Optimization of Multi-Track Laser-Cladding Process of Titanium Alloy Based on RSM and NSGA-II Algorithm
Source abstract
Abstract
Titanium alloy is an important material in the 21st century and its consumption in the aerospace and energy fields is increasing. In the production and repair of titanium alloy, the problem of energy saving and consumption reduction is becoming increasingly important. Laser-cladding technology with optimized parameters can bring great economic benefit. In order to obtain the best process parameters of laser-cladding TC4 alloy powder, a method of laser-cladding parameters’ optimization based on the RSM and NSGA-II Algorithm is proposed. The BBD (Box–Behnken Design) experiment scheme was designed by the response surface method. A surrogate model between input variables (laser power, scanning speed, and powder-feeding speed) and response values (macroscopic quality, microhardness, and average friction coefficient) was established. The second generation non-dominant sorting genetic algorithm (NSGA-II) was used to optimize the process parameters and the optimization results were verified by experiments. The results show that the optimum parameters are a laser power of 2600 W, scanning speed of 19.1 mm/s, and powder-feeding rate of 12.2 g/min. The samples prepared with the best process parameters show mainly abrasive wear, accompanied by a small amount of adhesive wear. Its wear depth is 7.71 μm and the average friction coefficient is 0.293. After cladding, the macroscopic quality of the cladding layer is increased by 5.8%, the microhardness is increased by 10.1%, and the average friction coefficient is reduced by 27.6%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Additive Manufacturing Materials and Processes · High Entropy Alloys Studies · Titanium Alloys Microstructure and Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001, China
Shaanxi Provincial Key Laboratory of Industrial Automation, Shaanxi University of Technology, Hanzhong 723001, China
Funding
National Natural Science Foundation of China
References (33)
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1. Samodurova, M., Logachev, I., Shaburova, N., Samoilova, O., Radionova, L., Zakirov, R., Pashkeev, K., Myasoedov, V., and Trofimov, E. (2019). A Study of the Structural Characteristics of Titanium Alloy Products Manufactured Using Additive Technologies by Combining the Selective Laser Melting and Direct Metal Deposition Methods. Materials, 12.10.3390/ma12193269 ↗
2. Analytical modeling of hot behavior of Ti-6Al-4V alloy at large strain10.1016/j.matdes.2018.11.025 ↗
3. Erdakov, I., Glebov, L., Pashkeev, K., Bykov, V., Bryk, A., Lezin, V., and Radionova, L. (2020). Effect of the Ti6Al4V Alloy Track Trajectories on Mechanical Properties in Direct Metal Deposition. Machines, 8.10.3390/machines8040079 ↗
4. Phase constituents and microhardness of laser alloyed Ti–6Al–4 V alloy10.2351/1.4906388 ↗
5. A novel thermal-tuning Fe-based amorphous alloy for automatically recycled methylene blue degradation10.1016/j.matdes.2018.11.031 ↗
6. A study on the influence mechanism and optimization of physical field parameters of electromagnetic-ultrasonic compound field–assisted laser cladding technology10.1007/s40194-021-01144-w ↗
7. Anisotropy of fracture toughness in nanostructured ceramics controlled by grain boundary design10.1016/j.matdes.2018.11.028 ↗
8. Liu, Y., Li, J., Xu, Q., Zhang, Y., Yan, X., Chen, Y., and He, H. (2022). Microstructure and Wear Behavior of TC4 Laser Cladding Modified via SiC and MoS2. Coatings, 12.10.3390/coatings12060792 ↗
9. Surface functionalization of 3D printed polymer scaffolds to augment stem cell response10.1016/j.matdes.2018.11.018 ↗
10. Ultra-rapid transient liquid phase bonding of Mg alloys within 1 s in air by ultrasonic assistance10.1016/j.matdes.2018.11.015 ↗
