316L不锈钢激光熔覆宏微观特征优化

叶超; 侯亮; 陈云; 徐杨; 刘文志; 王振忠

doi:10.12073/j.hjxb.20220426001

316L不锈钢激光熔覆宏微观特征优化

叶超^1,,
侯亮¹,
陈云^1, ,,
徐杨¹,
刘文志²,
王振忠¹

1.
厦门大学, 厦门, 361102
2.
机械科学研究总院海西(福建)分院有限公司, 三明, 365000

基金项目: 国家自然科学基金资助项目(51905461)

详细信息

作者简介:
叶超，博士研究生；主要研究方向为增减材复合制造；Email：136620990@qq.com

通讯作者:
陈云，博士，助理教授；Email：yun.chen@xmu.edu.cn.

中图分类号: TG456.7; TN249
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出版历程
- 收稿日期: 2022-04-25
- 网络出版日期: 2023-03-05
- 刊出日期: 2023-03-24

Research on optimization of macroscopic and microscopic characteristics of 316L stainless steel by laser cladding

1.
Xiamen University, Xiamen 361102, China
2.
Haixi Institute of China Academy of Machinery Science and Technology, Sanming 365000, China

摘要

摘要: 为了获得高质量激光熔覆制件，针对现有研究仅以宏观几何形貌为优化目标的问题，以316L不锈钢为例，提出一种以宏微观特征为目标的工艺优化方法. 首先通过全析因设计和回归分析构建熔覆层宏观几何形貌及微观组织同主要工艺参数的经验统计模型，探讨了工艺参数对几何形貌及微观晶粒平均截距的影响规律. 然后选用几何形貌和晶粒平均截距作为评价熔覆成形质量的指标，采用复合合意性函数确定了最佳工艺参数和合适工艺窗口，最后验证了该方法可行性和有效性. 结果表明，在选择最佳工艺参数的条件下，宏微观特征的统计模型具有较高预测精度，制备的熔覆样件不仅具更高的显微硬度，还具备良好的拉伸性能：屈服强度为439 MPa，抗拉极限为751 MPa，断后伸长率为26%，实现了宏微观特征的优化.
- 激光熔覆 /
- 宏观几何形貌 /
- 微观组织 /
- 工艺优化
Abstract: In order to obtain high-quality laser cladding fabricated parts, a process optimization method targeting macroscopic and microscopic characteristics is proposed for 316L stainless steel as an example, based on the problem that existing studies only target geometric morphology for optimization.Firstly, an empirical statistical model of the geometric morphology and microstructure of the cladding layer and the main process parameters is constructed through full factorial design and regression analysis, and the influence of process parameters on the geometric morphology and the average intercept of microscopic grain is discussed. Then, the geometric morphology and the average grain intercept are selected as the indicators for evaluating the quality of cladding, and the optimal process parameters and suitable process range are determined by the composite desirability function. Finally, the feasibility and effectiveness of the method are verified. The results show that under the condition of the best process parameters, the statistical model of macroscopic and microscopic characteristics has high prediction accuracy. The prepared cladding samples not only have higher microhardness, but also have excellent tensile properties: the yield strength is 439 MPa, the tensile strength is 751 MPa, and the elongation is 26%. The process optimization of macroscopic and microscopic characteristics is achieved.
- laser cladding /
- geometric morphology /
- microstructure /
- process optimization

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图 1 熔覆截面宏观几何形貌示意图

Figure 1. Schematic diagram of macro geometric morphology of cladding section

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图 2 直线截点法示意图

Figure 2. Diagram of the straight-line intersection method

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图 3 激光熔覆加工设备

Figure 3. Laser cladding processing device. (a) laser cladding processing system; (b) spindle

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图 4 拉伸试样示意图(mm)

Figure 4. Schematic diagram of tensile specimen. (a) specimen preparation; (b) sample size; (c) tensile specimen

