Journal of Northeastern University(Natural Science) ›› 2025, Vol. 46 ›› Issue (6): 66-75.DOI: 10.12068/j.issn.1005-3026.2025.20240096
• Mechanical Engineering • Previous Articles Next Articles
Bo XIN, Hong-liang LI, Wen-xin SUN, Ming-jun LIU
Received:
2023-08-27
Online:
2025-06-15
Published:
2025-09-01
CLC Number:
Bo XIN, Hong-liang LI, Wen-xin SUN, Ming-jun LIU. Transfer Learning-Based Robotic Belt Grinding Process for NiCo-FGM[J]. Journal of Northeastern University(Natural Science), 2025, 46(6): 66-75.
参数 | IN718 | Stellite 6 |
---|---|---|
弹性模量/GPa | 205 | 230 |
泊松比 | 0.3 | 0.34 |
线膨胀系数×106/℃-1 | 13.0 | 13.6 |
导热系数/[W·(m·K)-1] | 11.4 | 13.6 |
比热容/[J·(kg·K)-1] | 435 | 500 |
Table 1 Thermophysical parameters of IN718 and Stellite 6 (20 ℃)
参数 | IN718 | Stellite 6 |
---|---|---|
弹性模量/GPa | 205 | 230 |
泊松比 | 0.3 | 0.34 |
线膨胀系数×106/℃-1 | 13.0 | 13.6 |
导热系数/[W·(m·K)-1] | 11.4 | 13.6 |
比热容/[J·(kg·K)-1] | 435 | 500 |
参数名称 | 值 |
---|---|
水质量分数×108/% | <4 |
氧质量分数×108/% | <100 |
激光功率/W | 1 800 |
扫描速率/(mm∙min-1) | 600 |
送粉速率/(g∙min-1) | 15 |
Table 2 Experimental parameters
参数名称 | 值 |
---|---|
水质量分数×108/% | <4 |
氧质量分数×108/% | <100 |
激光功率/W | 1 800 |
扫描速率/(mm∙min-1) | 600 |
送粉速率/(g∙min-1) | 15 |
粉末材料 | C | Si | Mn | Cr | Mo | Ti | Fe | Al | Co | Ni |
---|---|---|---|---|---|---|---|---|---|---|
IN718 | 0.05 | 0.71 | 0.16 | 18.17 | 2.32 | 0.93 | 20.89 | 0.63 | — | 余量 |
Stellite 6 | 1.15 | 1.58 | 0.75 | 31.25 | 0.89 | — | 3.54 | — | 余量 | 2.54 |
Table 3 Chemical composition of powder material (mass fraction)
粉末材料 | C | Si | Mn | Cr | Mo | Ti | Fe | Al | Co | Ni |
---|---|---|---|---|---|---|---|---|---|---|
IN718 | 0.05 | 0.71 | 0.16 | 18.17 | 2.32 | 0.93 | 20.89 | 0.63 | — | 余量 |
Stellite 6 | 1.15 | 1.58 | 0.75 | 31.25 | 0.89 | — | 3.54 | — | 余量 | 2.54 |
水平 | 因素 | ||
---|---|---|---|
vs /(m·s-1) | Fn /N | vw /(mm·s-1) | |
1 | 4.712 | 4 | 3 |
2 | 7.069 | 8 | 4.5 |
3 | 9.425 | 12 | 6 |
Table 4 Orthogonal experimental level table of IN718
水平 | 因素 | ||
---|---|---|---|
vs /(m·s-1) | Fn /N | vw /(mm·s-1) | |
1 | 4.712 | 4 | 3 |
2 | 7.069 | 8 | 4.5 |
3 | 9.425 | 12 | 6 |
水平 | 因素 | ||
---|---|---|---|
vs /(m·s-1) | Fn /N | vw /(mm·s-1) | |
1 | 5.312 | 5 | 3.4 |
2 | 7.669 | 9 | 4.9 |
3 | 10.025 | 13 | 6.4 |
Table 5 Orthogonal experimental level table of
水平 | 因素 | ||
---|---|---|---|
vs /(m·s-1) | Fn /N | vw /(mm·s-1) | |
1 | 5.312 | 5 | 3.4 |
2 | 7.669 | 9 | 4.9 |
3 | 10.025 | 13 | 6.4 |
编号 | vw/(mm·s-1) | Fn/N | vs/(m·s-1) | 去除深度h/μm | 预测误差/% | ||
