journal article·2025-11

Study on the forming performance of alternating magnetic field-assisted laser cladding based on data-enhanced CPO-TCN-BiLSTM and SMS-EMOA composite algorithm

Jiabo Liu, Chang Li, Yuhao Wang, Sen Wang, Xing Han

Applied Physics A· Volume 131· Issue 11
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OpenAlex topics: Additive Manufacturing Materials and Processes · Welding Techniques and Residual Stresses · Technology Assessment and Management

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References (76)

Show source-provided references
1. I.O. Troyanchuk, S.V. Trukhanov, H. Szymczak, J. Przewoznik, K. Bärner, Phase transitions in La1-xCaxMnO3-x/2 manganites. J. Exp. Theor. Phys. 93, 161–167 (2001). https://doi.org/10.1134/1.139153310.1134/1.1391533 ↗
2. M.A. Almessiere, N.A. Algarou, Y. Slimani, D.S. Klygach, A. Baykal, A.D. Korkmaz, T.I. Zubar, M.V. Silibin, M.G. Vakhitov, A. UI-Hamid, S.V. Trukhanov, A.V. Trukhanov, Structure, magnetic and electrodynamic properties of SrInxFe12-xO4/Ni0.5Zn0.5Fe2O4 composites at low temperature. J. Am. Ceram. Soc. 107, 4936–4948 (2024). https://doi.org/10.1111/jace.1979810.1111/jace.19798 ↗
3. H. Alhashmi, M.A. Khan, P.R. Budarapu, S. Kumar, Stress transfer in three-phase composites: modeling of radially graded interphases with proof of concept from 3D printed composite testing. Mech. Adv. Mater. Struct. 1–18 (2025). https://doi.org/10.1080/15376494.2024.244358110.1080/15376494.2024.2443581 ↗
4. K.P. Chandra, P.R. Budarapu, Design and analysis of lithium-ion pouch cell with LMO-NMC blended cathode using coupled thermo-electro-chemical model. J. Energy Storage 78, 109958 (2024). https://doi.org/10.1016/j.est.2023.10995810.1016/j.est.2023.109958 ↗
5. Y. Yao, D.I. Tishkevich, T.N. Vershinina, T.I. Zubar, S. Lu, A.A. Rotkovich, A.A. Bondaruk, M.I. Sayyed, Q. Weng, S.V. Trukhanov, A.V. Trukhanov, Structure and shielding properties of the unsupported Bi films electrodeposited in galvanostatic and pulse regimes. Ceram. Int. 50, 16181–16189 (2024). https://doi.org/10.1016/j.ceramint.2024.02.09810.1016/j.ceramint.2024.02.098 ↗
6. D.A. Alshammari, H.H.A. El-Gawad, M. Abdullah, S. Manzoor, Z.M. El-Bahy, H.A. Alzahrani, N. Raza, A.M.A. Henaish, S. Trukhanov, M.I. Sayyed, D. Tishkevich, A. Trukhanov, CuTe supported on graphitic carbon nitride nanocomposite as an effective electrocatalyst for oxidation of water in basic media. J. Electroanal. Chem. 953, 118018 (2024). https://doi.org/10.1016/j.jelechem.2023.11801810.1016/j.jelechem.2023.118018 ↗
7. V.A. Turchenko, A.V. Trukhanov, I.A. Bobrikov, S.V. Trukhanov, A.M. Balagurov, Study of the crystalline and magnetic structures of BaFe11.4Al0.6O19 in a wide temperature range. Journal of Surface Investigation. X-ray, Synchrotron and Neutron Techniques 9, 17–23 (2015). https://doi.org/10.1134/S102745101501017610.1134/S1027451015010176 ↗
8. A.V. Trukhanov, V.G. Kostishyn, L.V. Panina, V.V. Korovushkin, V.A. Turchenko, P. Thakur, A. Thakur, Y. Yang, D.A. Vinnik, E.S. Yakovenko, L.Y. Matzui, E.L. Trukhanova, S.V. Trukhanov, Control of electromagnetic properties in substituted M-type hexagonal ferrites. J. Alloys Compd. 754, 247–256 (2018). https://doi.org/10.1016/j.jallcom.2018.04.15010.1016/j.jallcom.2018.04.150 ↗
9. Y. Zhang, X. Xu, Machine learning surface roughnesses in turning processes of brass metals. Int. J. Adv. Manuf. Technol. 121, 2437–2444 (2022). https://doi.org/10.1007/s00170-022-09498-110.1007/s00170-022-09498-1 ↗
10. Y. Zhang, X. Xu, Machine learning cutting force, surface roughness, and tool life in high speed turning processes. Manuf. Lett. 29, 84–89 (2021). https://doi.org/10.1016/j.mfglet.2021.07.00510.1016/j.mfglet.2021.07.005 ↗
11. Y. Zhang, X. Xu, Predicting the material removal rate during electrical discharge diamond grinding using the Gaussian process regression: a comparison with the artificial neural network and response surface methodology. Int. J. Adv. Manuf. Technol. 113, 1527–1533 (2021). https://doi.org/10.1007/s00170-021-06701-710.1007/s00170-021-06701-7 ↗