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图 5 熔覆层横截面

Figure 5. Cladding cross section

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图 6 因子对宽高比的影响

Figure 6. Effect of factors on aspect ratio

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图 7 因子对接触角的影响

Figure 7. Effect of factors on contact angle

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图 8 因子对稀释率的影响

Figure 8. Effect of factors on dilution rate

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图 9 因子对晶粒平均截距的影响

Figure 9. Effect of factors on the average intercept of grains

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图 10 优化等值线图

Figure 10. Optimized contour maps

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图 11 优化组熔覆层微观组织结构

Figure 11. Microstructure of cladding layer for optimization. (a) cladding layer of optimization group; (b) bottom region; (c) central region; (d) right region

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图 12 试验组19熔覆层微观组织结构

Figure 12. Microstructure of cladding layer for experimental No.19. (a) cladding layer of No.19 experiment; (b) left region; (c) bottom region; (d) right region

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图 13 维氏显微硬度

Figure 13. Vickers microhardness

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图 14 拉伸性能

Figure 14. Tensile properties

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图 15 多层多道试样的横截面

Figure 15. Cross-section of multi-layer specimen. (a) cross section of the sample; (b) partial enlarged view of the section

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表 1 工艺参数及其因子水平

Table 1 Process parameters and factor levels

水平	激光功率P/W	扫描速度v/(mm·min⁻¹)	送粉量Q/(g·min⁻¹)	粉末离焦量D/mm
1	1 400	700	15.5	1.5
0	1 200	600	14	1.0
−1	1 000	500	12.5	0.5

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表 2 因子水平及响应结果

Table 2 Factor levels and response results

运行序	工艺参数				响应值
运行序	激光功率P /W	扫描速度V /(mm·min⁻¹)	送粉量Q /(g·min⁻¹)	粉离焦量D /mm	宽高比λ	接触角θ /(°)	稀释率κ	晶粒平均截距l/μm
1	1 000	500	15.5	0.5	2.443	101.598	0.059	7.438
2	1 200	600	14.0	1.0	3.680	122.952	0.193	4.918
3	1 000	500	12.5	1.5	3.703	123.256	0.141	4.000
4	1 400	500	12.5	1.5	4.187	128.930	0.353	5.806
5	1 400	500	15.5	1.5	3.651	122.569	0.250	6.923
6	1 400	700	12.5	0.5	4.912	134.687	0.349	5.085
7	1 400	700	12.5	1.5	5.569	141.889	0.353	4.615
8	1 400	500	15.5	0.5	3.205	115.872	0.275	7.500
9	1 200	600	14.0	1.0	3.912	125.947	0.200	5.769
10	1 000	700	15.5	1.5	3.495	120.840	0.037	4.983
11	1 400	700	15.5	0.5	4.085	127.829	0.220	4.451
12	1 000	500	15.5	1.5	2.950	111.723	0.063	6.383
13	1 200	600	14.0	1.0	3.575	123.448	0.212	5.085
14	1 000	700	12.5	1.5	4.555	132.588	0.091	4.110
15	1 000	700	12.5	0.5	4.149	128.524	0.094	5.263
16	1 200	600	14.0	1.0	3.912	125.947	0.200	5.769
17	1 000	700	15.5	0.5	3.073	114.183	0.039	5.357
18	1 400	500	12.5	0.5	3.573	121.529	0.316	5.488
19	1 000	500	12.5	0.5	2.579	104.422	0.095	5.769
20	1 400	700	15.5	1.5	4.124	128.552	0.264	5.625

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表 3 方差分析结果

Table 3 Results of ANOVA

宽高比 λ	p 值	接触角 θ	p 值	稀释率 κ	p 值	晶粒平均截距 l	p 值
模型	0	模型	0	模型	0	模型	0
P	0	P	0	P	0	P	0.242
V	0	V	0	Q	0	V	0
Q	0	Q	0	—	—	Q	0
D	0	D	0	—	—	D	0.048
VQ	0	VD	0.048	—	—	PD	0.019
VD	0.045	—	—	—	—	VQ	0.006
QD	0.020	—	—	—	—	—	—
R² = 0.980	Adj. R² = 0.969	R² = 0.938	Adj. R² = 0.916	R² = 0.968	Adj. R² = 0.965	R² = 0.856	Adj. R² = 0.789
—	Pred. R² = 0.943	—	Pred. R² = 0.856	—	Pred. R² = 0.955	—	Pred. R² = 0.636

