Research on Optimization of Laser Powder Bed Fusion Process for 304L Stainless Steel Based on Machine Learning and Multi-Objective Optimization

doi:10.11900/0412.1961.2025.00286

Acta Metall Sin

2026, Vol. 62

Issue (7): 1228-1245 DOI: 10.11900/0412.1961.2025.00286

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Research on Optimization of Laser Powder Bed Fusion Process for 304L Stainless Steel Based on Machine Learning and Multi-Objective Optimization

XU Qingqing¹, YAN Zhen¹, GUO Yuyu¹, HOU Juan¹(

), WANG Hao², HUANG Aijun³

1 Interdisciplinary Center for Additive Manufacturing, School of Materials and Chemistry, University of Shanghai for Science and Technology, Shanghai 200093, China
2 Shenyang National Research Center for Materials Science, Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, China
3 Additive Manufacturing Centre, Monash University, Victoria 3800, Australia

Cite this article:

XU Qingqing, YAN Zhen, GUO Yuyu, HOU Juan, WANG Hao, HUANG Aijun. Research on Optimization of Laser Powder Bed Fusion Process for 304L Stainless Steel Based on Machine Learning and Multi-Objective Optimization. Acta Metall Sin, 2026, 62(7): 1228-1245.

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Abstract

Laser powder bed fusion (LPBF) has emerged as a promising additive manufacturing technique for producing high-performance 304L austenitic stainless steel, which is widely used in the aerospace and automotive industries and in biomedical engineering because of its excellent mechanical properties, corrosion resistance, and high-temperature stability. However, the mechanical properties of LPBF-fabricated materials are influenced by the complex interplay of various process parameters, including laser power, scanning speed, layer thickness, and hatch spacing. Traditional trial-and-error methods for process optimization are costly and time-consuming, and they often fail to precisely control the material's microstructure, resulting in suboptimal performance. Therefore, there is a pressing need to adopt advanced approaches to systematically optimize the LPBF process and ensure reliable, high-performance outcomes. This study proposes an innovative hybrid intelligent framework that integrates Stacking-ensemble learning, interpretable machine learning techniques (Shapley additive explanation (SHAP) method), and multiobjective optimization (nondominated sorting genetic algorithm II-technique for order preference by similarity to ideal solution (NSGA-II-TOPSIS)). The primary objective is to develop an all-encompassing framework for the simultaneous prediction and optimization of ultimate tensile strength, yield strength, and elongation in LPBF-fabricated 304L stainless steel. The framework is designed not only to improve the accuracy of predicting the mechanical properties but also to provide a clear understanding of the influence of process parameters on material behavior. The Stacking-ensemble model demonstrates superior performance in terms of accuracy, generalization, and stability compared to individual machine learning models, such as random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost). SHAP analysis has revealed that laser power plays a critical role in determining the mechanical properties of the material, making it the most important factor to consider in the optimization process. The multiobjective optimization approach facilitates the identification of the optimal process parameters, resulting in a balanced strength-ductility trade-off that is crucial for practical applications. Experimental validation was conducted to confirm the effectiveness of the proposed framework. The optimized LPBF samples exhibited refined, uniform cellular substructures, increased dislocation density, and the formation of twin boundaries, which significantly improved the material's mechanical properties.

Key words: laser powder bed fusion Stacking-ensemble learning interpretable machine learning multi-objective optimization

Received: 25 September 2025

ZTFLH:

TG142.7

Fund: National Natural Science Foundation of China(U22B2067);National Natural Science Foundation of China(52073176)

Corresponding Authors: HOU Juan, professor, Tel: 18217727686, E-mail: houjuan@usst.com

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	XU Qingqing
	YAN Zhen
	GUO Yuyu
	HOU Juan
	WANG Hao
	HUANG Aijun

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https://www.ams.org.cn/EN/10.11900/0412.1961.2025.00286 OR https://www.ams.org.cn/EN/Y2026/V62/I7/1228

Fig.1 Schematic of the proposed methodological framework for optimizing the process parameters of laser powder bed fusion (LPBF) 304L stainless steel (RF—random forest, GBDT—gradient boosting decision tree, XGBoost—extreme gradient boosting, CatBoost—categorical boosting, R²—coefficient of determination, MAE—mean absolute error, BD—building direction, SHAP—Shapley additive explanations, NSGA-II—non-dominated sorting genetic algorithm II)

Fig.2 Visualizations of data distribution ranges for LPBF process parameters and mechanical properties (Colored areas indicate the distribution density of the dataset)
(a) laser power and scanning speed (b) layer thickness and hatch distance (c) UTS, YS, and EL

Table 1 Value ranges for input features in the entire dataset

Table 2 Value ranges for output metrics in the entire dataset

Fig.3 Training framework of the feature-combination-based Stacking ensemble model (MCCV—Monte Carlo cross validation)

Table 3 Constraint ranges of process parameters in the NSGA-II

Fig.4 Mechanical properties of 304L stainless steel at different volumetric energy densities

