Manufacturing & Industrial Engineering Faculty Publications
Document Type
Article
Publication Date
3-13-2026
Abstract
Additive manufacturing (AM) is a transformative technology that enables the fabrication of complex geometries layer by layer. However, metal parts produced via AM processes such as laser powder bed fusion (LPBF) are prone to various defects, including porosity and deformation. These defects often result from suboptimal printing parameter settings. Traditional approaches typically aim to reduce defects by optimizing a fixed set of parameters for the entire part. However, such methods do not account for layer-wise variations in printing conditions caused by changes in geometry, heat transfer, and re-heating effects. While optimizing parameters for each layer could improve part quality, it would require an impractically large number of experiments for parts composed of hundreds or thousands of layers. To address this challenge, this paper proposes a novel approach in which printing parameters along the build direction are modeled using a parameter function. This function is constructed as a weighted combination of several basis functions, substantially reducing the number of decision variables. The weights are optimized using a response surface method to minimize a defect index, which aggregates multiple quality metrics, including residual stress, displacement, lack of fusion, and overheating. The proposed method significantly reduces porosity in an inverted-cone geometry compared to conventional approaches, as demonstrated through finite element analysis simulations and physical experiments that optimize laser power. This research presents a robust framework for efficiently optimizing layer-wise printing parameters to minimize process-induced defects in LPBF.
Recommended Citation
Dou, Chaoran, Rongxuan Wang, Raghav Gnanasambandam, Jianzhi Li, and Zhenyu Kong. "Layer-wise printing parameter optimization for laser powder bed fusion." Journal of Intelligent Manufacturing (2026): 1-19. https://doi.org/10.1007/s10845-026-02798-3
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
Journal of Intelligent Manufacturing
DOI
10.1007/s10845-026-02798-3
Comments
This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.