基于机器学习与多目标优化的<strong>304L</strong>不锈钢激光粉末床熔融工艺优化研究

doi:10.11900/0412.1961.2025.00286

金属学报

2026, Vol. 62

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

研究论文

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基于机器学习与多目标优化的304L不锈钢激光粉末床熔融工艺优化研究

徐青青¹, 闫震¹, 郭玉玉¹, 侯娟¹(

), 王皞², 黄爱军³

1 上海理工大学材料与化学学院增材制造研究院上海 200093
2 中国科学院金属研究所沈阳材料科学国家研究中心沈阳 110016
3 Additive Manufacturing Centre, Monash University, Victoria 3800, Australia

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

引用本文:

徐青青, 闫震, 郭玉玉, 侯娟, 王皞, 黄爱军. 基于机器学习与多目标优化的304L不锈钢激光粉末床熔融工艺优化研究[J]. 金属学报, 2026, 62(7): 1228-1245.
Qingqing XU, Zhen YAN, Yuyu GUO, Juan HOU, Hao WANG, Aijun HUANG. Research on Optimization of Laser Powder Bed Fusion Process for 304L Stainless Steel Based on Machine Learning and Multi-Objective Optimization[J]. Acta Metall Sin, 2026, 62(7): 1228-1245.

全文: PDF(4735 KB) HTML

摘要:

采用激光粉末床熔融(LPBF)技术制备高性能304L不锈钢时，其力学性能高度依赖复杂工艺参数的协同作用，但传统试错法因参数空间高维、变量耦合而面临成本高、效率低及显微组织调控困难的挑战。本工作提出了一种基于堆叠(Stacking)集成学习模型、可解释机器学习(SHAP方法)与多目标优化(NSGA-II-TOPSIS算法)相结合的混合智能策略，实现了对采用LPBF技术制备的304L不锈钢拉伸性能的高精度预测和协同优化，确定了最佳工艺参数窗口，并结合多目标优化算法获得了强塑性最优解。结果表明，该Stacking集成模型在抗拉强度、屈服强度和断后延伸率三项力学性能指标的预测精度、泛化能力和稳定性方面均显著优于单一模型，激光功率是同时影响三项力学性能的主导因素。实验结果与模型预测结果高度一致，且采用优化工艺制备的试样具有细小均匀的胞状亚结构、较高的位错密度以及高比例孪晶界，实现了晶界强化与位错强化的协同作用。

关键词 ：激光粉末床熔融, Stacking集成学习, 可解释机器学习, 多目标优化

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

收稿日期: 2025-09-25

ZTFLH:

TG142.7

基金资助:国家自然科学基金项目(U22B2067);国家自然科学基金项目(52073176)

通讯作者: 侯娟，houjuan@usst.com，主要从事金属增材制造领域相关研究

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

作者简介: 徐青青，女，1999年生，硕士

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图1 激光粉末床熔融(LPBF)技术制备304L不锈钢工艺参数优化方法框架示意图

图2 LPBF激光工艺参数和力学性能的可视化数据分布范围

表1 数据集中输入特征的取值范围

表2 数据集中各输出指标的取值范围

图3 基于特征组合的Stacking融合模型训练框架图

表3 非支配排序遗传算法II (NSGA-II)中工艺参数的约束范围

图4 不同体积能量密度下304L不锈钢的力学性能

图5 拉伸试样尺寸和显微组织取样示意图

图6 各工艺参数和拉伸性能间的Pearson相关系数热图

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

表4 各模型在训练集和测试集上的性能指标

图7 各模型基于Monte Carlo交叉验证(MCCV)的50 cyc独立重复实验所得测试集性能箱型图对比

图8 数据集中三个力学性能的预测值与实际值对比

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

表5 各模型在训练集和测试集上的性能指标

图9 三个力学性能的平均绝对SHAP值特征重要性排序及各样本的SHAP值分布

图10 不同激光功率和扫描速率下三项力学性能的SHAP值分布

图11 不同激光功率与扫描速率交互作用下三项力学性能的SHAP值分布

图12 基于NSGA-II-TOPSIS算法获得的解集(其中TOPSIS为逼近理想解排序法)