---|---|---|---|---|---|---|---|
神经网络 | 经验公式 | 迁移学习 | |||||
平均 | — | — | — | — | 38.071 | 9.405 | 5.336 |
1 | 5.312 | 5 | 3.4 | 15.545 | 24.451 | 19.735 | 15.840 |
2 | 5.312 | 9 | 4.9 | 28.655 | 43.600 | 8.909 | 5.362 |
3 | 5.312 | 13 | 6.4 | 35.296 | 68.530 | 11.249 | 2.596 |
4 | 7.669 | 5 | 4.9 | 22.247 | 94.781 | 10.042 | 10.366 |
5 | 7.669 | 9 | 6.4 | 28.181 | 35.695 | 7.294 | 2.882 |
6 | 7.669 | 13 | 3.4 | 67.068 | 0.298 | 6.957 | 1.644 |
7 | 10.025 | 5 | 6.4 | 23.442 | 53.298 | 10.014 | 7.087 |
8 | 10.025 | 9 | 3.4 | 59.917 | 6.311 | 5.153 | 0.769 |
9 | 10.025 | 13 | 4.9 | 74.106 | 15.670 | 5.295 | 1.477 |
Table 6 Comparison of empirical formulas, neural networks and transfer learning errors in the removal depth
编号 | vw/(mm·s-1) | Fn/N | vs/(m·s-1) | 去除深度h/μm | 预测误差/% | ||
---|---|---|---|---|---|---|---|
神经网络 | 经验公式 | 迁移学习 | |||||
平均 | — | — | — | — | 38.071 | 9.405 | 5.336 |
1 | 5.312 | 5 | 3.4 | 15.545 | 24.451 | 19.735 | 15.840 |
2 | 5.312 | 9 | 4.9 | 28.655 | 43.600 | 8.909 | 5.362 |
3 | 5.312 | 13 | 6.4 | 35.296 | 68.530 | 11.249 | 2.596 |
4 | 7.669 | 5 | 4.9 | 22.247 | 94.781 | 10.042 | 10.366 |
5 | 7.669 | 9 | 6.4 | 28.181 | 35.695 | 7.294 | 2.882 |
6 | 7.669 | 13 | 3.4 | 67.068 | 0.298 | 6.957 | 1.644 |
7 | 10.025 | 5 | 6.4 | 23.442 | 53.298 | 10.014 | 7.087 |
8 | 10.025 | 9 | 3.4 | 59.917 | 6.311 | 5.153 | 0.769 |
9 | 10.025 | 13 | 4.9 | 74.106 | 15.670 | 5.295 | 1.477 |
目标去除深度h/μm | 磨削法向力/N | ||||
---|---|---|---|---|---|
100%IN718 | 75%IN718 | 50%IN718 | 25%IN718 | 0%IN718 | |
20 | 3.929 | 5.012 | 5.370 | 5.784 | 7.074 |
22 | 4.397 | 5.459 | 5.877 | 6.277 | 7.591 |
24 | 4.865 | 5.905 | 6.383 | 6.761 | 8.095 |
26 | 5.333 | 6.348 | 6.885 | 7.237 | 8.587 |
28 | 5.801 | 6.789 | 7.386 | 7.707 | 9.071 |
30 | 6.270 | 7.227 | 7.885 | 8.174 | 9.549 |
32 | 6.739 | 7.662 | 8.382 | 8.640 | 10.024 |
34 | 7.208 | 8.095 | 8.878 | 9.108 | 10.498 |
36 | 7.678 | 8.527 | 9.372 | 9.580 | 10.974 |
Table 7 Grinding normal forces with different IN718 contents at the same removal depth
目标去除深度h/μm | 磨削法向力/N | ||||
---|---|---|---|---|---|
100%IN718 | 75%IN718 | 50%IN718 | 25%IN718 | 0%IN718 | |
20 | 3.929 | 5.012 | 5.370 | 5.784 | 7.074 |
22 | 4.397 | 5.459 | 5.877 | 6.277 | 7.591 |
24 | 4.865 | 5.905 | 6.383 | 6.761 | 8.095 |
26 | 5.333 | 6.348 | 6.885 | 7.237 | 8.587 |
28 | 5.801 | 6.789 | 7.386 | 7.707 | 9.071 |
30 | 6.270 | 7.227 | 7.885 | 8.174 | 9.549 |
32 | 6.739 | 7.662 | 8.382 | 8.640 | 10.024 |
