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

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, 闫震, 郭玉玉, 侯娟, 王皞, 黄爱军

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

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

表4 各模型在训练集和测试集上的性能指标

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

Mechanical property	Model	Training set				Testing set
Mechanical property	Model	$\bar{R^{2}}$	$S D_{\bar{R^{2}}}$	$\bar{M A E}$	$S D_{\bar{M A E}}$	$\bar{R^{2}}$	$S D_{\bar{R^{2}}}$	$\bar{M A E}$	$S D_{\bar{M A E}}$
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