图13 Pareto前沿曲线上不同工艺参数的分布

表6 验证实验的工艺参数

图14 试样S1、S2和S3的应力-应变曲线，力学性能预测值和测量值对比，以及与采用不同增材制造和锻造工艺制备的304L不锈钢的拉伸性能[47~55]对比

图15 S1和S2试样显微组织的SEM像

图16 S1和S2试样的EBSD分析

[1]	Marattukalam J J, Karlsson D, Pacheco V, et al. The effect of laser scanning strategies on texture, mechanical properties, and site-specific grain orientation in selective laser melted 316L SS [J]. Mater. Des., 2020, 193: 108852 doi: 10.1016/j.matdes.2020.108852
[2]	Shin W S, Son B, Song W, et al. Heat treatment effect on the microstructure, mechanical properties, and wear behaviors of stainless steel 316L prepared via selective laser melting [J]. Mater. Sci. Eng., 2021, A806: 140805
[3]	Liu Y D, Zhang M, Shi W T, et al. Study on performance optimization of 316L stainless steel parts by high-efficiency selective laser melting [J]. Opt. Laser Technol., 2021, 138: 106872 doi: 10.1016/j.optlastec.2020.106872
[4]	Zhang Y H, Underschultz J, Langhi L, et al. Numerical modelling of coal seam depressurization during coal seam gas production and its effect on the geomechanical stability of faults and coal beds [J]. Int. J. Coal Geol., 2018, 195: 1 doi: 10.1016/j.coal.2018.05.008
[5]	Lou X Y, Song M, Emigh P W, et al. On the stress corrosion crack growth behaviour in high temperature water of 316L stainless steel made by laser powder bed fusion additive manufacturing [J]. Corros. Sci., 2017, 128: 140 doi: 10.1016/j.corsci.2017.09.017
[6]	Mansoura A, Omidi N, Barka N, et al. Selective laser melting of stainless steels: A review of process, microstructure and properties [J]. Met. Mater. Int., 2024, 30: 2343 doi: 10.1007/s12540-024-01650-8
[7]	Yusuf S, Chen Y F, Boardman R, et al. Investigation on porosity and microhardness of 316L stainless steel fabricated by selective laser melting [J]. Metals, 2017, 7: 64 doi: 10.3390/met7020064
[8]	Hitzler L, Hirsch J, Heine B, et al. On the anisotropic mechanical properties of selective laser-melted stainless steel [J]. Materials, 2017, 10: 1136 doi: 10.3390/ma10101136
[9]	Yang X Q, Liu Y, Ye J W, et al. Enhanced mechanical properties and formability of 316L stainless steel materials 3D-printed using selective laser melting [J]. Int. J. Min. Metall. Mater., 2019, 26: 1396 doi: 10.1007/s12613-019-1837-2
[10]	Hou J, Dai B B, Min S L, et al. Influence of size design on microstructure and properties of 304L stainless steel by selective laser melting [J]. Acta Metall. Sin., 2023, 59: 623 doi: 10.11900/0412.1961.2021.00248
[10]	侯娟, 代斌斌, 闵师领等. 尺寸设计对选区激光熔化304L不锈钢显微组织与性能的影响 [J]. 金属学报, 2023, 59: 623
[11]	Chen C Y, Xie Y C, Liu L T, et al. Cold spray additive manufacturing of Invar 36 alloy: Microstructure, thermal expansion and mechanical properties [J]. J. Mater. Sci. Technol., 2021, 72: 39 doi: 10.1016/j.jmst.2020.07.038
[12]	Cao Y, Lin X, Wang Q Z, et al. Microstructure evolution and mechanical properties at high temperature of selective laser melted AlSi10Mg [J]. J. Mater. Sci. Technol., 2021, 62: 162 doi: 10.1016/j.jmst.2020.04.066
[13]	Jia Q B, Gu D D. Selective laser melting additive manufacturing of Inconel 718 superalloy parts: Densification, microstructure and properties [J]. J. Alloys Compd., 2014, 585: 713 doi: 10.1016/j.jallcom.2013.09.171
[14]	Wang R, Chen C Y, Liu M Y, et al. Effects of laser scanning speed and building direction on the microstructure and mechanical properties of selective laser melted Inconel 718 superalloy [J]. Mater. Today Commun., 2022, 30: 103095
[15]	Ghayoor M, Lee K, He Y J, et al. Selective laser melting of 304L stainless steel: Role of volumetric energy density on the microstructure, texture and mechanical properties [J]. Addit. Manuf., 2020, 32: 101011