34 | 7.208 | 8.095 | 8.878 | 9.108 | 10.498 |
36 | 7.678 | 8.527 | 9.372 | 9.580 | 10.974 |
磨削法向力Fn/N | 去除深度/μm | ||||
---|---|---|---|---|---|
100%IN718 | 75%IN718 | 50%IN718 | 25%IN718 | 0%IN718 | |
5.370 | 26.419 | 21.309 | 20.000 | 17.882 | 15.593 |
5.877 | 28.760 | 23.728 | 22.000 | 19.869 | 17.380 |
6.383 | 31.075 | 26.110 | 24.000 | 21.838 | 19.114 |
6.885 | 33.378 | 28.472 | 26.000 | 23.803 | 20.810 |
7.386 | 35.676 | 30.835 | 28.000 | 25.778 | 22.485 |
7.885 | 37.979 | 33.217 | 30.000 | 27.779 | 24.158 |
8.382 | 40.292 | 35.635 | 32.000 | 29.817 | 25.849 |
8.878 | 42.617 | 38.099 | 34.000 | 31.896 | 27.573 |
9.372 | 44.955 | 40.607 | 36.000 | 34.013 | 29.336 |
Table 8 Removal depths of different IN718 contents under the same grinding normal force
磨削法向力Fn/N | 去除深度/μm | ||||
---|---|---|---|---|---|
100%IN718 | 75%IN718 | 50%IN718 | 25%IN718 | 0%IN718 | |
5.370 | 26.419 | 21.309 | 20.000 | 17.882 | 15.593 |
5.877 | 28.760 | 23.728 | 22.000 | 19.869 | 17.380 |
6.383 | 31.075 | 26.110 | 24.000 | 21.838 | 19.114 |
6.885 | 33.378 | 28.472 | 26.000 | 23.803 | 20.810 |
7.386 | 35.676 | 30.835 | 28.000 | 25.778 | 22.485 |
7.885 | 37.979 | 33.217 | 30.000 | 27.779 | 24.158 |
8.382 | 40.292 | 35.635 | 32.000 | 29.817 | 25.849 |
8.878 | 42.617 | 38.099 | 34.000 | 31.896 | 27.573 |
9.372 | 44.955 | 40.607 | 36.000 | 34.013 | 29.336 |
[1] | Loh G H, Pei E, Harrison D, et al. An overview of functionally graded additive manufacturing[J]. Additive Manufacturing, 2018, 23: 34-44. |
[2] | Wang Y F, Chen X Z, Su C C. Microstructure and mechanical properties of Inconel 625 fabricated by wire-arc additive manufacturing[J]. Surface and Coatings Technology, 2019, 374: 116-123. |
[3] | Bobbio L D, Otis R A, Borgonia J P, et al. Additive manufacturing of a functionally graded material from Ti-6Al-4V to Invar: experimental characterization and thermodynamic calculations[J]. Acta Materialia, 2017, 127: 133-142. |
[4] | 于富明. 基于工件曲率的变压力砂带磨削技术研究[D]. 沈阳:东北大学,2017. |
Yu Fu-ming. Research on variable pressure abrasive belt grinding technology based on workpiece curvature [D]. Shenyang: Northeastern University,2017. | |
[5] | 王恭硕. 面向航空发动机整体叶盘材料去除精度要求的机器人磨抛技术研究[D]. 武汉:华中科技大学,2022. |
Wang Gong-shuo. Research on robotic grinding and polishing technology for aero-engine integral blisk material removal accuracy requirements [D]. Wuhan: Huazhong University of Science and Technology, 2022. | |
[6] | 巩亚东,赵显力,张伟健,等.机器人砂带磨削单磨粒材料去除影响因素[J].东北大学学报(自然科学版),2023,44(9):1285-1291. |
