Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network
Abstract
:1. Introduction
2. Experimental Procedures and Methods
2.1. Experimental Materials
2.2. Design of Orthogonal Experiment
2.3. Microhardness Measurement
2.4. Microstructure Characterization
2.5. Design of BP Neural Network
3. Results and Discussion
3.1. The Effect of Process Parameters on the Dilution Rate
3.2. Analysis of Performance of BP Model
3.3. Microstructure of the AlCoCrFeNi HEA Coating
4. Conclusions
- (1)
- The training performance of the established BP neural network was outstanding, and the maximum relative error between the training value and the predicted value was 1.70%. The prediction performance was excellent, with an average relative error of only 5.89% between the predicted value and the experimental value.
- (2)
- The optimal process parameters were as follows: laser power of 2000 W, a scanning speed of 2 mm/s, and a powder feeding rate of 15 g/min. The dilution rate was 16%, and the microhardness value was 521.6 HV0.3.
- (3)
- The grains of the coatings under this process parameter were equiaxed, with uniform composition distribution, and no serious composition segregation occurred. The microstructure of coatings was still composed of the BCC phase solid solution. The FCC phase does not form in the microstructure due to the excessive Fe element content. These results also prove that the dilution rate under the process parameters does not have a major impact on the microstructure and properties of the AlCoCrFeNi coatings.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Zhou, Z.; Liu, X.; Zhuang, S.; Yang, X.; Wang, M.; Sun, C. Preparation and high temperature tribological properties of laser in-situ synthesized self-lubricating composite coatings containing metal sulfides on Ti6Al4V alloy. Appl. Surf. Sci. 2019, 481, 209–218. [Google Scholar] [CrossRef]
- Zhu, Y.; Liu, X.; Liu, Y.; Wang, G.; Wang, Y.; Meng, Y.; Liang, J. Development and characterization of Co-Cu/Ti3SiC2 self-lubricating wear resistant composite coatings on Ti6Al4V alloy by laser cladding. Surf. Coat. Technol. 2021, 424, 127664. [Google Scholar] [CrossRef]
- Oliveira, U.D.; Ocelík, V.; De Hosson, J.T.M. Analysis of coaxial laser cladding processing conditions. Surf. Coat. Technol. 2005, 197, 127–136. [Google Scholar] [CrossRef]
- Weng, F.; Chen, C.; Yu, H. Research status of laser cladding on titanium and its alloys: A review. Mater. Des. 2014, 58, 412–425. [Google Scholar] [CrossRef]
- Wang, K.; Du, D.; Liu, G.; Pu, Z.; Chang, B.; Ju, J. Microstructure and mechanical properties of high chromium nickel-based superalloy fabricated by laser metal deposition. Mater. Sci. Eng. A 2020, 780, 139185. [Google Scholar] [CrossRef]
- Wang, K.; Du, D.; Liu, G.; Pu, Z.; Chang, B.; Ju, J. A study on the additive manufacturing of a high chromium Nickel-based superalloy by extreme high-speed laser metal deposition. Opt. Laser Technol. 2021, 133, 106504. [Google Scholar] [CrossRef]
- Cantor, B.; Chang, I.T.H.; Knight, P.; Vincent, A.J.B. Microstructural development in equiatomic multicomponent alloys. Mater. Sci. Eng. A 2004, 375, 213–218. [Google Scholar] [CrossRef]
- Yeh, J.; Chen, S.; Lin, S.; Gan, J.; Chin, T.; Shun, T.; Tsau, C.; Chang, S. Nanostructured high-entropy alloys with multiple principal elements: Novel alloy design concepts and outcomes. Adv. Eng. Mater. 2004, 6, 299–303. [Google Scholar] [CrossRef]
- George, E.P.; Curtin, W.A.; Tasan, C.C. High entropy alloys: A focused review of mechanical properties and deformation mechanisms. Acta Mater. 2020, 188, 435–474. [Google Scholar] [CrossRef]
- Qiu, X.; Zhang, Y.; He, L.; Liu, C. Microstructure and corrosion resistance of AlCrFeCuCo high entropy alloy. J. Alloys Compd. 2013, 549, 195–199. [Google Scholar] [CrossRef]