[16]	Sun X F, Song W, Liang J J, et al. Research and development in materials and processes of superalloy fabricated by laser additive manufacturing [J]. Acta Metall. Sin., 2021, 57: 1471 doi: 10.11900/0412.1961.2021.00371
[16]	孙晓峰, 宋巍, 梁静静等. 激光增材制造高温合金材料与工艺研究进展 [J]. 金属学报, 2021, 57: 1471 doi: 10.11900/0412.1961.2021.00371
[17]	Li D M, Zhang X, Qin R X, et al. Influence of processing parameters on AlSi10Mg lattice structure during selective laser melting: Manufacturing defects, thermal behavior and compression properties [J]. Opt. Laser Technol., 2023, 161: 109182 doi: 10.1016/j.optlastec.2023.109182
[18]	Wang M Y, Li S J, He Z H, et al. Effect of process parameters on density and compressive properties of Ti5553 alloy block prepared by SLM [J]. Chin. J. Mater. Res., 2025, 39: 583 doi: 10.11901/1005.3093.2024.398
[18]	王铭宇, 李述军, 和正华等. 激光功率和扫描速度对SLM制备Ti5553合金性能的影响 [J]. 材料科学学报, 2025, 39: 583
[19]	Wang Q, Chen J K, Sun G Q, et al. Microstructure and mechanical performance of 304 stainless steel fabricated by laser powder bed fusion: The effect of post-processing heat treatment [J]. J. Mater. Eng. Perform., 2023, 32: 695 doi: 10.1007/s11665-022-07108-5
[20]	Zhang H Z, Li C Y, Yao G, et al. Effect of annealing treatment on microstructure evolution and deformation behavior of 304 L stainless steel made by laser powder bed fusion [J]. Int. J. Plast., 2022, 155: 103335 doi: 10.1016/j.ijplas.2022.103335
[21]	Yang F P, Wang J Y, Wen T, et al. Laser powder bed fusion of a novel high strength Al-Mg alloy via Si and Zn microalloying [J]. Mater. Lett., 2023, 343: 134358 doi: 10.1016/j.matlet.2023.134358
[22]	Jin Z Q, Zhang Z Z, Demir K, et al. Machine learning for advanced additive manufacturing [J]. Matter, 2020, 3: 1541 doi: 10.1016/j.matt.2020.08.023
[23]	Guo C P, Shi C C, Liu P, et al. Prediction of mechanical properties of biodegradable zinc alloys based on machine learning [J]. Acta Metall. Sin., DOI: 10.11900/0412.1961.2024.00332
[23]	郭传平, 石尘尘, 刘鹏等. 基于机器学习的生物可降解锌合金力学性能预测 [J]. 金属学报, 2024, DOI: 10.11900/0412.1961.2024.00332
[24]	Yang L, Zhao F, Jiang L, et al. Development of composition and heat treatment process of 2000 MPa grade spring steels assisted by machine learning [J]. Acta Metall. Sin., 2023, 59: 1500
[24]	杨累, 赵帆, 姜磊等. 机器学习辅助2000 MPa级弹簧钢成分和热处理工艺开发 [J]. 金属学报, 2023, 59: 1500
[25]	Gao T C, Gao J B, Zhang J L, et al. Development of an accurate “composition-process-properties” dataset for SLMed Al-Si-(Mg) alloys and its application in alloy design [J]. J. Mater. Inf., 2023, 3: 6
[26]	Liu Q, Wu H K, Paul M J, et al. Machine-learning assisted laser powder bed fusion process optimization for AlSi10Mg: New microstructure description indices and fracture mechanisms [J]. Acta Mater., 2020, 201: 316 doi: 10.1016/j.actamat.2020.10.010
[27]	Ma Z Y, Liu W W, Li W Y, et al. Optimization of density and surface morphology of SS 316L/IN718 functionally graded thin-walled structures using hybrid prediction-multi-objective optimization method [J]. J. Manuf. Process., 2024, 120: 337 doi: 10.1016/j.jmapro.2024.04.044
[28]	Maitra V, Shi J, Lu C Y. Robust prediction and validation of as-built density of Ti-6Al-4V parts manufactured via selective laser melting using a machine learning approach [J]. J. Manuf. Process., 2022, 78: 183 doi: 10.1016/j.jmapro.2022.04.020
[29]	Zhai W G, Zhou W, Zhu Z G, et al. Selective laser melting of 304L and 316L stainless steels: A comparative study of microstructures and mechanical properties [J]. Steel. Res. Int., 2022, 93: 2100664 doi: 10.1002/srin.v93.7
[30]	Zou Z X, Yang Y M, Fan Z Q, et al. Suitability of data preprocessing methods for landslide displacement forecasting [J]. Stoch. Environ. Res. Risk Assess., 2020, 34: 1105 doi: 10.1007/s00477-020-01824-x