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表 4 规范化的各响应回归系数

Table 4 Normalized regression coefficients for responses

回归系数对应变量	激光功率P*	扫描速度V*	送粉量Q*	粉离焦量D*	扫描速度送粉量VQ	扫描速度粉离焦量VD	送粉量粉离焦量QD	激光功率粉离焦量PD
宽高比 λ	0.397	0.479	−0.388	0.263	−0.163	−0.073	−0.087	—
接触角 θ	5.295	6.200	−4.541	3.856	—	−1.526	—	—
稀释率 κ	0.110	—	−0.036	—	—	—	—	—
晶粒平均截距 l	0.137	−0.614	0.533	−0.244	−0.365	—	—	0.300

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表 5 优化结果和试验

Table 5 Optimization results and validation experiment

	激光功率P/W	扫描速度v /(mm·min⁻¹)	送粉量Q/(g·min⁻¹)	粉离焦量D/mm	宽高比λ	接触角θ/(°)	稀释率κ(%)	晶粒平均截距l/μm	合意性	结果
预测值	1 400.00	503.13	12.50	0.50	3.50	121.26	0.34	5.31	0.79	优化
	1 350.00	550.00	12.50	0.50	3.74	123.55	0.31	5.23	0.53	—
	1 350.00	516.92	12.50	0.50	3.49	121.00	0.31	5.31	0.56	—
	1 400.00	500.00	12.50	0.50	4.00	124.70	0.31	5.57	—	—
误差δ(%)	—	—	—	—	12.50	2.76	9.68	4.67	—	—

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参考文献(22)

