钴铬合金选区激光熔化工艺参数可靠性优化

doi:10.12382/bgxb.2023.0923

摘要/Abstract

摘要：

选区激光熔化(Selective Laser Melting, SLM)是当前最常见的增材制造技术之一,工艺参数、材料特性以及气氛稳定性对成形质量有重要影响。以激光功率名义值、扫描速度和扫描间距为设计变量,以激光功率、钴铬合金粉末粒径局部分布均值和风速波动度为随机参数,设计多因素多水平实验。基于秩关联原理实现不完备数据的概率增广,获取设计变量、随机参数与试样性能之间的响应面。在满足许用抗拉强度的概率约束下,以成形效率最高为优化目标,建立钴铬合金SLM工艺参数的可靠性设计优化模型。针对SLM过程随机参数非正态和功能函数非线性的问题,引入通用生成函数,融合离散枚举和随机抽样,基于自适应细分-重要抽样创新优化算法,求取最优工艺参数,在可靠性约束下获得最高成形效率。

关键词: 选区激光熔化, 可靠性设计优化, 钴铬合金, 工艺参数, 通用生成函数, 秩关联

Abstract:

Selective laser melting (SLM) is one of the most common additive manufacturing technologies at present. The process parameters, material properties and atmosphere stability have important impacts on the forming quality. A multi-factor and multi-level experiment is designed by taking the nominal value of laser power, scanning speed and scanning pitch as design variables, and the laser power, local distribution mean value of Co-Cr alloy powder diameters, and wind speed fluctuation strength as random parameters. Based on the principle of rank correlation, the probabilistic augmentation of incomplete data is achieved to obtain the response surface among design variables, random parameters and properties of samples. The reliability-based design optimization (RBDO) model for SLM process parameters of Co-Cr alloy is established with the optimization goal of maximizing forming efficiency under the probability constraint of meeting the allowable tensile strength. Aiming at non-normal random parameters and non-linear performance function in the Co-Cr alloy SLM process, the universal generation function is introduced, discrete enumeration and random sampling are integrated, and an innovative RBDO algorithm based on adaptive subdivision-importance sampling strategy is proposed to obtain the optimal solution of SLM process parameters for achieving the highest forming efficiency under reliability constraints.

Key words: selective laser melting, reliability-based design optimization, Co-Cr alloy, process parameter, universal generating function, rank correlation

中图分类号:

周金宇, 王新愿, 程锦翔, 王林. 钴铬合金选区激光熔化工艺参数可靠性优化[J]. 兵工学报, 2024, 45(12): 4530-4538.

ZHOU Jinyu, WANG Xinyuan, CHENG Jinxiang, WANG Lin. Reliability-based Optimization of Selective Laser Melting Process Parameters for Co-Cr Alloy[J]. Acta Armamentarii, 2024, 45(12): 4530-4538.

图/表 11

图1 SLM技术原理

Fig.1 Technology principle of SLM

表1 钴铬合金粉末化学成分

Table 1 Chemical composition of Co-Cr alloy powder

元素组成	钴	铬	钨	硅	铁	锰
占比/%	61.5	27.75	8.5	1.5	0.5	0.25

图2 粉末粒径分布

Fig.2 Distribution of power particle size

图3 拉伸试样

Fig.3 Tensile specimens

表2 实验设计

Table 2 Design of experiments

因素	水平
因素	-2	-1	0	1	2
P_m /W	120	140	160	180
v/(mm·s^-1)	1000	1200	1400	1600	1800
h/mm	0.06	0.07	0.08

表3 抗拉强度测试结果(局部)

Table 3 Test results of tensile strength (partially)

