Austria
关键词
Laser powder bed fusion (LPBF); Melting mode; Low-alloy steel; Additive manufacturing (AM); Laser parameter optimization; Normalized enthalpy; SCALING LAWS; MICROSTRUCTURE;
D O I
10.1007/s00170-023-11377-2
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In the metal additive manufacturing (AM) process of laser powder bed fusion (LPBF), there are a limited number of materials suitable for producing parts with high density and desired mechanical properties. To establish novel materials, it is essential to determine optimized process parameters in order to overcome process-related challenges and mitigate defects such as lack of fusion, keyholing, and balling. Scaling laws based on thermophysical properties and process parameters can be used to transfer knowledge from other materials or LPBF systems. In this work, a scaling law is used to adjust process parameters for single-track experiments over a wide range, which are laser power P-L (100-1000 W), scan speed v(s) (300- 2500 mm/s), and laser spot size d(s) (0.08-0.25 mm). Compared to existing studies, the parameter range is thus extended towards large laser spot sizes and high laser powers. The scaling law used is based on the calculation of the normalized enthalpy (?H)(hs). The ratio of the deposited energy density ? H and the melting enthalpy hs correlates with the dimensions of the melt pool. According to the aspect ratio oc of the melt pool of each single track, the respective melting mode-conduction, transition, and keyhole mode-was identified. The process parameters of the single tracks in transition mode were used to optimize the density of the LPBF specimens with varying hatch distance h(d) (0.06-0.12 mm), resulting in specimens with a relative density of > 99.8%. The proposed methodology can accelerate the process parameter finding for new alloys and avoid process-related defects.
引用
收藏
页码:4385 / 4398
页数:14
相关论文
共 39 条
[1]   Process parameter selection and optimization of laser powder bed fusion for 316L stainless steel: A review [J].
Ahmed, N. ;
Barsoum, I. ;
Haidemenopoulos, G. ;
Abu Al-Rub, R. K. .
JOURNAL OF MANUFACTURING PROCESSES, 2022, 75 :415-434
[2]  
[Anonymous], 2021, Fundamentals of laser powder bed fusion of metals, DOI [10.1016/C2020-0-01200-4, DOI 10.1016/C2020-0-01200-4]
[3]  
Aumayr C., 2020, Berg Huettenmaenn Monatsh, V165, P137, DOI [10.1007/s00501-020-00966-3, DOI 10.1007/S00501-020-00966-3]
[4]   Additive manufacturing of 24CrNiMo low alloy steel by selective laser melting: Influence of volumetric energy density on densification, microstructure and hardness [J].
Cui, X. ;
Zhang, S. ;
Zhang, C. H. ;
Chen, J. ;
Zhang, J. B. ;
Dong, S. Y. .
MATERIALS SCIENCE AND ENGINEERING A-STRUCTURAL MATERIALS PROPERTIES MICROSTRUCTURE AND PROCESSING, 2021, 809
[5]   Scaling laws for the laser welding process in keyhole mode [J].
Fabbro, Remy .
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2019, 264 :346-351
[6]   Heterogeneous sensing and scientific machine learning for quality assurance in laser powder bed fusion - A single-track study [J].
Gaikwad, Aniruddha ;
Giera, Brian ;
Guss, Gabriel M. ;
Forien, Jean-Baptiste ;
Matthews, Manyalibo J. ;
Rao, Prahalada .
ADDITIVE MANUFACTURING, 2020, 36
[7]   An effective rule for translating optimal selective laser melting processing parameters from one material to another [J].
Ghasemi-Tabasi, Hossein ;
Jhabvala, Jamasp ;
Boillat, Eric ;
Ivas, Toni ;
Drissi-Daoudi, Rita ;
Loge, Roland E. .
ADDITIVE MANUFACTURING, 2020, 36
[8]  
GKN Powder Metallurgy, 2022, LOW ALL STEELS ADD M
[9]   In-situ full-field mapping of melt flow dynamics in laser metal additive manufacturing [J].
Guo, Qilin ;
Zhao, Cang ;
Qu, Minglei ;
Xiong, Lianghua ;
Hojjatzadeh, S. Mohammad H. ;
Escano, Luis, I ;
Parab, Niranjan D. ;
Fezzaa, Kamel ;
Sun, Tao ;
Chen, Lianyi .
ADDITIVE MANUFACTURING, 2020, 31
[10]   Keyholing or Conduction - Prediction of Laser Penetration Depth [J].
Hann, D. B. ;
Iammi, J. ;
Folkes, J. .
PROCEEDINGS OF THE 36TH INTERNATIONAL MATADOR CONFERENCE, 2010, :275-278