12. Y. Zhang, X. Xu, Predicting springback radii and angles in air bending of high-strength sheet steel through Gaussian process regressions. Int. J. Interact. Des. Manuf. 16, 863–870 (2022). https://doi.org/10.1007/s12008-022-00945-710.1007/s12008-022-00945-7 ↗
13. X. Xu, Y. Zhang, Corn cash price forecasting with neural networks. Comput. Electron. Agric. 184, 106120 (2021). https://doi.org/10.1016/j.compag.2021.10612010.1016/j.compag.2021.106120 ↗
14. X. Xu, Y. Zhang, House price forecasting with neural networks. Intell. Syst. Appl. 12, 200052 (2021). https://doi.org/10.1016/j.iswa.2021.20005210.1016/j.iswa.2021.200052 ↗
15. X. Xu, Y. Zhang, House price forecasting with neural networks[J]. Intell. Syst. Appl. 12, 200052 (2021). https://doi.org/10.1016/j.engappai.2023.10687010.1016/j.engappai.2023.106870 ↗
16. X. Xu, Y. Zhang, A gaussian process regression machine learning model for forecasting retail property prices with bayesian optimizations and cross-validation. Decis. Anal. J. 8, 100267 (2023). https://doi.org/10.1016/j.dajour.2023.10026710.1016/j.dajour.2023.100267 ↗
17. X. Xu, Corn cash price forecasting[J]. Am. J. Agric. Econ. 102(4), 1297–1320 (2020). https://doi.org/10.1002/ajae.1204110.1002/ajae.12041 ↗
18. X. Xu, Y. Zhang, Individual time series and composite forecasting of the Chinese stock index. Mach. Learn. Appl. 5, 100035 (2021). https://doi.org/10.1016/j.mlwa.2021.10003510.1016/j.mlwa.2021.100035 ↗
19. Y. Liu, Y. Ding, L. Yang et al., Research and progress of laser cladding on engineering alloys: a review. J. Manuf. Process. 66, 341–363 (2021). https://doi.org/10.1016/j.jmapro.2021.03.06110.1016/j.jmapro.2021.03.061 ↗
20. M. Richert, M. Dudek, D. Sala, Surface quality as a factor affecting the functionality of products manufactured with metal and 3D printing technologies. Materials 17(21), 5371 (2024). https://doi.org/10.3390/ma1721537110.3390/ma17215371 ↗
21. W.Y.S. Lim, J. Cao, A. Suwardi et al., Recent advances in laser-cladding of metal alloys for protective coating and additive manufacturing. J. Adhes. Sci. Technol. 36(23–24), 2482–2504 (2022). https://doi.org/10.1080/01694243.2022.208549910.1080/01694243.2022.2085499 ↗
22. J. Lv, Y. Liang, X. Xu et al., Performance-control-orientated hybrid metal additive manufacturing technologies: state of the art, challenges, and future trends. Int. J. Extrem. Manuf. 6(3), 032009 (2024). https://doi.org/10.1088/2631-7990/ad331510.1088/2631-7990/ad3315 ↗
23. J. Liu, Z. Zhou, WC-304 Powder/Screen Laser Composite Cladding Process and Simulation Research. JOM. 77(4), 2013–2027 (2025). https://doi.org/10.1007/s11837-024-07015-1.10.1007/s11837-024-07015-1 ↗
24. M. Li, K. Huang, X. Yi, Crack formation mechanisms and control methods of laser cladding coatings: a review. Coatings 13(6), 1117 (2023). https://doi.org/10.3390/coatings1306111710.3390/coatings13061117 ↗
25. Y. Liang, Z.Y. Liao, L.L. Zhang et al., A review on coatings deposited by extreme high-speed laser cladding: processes, materials, and properties. Opt. Laser Technol. 164, 109472 (2023). https://doi.org/10.1016/j.optlastec.2023.10947210.1016/j.optlastec.2023.109472 ↗
26. J. Wang, R. Zhu, Y. Liu et al., Understanding melt pool characteristics in laser powder bed fusion: an overview of single-and multi-track melt pools for process optimization. Advanced Powder Materials 2(4), 100137 (2023). https://doi.org/10.1016/j.apmate.2023.10013710.1016/j.apmate.2023.100137 ↗
27. S. Zhong, X. Guo, A. Zhou et al., Fundamentals and recent progress in magnetic field-assisted CO2 capture and conversion. Small 20(5), 2305533 (2024). https://doi.org/10.1002/smll.20230553310.1002/smll.202305533 ↗
28. Y. Zhang, Y. Tang, X. Yang et al., Magnetic field-assisted photocatalysis: mechanisms, devices, and applications. Small Methods , 2402041 (2025). https://doi.org/10.1002/smtd.20240204110.1002/smtd.202402041 ↗
29. H. Mu, S. Luo, L. Wang et al., Microstructure and mechanical properties of aluminum alloy laser welded joint assisted by alternating magnetic field. J. Mater. Res. Technol. 33, 6842–6852 (2024). https://doi.org/10.1016/j.jmrt.2024.11.05410.1016/j.jmrt.2024.11.054 ↗