- Zhang, S.; Wu, C.L.; Zhang, C.H.; Guan, M.; Tan, J.Z. Laser surface alloying of FeCoCrAlNi high-entropy alloy on 304 stainless steel to enhance corrosion and cavitation erosion resistance. Opt. Laser Technol. 2016, 84, 23–31. [Google Scholar] [CrossRef]
- Kunce, I.; Polanski, M.; Karczewski, K.; Plocinski, T.; Kurzydlowski, K.J. Microstructural characterisation of high-entropy alloy AlCoCrFeNi fabricated by laser engineered net shaping. J. Alloys Compd. 2015, 648, 751–758. [Google Scholar] [CrossRef]
- Juan, Y.; Li, J.; Jiang, Y.; Jia, W.; Lu, Z. Modified criterions for phase prediction in the multi-component laser-clad coatings and investigations into microstructural evolution/wear resistance of FeCrCoNiAlMox laser-clad coatings. Appl. Surf. Sci. 2019, 465, 700–714. [Google Scholar] [CrossRef]
- Guo, L.; Xiao, D.H.; Wu, W.Q.; Ni, S.; Song, M. Effect of Fe on microstructure, phase evolution and mechanical properties of (AlCoCrFeNi)100-xFex high entropy alloys processed by spark plasma sintering. Intermetallics 2018, 103, 1–11. [Google Scholar] [CrossRef]
- Cai, Y.; Chen, Y.; Manladan, S.M.; Luo, Z.; Gao, F.; Li, L. Influence of dilution rate on the microstructure and properties of FeCrCoNi high-entropy alloy coating. Mater. Des. 2018, 142, 124–137. [Google Scholar] [CrossRef]
- Sun, Y.; Hao, M. Statistical analysis and optimization of process parameters in Ti6Al4V laser cladding using Nd:YAG laser. Opt. Laser Eng. 2012, 50, 985–995. [Google Scholar] [CrossRef]
- Casalino, G. Computational intelligence for smart laser materials processing. Opt. Laser Technol. 2018, 100, 165–175. [Google Scholar] [CrossRef]
- Rong, Y.; Zhang, Z.; Zhang, G.; Yue, C.; Gu, Y.; Huang, Y.; Wang, C.; Shao, X. Parameters optimization of laser brazing in crimping butt using taguchi and BPNN-GA. Opt. Laser Eng. 2015, 67, 94–104. [Google Scholar] [CrossRef]
- Sathiya, P.; Panneerselvam, K.; Soundararajan, R. Optimal design for laser beam butt welding process parameter using artificial neural networks and genetic algorithm for super austenitic stainless steel. Opt. Laser Technol. 2012, 44, 1905–1914. [Google Scholar] [CrossRef]
- Deng, Z.; Chen, T.; Wang, H.; Li, S.; Liu, D. Process parameter optimization when preparing Ti (C, N) ceramic coatings using laser cladding based on a neural network and quantum-behaved particle swarm optimization algorithm. Appl. Sci. 2020, 10, 6331. [Google Scholar] [CrossRef]
- Guo, S.; Chen, Z.; Cai, D.; Zhang, Q.; Kovalenko, V.; Yao, J. Prediction of simulating and experiments for Co-based alloy laser cladding by HPDL. Phys. Procedia 2013, 50, 375–382. [Google Scholar] [CrossRef] [Green Version]
- Xi, W.; Song, B.; Zhao, Y.; Yu, T.; Wang, J. Geometry and dilution rate analysis and prediction of laser cladding. Int. J. Adv. Manuf. Technol. 2019, 103, 4695–4702. [Google Scholar] [CrossRef]
- Manzoni, A.; Daoud, H.; Völkl, R.; Glatzel, U.; Wanderka, N. Phase separation in equiatomic AlCoCrFeNi high-entropy alloy. Ultramicroscopy 2013, 132, 212–215. [Google Scholar] [CrossRef] [PubMed]
- Butler, T.M.; Weaver, M.L. Oxidation behavior of arc melted AlCoCrFeNi multi-component high-entropy alloys. J. Alloys Compd. 2016, 674, 229–244. [Google Scholar] [CrossRef]
- Xu, X.; Cheng, H.; Wu, W.; Liu, Z.; Li, X. Stress corrosion cracking behavior and mechanism of Fe-Mn-Al-C-Ni high specific strength steel in the marine atmospheric environment. Corros. Sci. 2021, 191, 109760. [Google Scholar] [CrossRef]
- Li, Y.; Liu, Z.; Fan, E.; Huang, Y.; Fan, Y.; Zhao, B. Effect of cathodic potential on stress corrosion cracking behavior of different heat-affected zone microstructures of E690 steel in artificial seawater. J. Mater. Sci. Technol. 2021, 64, 141–152. [Google Scholar] [CrossRef]