11. A parametric study of Inconel 625 wire laser deposition10.1016/j.jmatprotec.2013.06.007 ↗
12. Effect of solid phase corrugation on the thermo-mechanical properties of low density flexible cellular polymers10.1016/j.matdes.2018.11.020 ↗
13. Multi-objective optimization analysis of cutting parameters when drilling composite materials with special geometry drills10.1016/j.compstruct.2019.111187 ↗
14. Investigation of the WEDM of Al/B4C/Gr reinforced hybrid composites using the Taguchi method and response surface methodology10.1515/secm-2014-0063 ↗
15. Wang, Q., Zeng, X., Chen, C., Lian, G., and Huang, X. (2020). An Integrated Method for Multi-Objective Optimization of Multi-Pass Fe50/TiC Laser Cladding on AISI 1045 Steel based on Grey Relational Analysis and Principal Component Analysis. Coatings, 10.10.3390/coatings10020151 ↗
16. Study on process optimization of WC-Co50 cermet composite coating by laser cladding10.1016/j.ijrmhm.2019.105133 ↗
17. Statistical analysis and multi-objective process optimization of laser cladding TiC-Inconel718 composite coating10.1016/j.ijleo.2021.166828 ↗
18. Systematic evaluation of process parameter maps for laser cladding and directed energy deposition
19. Application of artificial neural network for the prediction of laser cladding process characteristics at Taguchi-based optimized condition10.1007/s00170-013-5393-z ↗
20. Li, Y., Wang, K., Fu, H., Zhi, X., Guo, X., and Lin, J. (2021). Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network. Coatings, 11.10.3390/coatings11111402 ↗
21. Prediction of 18Ni300 laser cladding topography based on back-propagation neural network and particle swarm optimization10.1088/1742-6596/1775/1/012009 ↗
22. Study on optimization of laser cladding process parameters of aluminum alloys using a prediction model of the neuralgenetic algorithm10.1088/1742-6596/2044/1/012149 ↗
23. ICME guided property design: Room temperature hardness in cemented carbides10.1016/j.matdes.2018.11.029 ↗
24. Yu, J., Sun, W., Huang, H., Wang, W., Wang, Y., and Hu, Y. (2019). Crack Sensitivity Control of Nickel-Based Laser Coating Based on Genetic Algorithm and Neural Network. Coatings, 9.10.3390/coatings9110728 ↗
25. Geometry Characteristics Prediction of Single Track Cladding Deposited by High Power Diode Laser Based on Genetic Algorithm and Neural Network10.1007/s12541-018-0126-8 ↗
26. Sommer, N., Stredak, F., and Böhm, S. (2021). High-Speed Laser Cladding on Thin-Sheet-Substrates—Influence of Process Parameters on Clad Geometry and Dilution. Coatings, 11.10.3390/coatings11080952 ↗
27. Yu, J., Sun, W., Huang, H., and Huang, Y. (2020). Study on the Deformation Control and Microstructures of Thin-Walled Parts Repaired by Laser Cladding. Coatings, 10.10.3390/coatings10040369 ↗
28. Lian, G., Zhang, Y., Zhang, H., Huang, X., Chen, C., and Jiang, J. (2019). Investigation of Geometric Characteristics in Curved Surface Laser Cladding with Curve Path. Metals, 9.10.3390/met9090947 ↗
29. Lian, G., Liu, Z., Zhang, Y., Feng, M., Chen, C., and Jiang, J. (2020). The Forming Control Method of Multi-Track Laser Cladding on Curved Surface. Metals, 10.10.3390/met10081109 ↗
30. Niu, F., Li, Y., Song, C., Yan, X., Zhang, Z., Ma, G., and Wu, D. (2022). Microstructure and Wear Resistance of TiCp/Ti6Al4V Composite Coatings by Follow-Up Ultrasonic-Assisted Laser Additive Manufacturing. Coatings, 12.10.3390/coatings12070986 ↗
31. Tribological behaviour of laser synthesized Ti-Al2O3 coatings on Ti-6Al-4V alloy10.1007/s00170-019-03605-5 ↗
32. Microstructure and Wear Properties of Laser-Cladded cBN/Ti3Al on Pure Titanium10.1007/s13369-017-2585-0 ↗
33. Underwater wire-feed laser deposition of the Ti–6Al–4V titanium alloy10.1016/j.matdes.2019.108284 ↗