[31]	Dufera A G, Liu T T, Xu J. Regression models of Pearson correlation coefficient [J]. Stat. Theory Relat. Fields, 2023, 7: 97
[32]	Todorovic M, Stanisic N, Zivkovic M, et al. Improving audit opinion prediction accuracy using metaheuristics-tuned XGBoost algorithm with interpretable results through SHAP value analysis [J]. Appl. Soft Comput., 2023, 149: 110955 doi: 10.1016/j.asoc.2023.110955
[33]	Xu Q S, Liang Y Z, Du Y P. Monte Carlo cross‐validation for selecting a model and estimating the prediction error in multivariate calibration [J]. J. Chemom., 2004, 18: 112 doi: 10.1002/cem.v18:2
[34]	Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II [J]. IEEE Trans. Evol. Comput., 2002, 6: 182 doi: 10.1109/4235.996017
[35]	Behzadian M, Khanmohammadi Otaghsara S, Yazdani M, et al. A state-of the-art survey of TOPSIS applications [J]. Expert Syst. Appl., 2012, 39: 13051 doi: 10.1016/j.eswa.2012.05.056
[36]	Ahmed Obeidi M, Uí Mhurchadha S M, Raghavendra R, et al. Comparison of the porosity and mechanical performance of 316L stainless steel manufactured on different laser powder bed fusion metal additive manufacturing machines [J]. J. Mater. Res. Technol., 2021, 13: 2361 doi: 10.1016/j.jmrt.2021.06.027
[37]	Mansoura A, Dehghan S, Barka N, et al. Investigation into the effect of process parameters on density, surface roughness, and mechanical properties of 316L stainless steel fabricated by selective laser melting [J]. Int. J. Adv. Manuf. Technol., 2024, 130: 2547 doi: 10.1007/s00170-023-12865-1
[38]	Leicht A, Rashidi M, Klement U, et al. Effect of process parameters on the microstructure, tensile strength and productivity of 316L parts produced by laser powder bed fusion [J]. Mater. Charact., 2020, 159: 110016 doi: 10.1016/j.matchar.2019.110016
[39]	Li T, Shi L, Pang J Y, et al. Regulation of heat treatment and synergistic mechanism of strength-ductility in precipitation-strengthened high-entropy alloy fabricated by laser powder bed fusion [J]. Acta Metall. Sin., 2025, DOI: 10.11900/0412.1961.2025.00163
[39]	李彤, 石磊, 庞景宇等. 激光粉末床熔化制备沉淀强化高熵合金的热处理调控及强塑性协同机制 [J]. 金属学报, DOI: 10.11900/0412.1961.2025.00163
[40]	Kumar P, Zhu Z G, Nai S M L, et al. Fracture toughness of 304L austenitic stainless steel produced by laser powder bed fusion [J]. Scr. Mater., 2021, 202: 114002 doi: 10.1016/j.scriptamat.2021.114002
[41]	De Sonis E, Dépinoy S, Giroux P F, et al. Microstructure-toughness relationships in 316L stainless steel produced by laser powder bed fusion [J]. Mater. Sci. Eng., 2023, A877: 145179
[42]	Dong S Y, Wang Y Y, Li J Y, et al. Machine learning aided prediction and design for the mechanical properties of magnesium alloys [J]. Met. Mater. Int., 2024, 30: 593 doi: 10.1007/s12540-023-01531-6
[43]	Hou H B, Wang J F, Ye L, et al. Prediction of mechanical properties of biomedical magnesium alloys based on ensemble machine learning [J]. Mater. Lett., 2023, 348: 134605 doi: 10.1016/j.matlet.2023.134605
[44]	Wang Y C, Hu B L, Zhang J F, et al. Effect of process parameters and post-treatment on room temperature tensile properties of GH3536 superalloy fabricated by laser powder bed fusion [J]. Acta Metall. Sin., 2025, DOI: 10.11900/0412.1961.2025.00053
[44]	王源晨, 胡炳利, 张剑锋等. 成形工艺参数及后处理对激光粉末床熔融成形GH3536合金室温拉伸性能的影响 [J]. 金属学报, 2025, DOI: 10.11900/0412.1961.2025.00053
[45]	Zhang A, Wu W P, Wu M, et al. Influence of laser power on mechanical properties and pitting corrosion behavior of additively manufactured 316L stainless steel by laser powder bed fusion (L-PBF) [J]. Opt. Laser Technol., 2024, 176: 110886 doi: 10.1016/j.optlastec.2024.110886
[46]	Uva Narayanan C, Daniel A, Praveenkumar K, et al. Effect of scanning speed on mechanical, corrosion, and fretting-tribocorrosion behavior of austenitic 316L stainless steel produced by laser powder bed fusion process [J]. J. Manuf. Process., 2024, 131: 1582 doi: 10.1016/j.jmapro.2024.09.108