Gong Ya-dong, Zhao Xian-li, Zhang Wei-jian,et al. Factors influencing single abrasive material removal for robotic abrasive belt grinding[J]. Journal of Northeastern University(Natural Science), 2023, 44(9): 1285-1291. | |
[7] | Preston F W. The theory and design of plate glass polishing machines [J]. Journal of the Society of Glass Technology, 1927(11):277-281. |
[8] | 计时鸣, 李琛, 谭大鹏, 等. 基于Preston方程的软性磨粒流加工特性[J].机械工程学报, 2011,47(17):156-163. |
Ji Shi-ming, Li Chen, Tan Da-peng, et al. Study on machinability of softness abrasive flow based on Preston equation[J]. Journal of Mechanical Engineering, 2011,47(17):156-163. | |
[9] | 张雷, 袁楚明, 周祖德, 等. 模具曲面抛光时表面去除的建模与试验研究[J]. 机械工程学报, 2002, 38(12):98-102. |
Zhang Lei, Yuan Chu-ming, Zhou Zu-de, et al. Modeling and experiment of material removal in polishing on mold curved surfaces[J]. Chinese Journal of Mechanical Engineering, 2002, 38(12):98-102. | |
[10] | Cabaravdic M, Kuhlenköetter B. Optimising belt grinding processes[J]. Metal Surface, 2005(4):44-47. |
[11] | Himeur Y, Elnour M, Fadli F, et al. Next-generation energy systems for sustainable smart cities: roles of transfer learning[J]. Sustainable Cities and Society, 2022, 85: 104059. |
[12] | Hazarika D, Poria S, Zimmermann R, et al. Conversational transfer learning for emotion recognition[J]. Information Fusion, 2021, 65: 1-12. |
[13] | Lu J, Bebbood V, Hao P, et al. Transfer learning using computational intelligence: a survey[J]. Knowledge-Based Systems, 2015, 80: 14-23. |
[14] | Zhuang F Z, Qi Z Y, Duan K Y, et al. A comprehensive survey on transfer learning[J]. Proceedings of the IEEE, 2021, 109(1): 43-76. |
[15] | Wang J D, Chen Y Q, Feng W J, et al. Transfer learning with dynamic distribution adaptation[J]. ACM Transactions on Intelligent Systems and Technology, 2020, 11(1): 1-25. |
[16] | Tzeng E, Hoffman J, Darrell T, et al. Simultaneous deep transfer across domains and tasks[C]// 2015 IEEE International Conference on Computer Vision(ICCV). Santiago, 2015: 4068-4076. |
[17] | Pardoe D, Stone P. Boosting for regression transfer[C]// 27th International Conference on Machine Learning. Haifa, 2010: 863-870. |
[18] | 杨吉祥,王恭硕,叶葱葱, 等. 一种基于迁移学习的材料去除率模型的建立方法和装置: CN202110230749.5[P]. 2021-07-06. |
Yang Ji-xiang, Wang Gong-shuo, Ye Cong-cong, et al. A method and device for establishing a material removal rate model based on transfer learning: CN202110230749.5 [P]. 2021-07-06. |
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