- Wang, R.; Zhang, K.; Davies, C.; Wu, X. Evolution of microstructure, mechanical and corrosion properties of AlCoCrFeNi high-entropy alloy prepared by direct laser fabrication. J. Alloys Compd. 2016, 694, 971–981. [Google Scholar] [CrossRef]
- Niu, P.; Li, R.; Yuan, T.; Zhu, S.; Chen, C.; Wang, M.; Huang, L. Microstructures and properties of an equimolar AlCoCrFeNi high entropy alloy printed by selective laser melting. Intermetallics 2019, 104, 24–32. [Google Scholar] [CrossRef]
- Liu, Z.; Zhang, X.; Xuan, F.; Wang, Z.; Tu, S. In situ synthesis of TiN/Ti3Al intermetallic matrix composite coatings on Ti6Al4V alloy. Mater. Des. 2012, 37, 268–273. [Google Scholar] [CrossRef]
Element | Al | Co | Cr | Fe | Ni |
---|---|---|---|---|---|
Concentration (%) | 9.8171 | 23.1098 | 20.5944 | 22.8544 | 23.5598 |
Atomic ratio (at.%) | 18.5431 | 19.9760 | 20.1981 | 20.8136 | 20.4691 |
Levels | Factors | ||
---|---|---|---|
P/(kW) | R/(g/min) | V/(mm/s) | |
Level 1 | 2.00 | 15.00 | 6.00 |
Level 2 | 2.50 | 10.00 | 4.00 |
Level 3 | 1.50 | – | 2.00 |
Sample | P (kW) | R (g/min) | V (mm/s) | H (mm) | W (mm) | h (mm) | η (%) | Hv (HV0.3) |
---|---|---|---|---|---|---|---|---|
S-1 | 1.50 | 15.00 | 4.00 | 1.09 | 6.03 | 0.36 | 25 | 491.87 |
S-2 | 2.00 | 15.00 | 4.00 | 1.20 | 6.57 | 0.42 | 26 | 516.50 |
S-3 | 2.50 | 15.00 | 4.00 | 1.38 | 7.43 | 0.68 | 33 | 503.80 |
S-4 | 1.50 | 15.00 | 2.00 | 2.20 | 5.26 | 0.30 | 12 | 499.75 |
S-5 | 2.00 | 15.00 | 2.00 | 2.40 | 6.68 | 0.45 | 16 | 521.60 |
S-6 | 2.50 | 15.00 | 2.00 | 2.10 | 7.08 | 0.69 | 25 | 558.87 |
S-7 | 1.50 | 15.00 | 6.00 | 0.49 | 6.27 | 0.44 | 47 | 259.00 |
S-8 | 2.00 | 15.00 | 6.00 | 0.79 | 7.11 | 0.32 | 29 | 320.97 |
S-9 | 2.50 | 15.00 | 6.00 | 1.02 | 7.78 | 0.55 | 35 | 363.07 |
S-10 | 1.50 | 10.00 | 4.00 | 0.88 | 8.43 | 0.79 | 47 | 333.27 |
S-11 | 2.00 | 10.00 | 4.00 | 0.92 | 6.45 | 0.45 | 33 | 305.43 |
S-12 | 2.50 | 10.00 | 4.00 | 0.81 | 6.91 | 0.48 | 37 | 264.47 |
S-13 | 1.50 | 10.00 | 2.00 | 1.48 | 6.40 | 0.31 | 17 | 467.4 |
S-14 | 2.00 | 10.00 | 2.00 | 1.61 | 7.67 | 0.42 | 21 | 441.97 |
S-15 | 2.50 | 10.00 | 2.00 | 1.58 | 8.10 | 0.49 | 24 | 413.97 |
S-16 | 1.50 | 10.00 | 6.00 | 0.41 | 7.47 | 0.39 | 49 | 351.53 |
S-17 | 2.00 | 10.00 | 6.00 | 0.68 | 8.34 | 0.72 | 51 | 407.63 |
S-18 | 2.50 | 10.00 | 6.00 | 0.72 | 9.38 | 0.82 | 53 | 256.23 |
No. | Sample | Experimental η (%) | Predicted η (%) |
---|---|---|---|
1 | S-1 | 25 | 24.26575 |
2 | S-2 | 26 | 26.06222 |
3 | S-3 | 33 | 32.87267 |
4 | S-4 | 12 | 11.67059 |
5 | S-5 | 16 | 16.06459 |
6 | S-6 | 25 | 25.05431 |
7 | S-8 | 29 | 28.77535 |
8 | S-9 (test) | 35 | 32.99414 |
9 | S-11 (test) | 33 | 30.54423 |
10 | S-12 (test) | 37 | 38.58375 |
11 | S-13 | 17 | 17.01862 |
12 | S-14 | 21 | 21.08211 |
13 | S-15 (test) | 24 | 22.52957 |
14 | S-16 | 49 | 48.90431 |
15 | S-17 | 51 | 50.79725 |
16 | S-18 | 53 | 52.09692 |
Points | Al | Co | Cr | Fe | Ni | N |
---|---|---|---|---|---|---|
A | 38.41 | 6.02 | 9.87 | 13.50 | 6.97 | 25.23 |
B | 14.98 | 19.66 | 17.32 | 30.40 | 17.64 | 0 |
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Li, Y.; Wang, K.; Fu, H.; Zhi, X.; Guo, X.; Lin, J. Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network. Coatings 2021, 11, 1402. https://doi.org/10.3390/coatings11111402
Li Y, Wang K, Fu H, Zhi X, Guo X, Lin J. Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network. Coatings. 2021; 11(11):1402. https://doi.org/10.3390/coatings11111402
Chicago/Turabian StyleLi, Yutao, Kaiming Wang, Hanguang Fu, Xiaohui Zhi, Xingye Guo, and Jian Lin. 2021. "Prediction for Dilution Rate of AlCoCrFeNi Coatings by Laser Cladding Based on a BP Neural Network" Coatings 11, no. 11: 1402. https://doi.org/10.3390/coatings11111402