编号	P_m/ W	v/ (mm·s^-1)	h/ mm	σ₁/ MPa	σ₂/ MPa	σ₃/ MPa
1	120	1000	0.06	1034.61	1048.21	1065.21
2	140	1000	0.07	967.49	1043.90	1064.48
3	160	1000	0.08	1038.29	1049.98	1058.25
4	180	1000	0.06	970.48	1003.72	1019.47
5	120	1000	0.07	1060.08	1089.45	1100.81
6	120	1200	0.07	1053.07	1108.34	1132.05
7	140	1200	0.08	1027.81	1095.79	1113.43
8	160	1200	0.08	1070.51	1083.22	1127.67
9	180	1200	0.06	984.53	1013.28	1037.55
10	140	1200	0.06	1012.73	1056.96	1073.91
11	120	1400	0.08	814.01	895.21	911.73
12	140	1400	0.07	966.58	1062.39	1099.09
13	160	1400	0.08	998.11	1082.37	1165.05
14	180	1400	0.06	984.72	1015.36	1028.18
15	160	1400	0.07	995.20	1050.17	1083.77
16	120	1600	0.06	679.88	896.29	946.10
17	140	1600	0.07	900.60	947.05	1009.10
18	160	1600	0.06	972.97	1078.37	1096.74
19	180	1600	0.07	1042.13	1086.49	1107.93
20	180	1600	0.08	1069.77	1093.74	1116.56
21	120	1800	0.08	502.59	617.80	709.61
22	140	1800	0.06	853.33	944.61	1072.16
23	160	1800	0.07	764.34	909.13	1049.10
24	180	1800	0.08	775.43	928.35	1005.69
25	180	1800	0.06	962.38	1067.77	1085.42

图4 工艺参数对试样抗拉强度的主效应

Fig.4 Main effect of the process parameters on the tensile strength of specimen

表4 随机参数与抗拉强度数据映射(某组实验)

Table 4 Mapping of random parameters and tensile strengths (for a group of experiments)

P/W	d_m /μm	δ/(m·s^-1)	σ/MPa
117.14	37.83	0.39	502.59
118.09	37.50	0.35	588.74
118.65	37.17	0.32	606.65
119.09	36.83	0.30	611.11
119.47	36.50	0.28	611.55
119.83	36.17	0.26	615.16
120.17	35.83	0.24	617.80
120.53	35.50	0.22	621.28
120.91	35.17	0.20	644.93
121.35	34.83	0.18	668.51
121.91	34.50	0.15	681.18
122.86	34.17	0.11	709.61

表5 抗拉强度响应面模型方差分析

Table 5 Analysis of variance of response surface model for tensile strength

来源	自由度	F值	p值	评价
模型	10	196.06	<0.0001	显著
P	1	331.78	<0.0001	显著
v	1	615.77	<0.0001	显著
h	1	38.94	<0.0001	显著
δ	1	163.59	<0.0001	显著
Pv	1	365.98	<0.0001	显著
Ph	1	14.90	0.0001	显著
vh	1	74.42	<0.0001	显著
P²	1	54.66	<0.0001	显著
v²	1	118.32	<0.0001	显著
$d m 2$	1	6.93	0.0089	显著

表5 抗拉强度响应面模型方差分析

Table 5 Analysis of variance of response surface model for tensile strength

来源	自由度	F值	p值	评价
模型	10	196.06	<0.0001	显著
P	1	331.78	<0.0001	显著
v	1	615.77	<0.0001	显著
h	1	38.94	<0.0001	显著
δ	1	163.59	<0.0001	显著
Pv	1	365.98	<0.0001	显著
Ph	1	14.90	0.0001	显著
vh	1	74.42	<0.0001	显著
P²	1	54.66	<0.0001	显著
v²	1	118.32	<0.0001	显著
$d m 2$	1	6.93	0.0089	显著

图5 基于AS-IS的SLM工艺参数RBDO流程

Fig.5 Flowchart of AS-IS-based RBDO framework for SLM process parameters

表6 方法计算结果对比

Table 6 Comparison of calculated results of given methods

方法	设计点位置向量	β_MCS	ε_i/ %	迭代次数	功能函数调用次数	时间/ s
1	×	×	×	×	×	×
2	×	×	×	×	×	×
3	×	×	×	×	×	×
4	$165.6 1733.2 0.08$	1.9917	0.42	5	153279	1.08
5	$165.7 1733.2 0.079$	2.0092	0.46	3	30400000	318

表6 方法计算结果对比

Table 6 Comparison of calculated results of given methods

方法	设计点位置向量	β_MCS	ε_i/ %	迭代次数	功能函数调用次数	时间/ s
1	×	×	×	×	×	×
2	×	×	×	×	×	×
3	×	×	×	×	×	×
4	$165.6 1733.2 0.08$	1.9917	0.42	5	153279	1.08
5	$165.7 1733.2 0.079$	2.0092	0.46	3	30400000	318

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