[1]	刘伟, 李能, 周标, 等. 复杂结构与高性能材料增材制造技术进展[J]. 机械工程学报, 2019, 55(20): 128 − 151. Liu Wei, Li Neng, Zhou Biao, et al. Progress in additive manufacturing on complex structures and high-performance materials[J]. Journal of Mechanical Engineering, 2019, 55(20): 128 − 151.
[2]	刘立君, 刘大宇, 王晓陆, 等. H13钢激光熔覆陶瓷修复层的参数优化[J]. 焊接学报, 2020, 41(7): 65 − 70. doi: 10.12073/j.hjxb.20200508002 Liu Lijun, Liu Dayu, Wang Xiaolu, et al. Parameter optimization of laser cladding ceramic repair layer of H13 steel[J]. Transactions of the China Welding Institution, 2020, 41(7): 65 − 70. doi: 10.12073/j.hjxb.20200508002
[3]	Gao J, Wu C, Hao Y, et al. Numerical simulation and experimental investigation on three-dimensional modelling of single-track geometry and temperature evolution by laser cladding[J]. Optics & Laser Technology, 2020, 129: 106287 − 106298.
[4]	Gao S, Feng Y, Wang J, et al. Molten pool characteristics of a nickel-titanium shape memory alloy for directed energy deposition[J]. Optics & Laser Technology, 2021, 142: 107215 − 107228.
[5]	Huang Y, Khamesee M B, Toyserkani E. A comprehensive analytical model for laser powder-fed additive manufacturing[J]. Additive Manufacturing, 2016, 12(partA): 90 − 99.
[6]	Ertay D S, Vlasea M, Erkorkmaz K. Thermomechanical and geometry model for directed energy deposition with 2d/3d toolpaths[J]. Additive Manufacturing, 2020, 35: 1 − 42.
[7]	Ansari M, Shoja Razavi R, Barekat M. An empirical-statistical model for coaxial laser cladding of NiCrAlY powder on Inconel 738 superalloy[J]. Optics & Laser Technology, 2016, 86: 136 − 144.
[8]	Erfanmanesh M, Abdollah-Pour H, Mohammadian-Semnani H, et al. An empirical-statistical model for laser cladding of WC-12Co powder on AISI 321 stainless steel[J]. Optics & Laser Technology, 2017, 97: 180 − 186.
[9]	Nabhani M, Razavi R S, Barekat M. An empirical-statistical model for laser cladding of Ti-6Al-4V powder on Ti-6Al-4V substrate[J]. Optics & Laser Technology, 2018, 100: 265 − 271.
[10]	Wen P, Feng Z, Zheng S. Formation quality optimization of laser hot wire cladding for repairing martensite precipitation hardening stainless steel[J]. Optics & Laser Technology, 2015, 65: 180 − 188.
[11]	Sun Y, Hao M. Statistical analysis and optimization of process parameters in Ti6Al4V laser cladding using Nd: YAG laser[J]. Optics and Lasers in Engineering, 2012, 50(7): 985 − 995. doi: 10.1016/j.optlaseng.2012.01.018
[12]	Alam M K, Urbanic R J, Nazemi N, et al. Predictive modeling and the effect of process parameters on the hardness and bead characteristics for laser-cladded stainless steel[J]. The International Journal of Advanced Manufacturing Technology, 2018, 94(1): 397 − 413.
[13]	Zhang Hui, Zou Yong, Zou Zengda, et al. Comparative study on continuous and pulsed wave fiber laser cladding in-situ titanium-vanadium carbides reinforced Fe-based composite layer[J]. Materials Letters, 2015, 139: 255 − 257. doi: 10.1016/j.matlet.2014.10.102
[14]	Li Ruidi, Yuan Tiechui, Qiua Zili, et al. Nanostructured Co-Cr-Fe alloy surface layer fabricated by combination of laser clad and friction stir processing[J]. Surface & Coatings Technology, 2014, 258: 412 − 425.
[15]	Xie S, Li R, Yuan T, et al. Laser cladding assisted by friction stir processing for preparation of deformed crack-free Ni-Cr-Fe coating with nanostructure[J]. Optics Laser Technology, 2018, 337: 426 − 433.
[16]	Montero-Sistiaga Maria L, Godino-Martinez Miguel, Boschmans Kurt, et al. Microstructure evolution of 316L produced by HP-SLM (high power selective laser melting)[J]. Additive Manufacturing, 2018, 23: 402 − 410. doi: 10.1016/j.addma.2018.08.028
[17]	Zhang Z, Farahmand P, Kovacevic R. Laser cladding of 420 stainless steel with molybdenum on mild steel A36 by a high power direct diode laser[J]. Materials & Design, 2016, 109: 686 − 699.
[18]	Zhang D, Qiu D, Gibson M A, et al. Additive manufacturing of ultrafine-grained high-strength titanium alloys[J]. Nature, 2019, 576(7785): 91 − 95. doi: 10.1038/s41586-019-1783-1
[19]	Alali M, Todd I, Wynne B P. Through-thickness microstructure and mechanical properties of electron beam welded 20 mm thick AISI 316L austenitic stainless steel[J]. Materials & Design, 2017, 130: 488 − 500.
[20]	Erinosho M F, Akinlabi E T. Central composite design on the volume of laser metal deposited Ti6Al4V and Cu[J]. Materiali in Tehnologije, 2017, 51(3): 419 − 426. doi: 10.17222/mit.2016.019
[21]	Ma Pengzhao, Wu Yu, Zhang Pengju, et al. Solidification prediction of laser cladding 316L by the finite element simulation[J]. The International Journal of Advanced Manufacturing Technology, 2019, 103(1): 957 − 969.
[22]	Wang L, Xue J, Wang Q. Correlation between arc mode, microstructure, and mechanical properties during wire arc additive manufacturing of 316L stainless steel[J]. Materials Science & Engineering, 2019, 751(28): 183 − 190.

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