[47]	Huang W B, Zhang Y M, Dai W B, et al. Mechanical properties of 304 austenite stainless steel manufactured by laser metal deposition [J]. Mater. Sci. Eng., 2019, A758: 60
[48]	Wang J D, Xue Y, Xu D, et al. Effects of layer-by-layer ultrasonic impact treatment on microstructure and mechanical properties of 304 stainless steel manufactured by directed energy deposition [J]. Addit. Manuf., 2023, 68: 103523
[49]	Jackson M A, Morrow J D, Thoma D J, et al. A comparison of 316L stainless steel parts manufactured by directed energy deposition using gas-atomized and mechanically-generated feedstock [J]. CIRP Ann., 2020, 69: 165 doi: 10.1016/j.cirp.2020.04.042
[50]	Liu G, Su Y G, Pi X Y, et al. Achieving high strength 316L stainless steel by laser directed energy deposition-ultrasonic rolling hybrid process [J]. Mater. Sci. Eng., 2024, A903: 146665
[51]	Saboori A, Piscopo G, Lai M, et al. An investigation on the effect of deposition pattern on the microstructure, mechanical properties and residual stress of 316L produced by directed energy deposition [J]. Mater. Sci. Eng., 2020, A780: 139179
[52]	Yang N, Yee J, Zheng B, et al. Process-structure-property relationships for 316L stainless steel fabricated by additive manufacturing and its implication for component engineering [J]. J. Therm. Spray Technol., 2017, 26: 610 doi: 10.1007/s11666-016-0480-y
[53]	Wu D J, Yu C S, Wang Q Y, et al. Synchronous-hammer-forging-assisted laser directed energy deposition additive manufacturing of high-performance 316L samples [J]. J. Mater. Process. Technol., 2022, 307: 117695 doi: 10.1016/j.jmatprotec.2022.117695
[54]	Zhang K, Wang S J, Liu W J, et al. Characterization of stainless steel parts by laser metal deposition shaping [J]. Mater. Des., 2014, 55: 104 doi: 10.1016/j.matdes.2013.09.006
[55]	Bai Y, Akita M, Uematsu Y, et al. Improvement of fatigue properties in type 304 stainless steel by annealing treatment in nitrogen gas [J]. Mater. Sci. Eng., 2014, A607: 578
[56]	Hou J, Chen W, Chen Z E, et al. Microstructure, tensile properties and mechanical anisotropy of selective laser melted 304L stainless steel [J]. J. Mater. Sci. Technol., 2020, 48: 63 doi: 10.1016/j.jmst.2020.01.011
[57]	Zhou C L, Yan X R, Long Y L, et al. Effect of laser power on hydrogen embrittlement and microstructural evolution in selective laser melted 304L austenitic stainless steel [J]. Corros. Sci., 2025, 249: 112814 doi: 10.1016/j.corsci.2025.112814
[58]	Huang G, Wei K W, Deng J F, et al. High-power laser powder bed fusion of 316L stainless steel: Defects, microstructure, and mechanical properties [J]. Manuf. Process., 2022, 83: 235
[59]	Li X Z, Fang X W, Jiang X, et al. Additively manufactured high-performance AZ91D magnesium alloys with excellent strength and ductility via nanoparticles reinforcement [J]. Addit. Manuf., 2023, 69: 103550
[60]	Yang H H, Wang Z M, Wang H Z, et al. Microstructure, mechanical property and heat treatment schedule of the Inconel 718 manufactured by low and high power laser powder bed fusion [J]. Mater. Sci. Eng., 2023, A863: 144517
[61]	Li M C, Ma R, Ren Y Q, et al. New insights on dislocation forming mechanism of nickel-based superalloy fabricated by laser powder bed fusion [J]. J. Mater. Res. Technol., 2024, 30: 4303 doi: 10.1016/j.jmrt.2024.04.105
[62]	Li W Q, Meng L X, Zhang Q F, et al. High-temperature stability and mechanical property optimization of laser powder bed fusion 316L steel after controlled annealing [J]. J. Cent. South Univ., 2025, 32: 1179 doi: 10.1007/s11771-025-5947-x

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