Fig.5 Schematic of tensile specimen dimension and microstructure sampling position (unit: mm)

Fig.6 Pearson correlation coefficient heatmap of process parameters and tensile properties

Mechanical property	Model	Training set				Testing set
Mechanical property	Model	$R 2 ¯$	$S D R 2 ¯$	$M A E ¯$	$S D M A E ¯$	$R 2 ¯$	$S D R 2 ¯$	$M A E ¯$	$S D M A E ¯$
UTS	RF	0.963	0.004	25.735	1.254	0.897	0.045	40.136	5.141
	GBDT	0.980	0.002	18.750	0.919	0.915	0.033	37.071	4.601
	XGBoost	0.980	0.002	18.321	0.919	0.918	0.030	36.185	4.951
	Stacking	0.976	0.017	19.026	7.712	0.923	0.023	33.650	3.952
YS	RF	0.959	0.005	23.183	1.528	0.893	0.035	35.612	4.543
	GBDT	0.992	0.002	9.160	1.055	0.900	0.030	34.078	4.751
	XGBoost	0.979	0.002	16.967	0.856	0.906	0.027	33.184	4.042
	Stacking	0.958	0.021	22.802	6.027	0.919	0.021	31.444	3.587
EL	RF	0.925	0.008	3.181	0.180	0.808	0.106	4.806	1.027
	GBDT	0.935	0.002	3.450	0.117	0.748	0.094	6.554	0.978
	XGBoost	0.953	0.006	2.601	0.164	0.809	0.089	4.910	1.055
	Stacking	0.940	0.016	3.022	0.457	0.820	0.075	3.718	0.802

Table 4 Performance metrics of each model on the training and testing datasets

Fig.7 Boxplot comparisons of testing set performances of different models based on 50 cyc independent MCCV trials (Points represent outliers, boxes represent interquartile range (IQR))
(a-c) R² distributions for UTS (a), YS (b), and EL (c)
(d-f) MAE distributions for UTS (d), YS (e), and EL (f)

Fig.8 Comparisons of predicted and actual values for UTS (a), YS (b), and EL (c) in training and testing sets

Mechanical property	Model	Training set				Testing set
		$R 2 ¯$	$S D R 2 ¯$	$M A E ¯$	$S D M A E ¯$	$R 2 ¯$	$S D R 2 ¯$	$M A E ¯$	$S D M A E ¯$
UTS	SVR	0.926	0.041	28.768	9.797	0.854	0.056	46.561	7.273
	GPR	0.937	0.043	32.289	9.971	0.803	0.069	56.619	8.956
	Stacking	0.976	0.017	19.026	7.712	0.923	0.023	33.650	3.952
YS	SVR	0.923	0.035	25.527	7.055	0.840	0.036	43.442	7.329
	GPR	0.934	0.042	28.410	7.131	0.810	0.065	50.098	7.447
	Stacking	0.958	0.021	22.802	6.027	0.919	0.021	31.444	3.587
EL	SVR	0.901	0.057	5.413	1.113	0.791	0.095	4.736	1.657
	GPR	0.906	0.064	5.294	1.093	0.766	0.106	5.861	1.437
	Stacking	0.940	0.016	3.022	0.457	0.820	0.075	3.718	0.802

Table 5 Performance metrics of each model on the training and testing datasets

Fig.9 Mean absolute SHAP value feature importance ranking (a, c, e) and SHAP value distribution (b, d, f) of UTS (a, b), YS (c, d), and EL (e, f) for each sample

Fig.8 SHAP value distributions of UTS (a, d), YS (b, e), and EL (c, f) for samples under various laser powers (a-c) and scanning speeds (d-f)

Fig.11 SHAP value distributions of UTS (a), YS (b), and EL (c) for samples under interactions between laser power and scanning speed

Fig.12 Pareto front distribution identified by NSGA-II (a) and optimal solution identified by the TOPSIS (b) (TOPSIS—technique for order preference by similarity to ideal solution. S1, S2, and S3—three process parameters)

Fig.13 Distributions of laser power (a), scanning speed (b), and hatch distance (c) along the Pareto front

Table 6 Process parameters of the validation experiments

Fig.14 Stress-strain curves of S1 (a), S2 (b), and S3 (c) samples from three validation experiments; comparison between predicted and measured tensile properties of S1-S3 samples (d); comparison of tensile properties of 304L stainless steels obtained by this work and other various additive manufacturing and forging processes^[47-55] (e) (DED—directed energy deposition)

Fig.15 Low and high (insets) magnified SEM images of S1 sample processed at 220 W (a) and S2 sample processed at 278 W (b)

Fig.16 Inverse pole figures (IPFs) (a, d), kernel average misorientation (KAM) maps (b, e), and grain boundary distribution maps (c, f) of S1 sample processed at 220 W (a-c) and S2 sample processed at 278 W (d-f) (Insets in Figs.16a and d are grain size distribution histograms; insets in Figs.16b and e are KAM distribution histograms)

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