30. W. Li, Y. Yang, K. Han et al., Study on the microstructure and wear resistance of Fe-Co-Sm alloy coatings with different Sm contents assisted by alternating magnetic field during laser cladding. Mater. Today Commun. 41, 110986 (2024). https://doi.org/10.1016/j.mtcomm.2024.11098610.1016/j.mtcomm.2024.110986 ↗
31. J. Liu, C. Li, S. Li et al., Study on transfer mechanism of mass in laser cladding Fe60 process assisted by alternating magnetic field. J. Mater. Sci. (2025). https://doi.org/10.1007/s10853-025-10954-y10.1007/s10853-025-10954-y ↗
32. Z. Gao, Y. Yuan, Y. Li et al., Effect of alternating magnetic field on microstructure and mechanical properties of Ni60/La2O3 laser cladding layer. Surf. Coat. Technol. 491, 131199 (2024). https://doi.org/10.1016/j.surfcoat.2024.13119910.1016/j.surfcoat.2024.131199 ↗
33. K. Wang, W. Liu, Y. Hong et al., An overview of technological parameter optimization in the case of laser cladding. Coatings 13(3), 496 (2023). https://doi.org/10.3390/coatings1303049610.3390/coatings13030496 ↗
34. Z. Li, Y. Du, G. He et al., Parameters optimization for single-track laser cladding based on MPA-SVR and A-NSGA-III[J]. Opt. Laser Technol. 169, 110069 (2024). https://doi.org/10.1016/j.optlastec.2023.11006910.1016/j.optlastec.2023.110069 ↗
35. L. Zhang, C. Li, X. Wu et al., Bo-cnn-bilstm deep learning model integrating multisource remote sensing data for improving winter wheat yield estimation. Front. Plant Sci. 15, 1500499 (2024). https://doi.org/10.3389/fpls.2024.150049910.3389/fpls.2024.1500499 ↗
36. G. Qu, M. Song, L. Sun, Bridge deformation quantiles prediction with MVO-CNN-BiLSTM based on mixed attention mechanism and periodic multi-source information fusion. J. Civil Struct. Health Monit. 15(5), 1381–1402 (2025). https://doi.org/10.1007/s13349-024-00894-710.1007/s13349-024-00894-7 ↗
37. J. Jiang, X. Wang, X. Li et al., Multi-response optimization of laser cladding assisted by electromagnetic compound field based on grey relational analysis. Weld. Int. 1–14 (2025). https://doi.org/10.1080/09507116.2025.250442710.1080/09507116.2025.2504427 ↗
38. D. Xiang, D. Wang, S. Wang et al., Analysis and optimization of laser cladding Ti-B4C composite coatings based on the interaction and GABP-NSGAII algorithm. Surf. Coat. Technol. 493, 131186 (2024). https://doi.org/10.1016/j.surfcoat.2024.13118610.1016/j.surfcoat.2024.131186 ↗
39. K. Hu, Q. Huang, L. Wang et al., Optimization of multi-track, multi-layer laser cladding process parameters using Gaussian process regression and improved multi-objective particle swarm optimization. Int. J. Adv. Manuf. Technol. 137(7), 3503–3523 (2025). https://doi.org/10.1007/s00170-025-15349-610.1007/s00170-025-15349-6 ↗
40. D. Luo, C. Liu, C. Wang et al., Optimization of multilayer laser cladding process parameters based on NSGA-II-MOPSO algorithm. Opt. Laser Technol. 176, 111025 (2024). https://doi.org/10.1016/j.optlastec.2024.11102510.1016/j.optlastec.2024.111025 ↗
41. N. Beume, B. Naujoks, M. Emmerich, SMS-emoa: multiobjective selection based on dominated hypervolume. J. Eur. J. Oper. Res. 181(3), 1653–1669 (2007). https://doi.org/10.1016/j.ejor.2006.08.00810.1016/j.ejor.2006.08.008 ↗
42. YAKICI E, A multiobjective fleet location problem solved by adaptation of evolutionary algorithms NSGA-II and SMS-EMOA Bir çokamaçlı filo konuşlandırma probleminin NSGA-II ve SMS-EMOA evrimsel algoritmalarının uyarlanması ile çözümü[J]. https://doi.org/10.5505/PAJES.2017.2068810.5505/PAJES.2017.20688 ↗
43. D.I. Tishkevich, S.S. Grabchikov, S.B. Lastovskii et al., Correlation of the synthesis conditions and microstructure for Bi-based electron shields production. J. Alloys Compd. 749, 1036–1042 (2018). https://doi.org/10.1016/j.jallcom.2018.03.28810.1016/j.jallcom.2018.03.288 ↗
44. G. Li, Z. Wang, L. Yao et al., Concentration mixing and melt pool solidification behavior during the magnetic field assisted laser cladding of Fe-Cr-based alloy on 45 steel surface. Surf. Coat. Technol. 445, 128732 (2022). https://doi.org/10.1016/j.surfcoat.2022.12873210.1016/j.surfcoat.2022.128732 ↗
45. C. Chen, Z. Zhang, Y. Cai et al., Research and development status of in situ field assisted laser additive manufacturing: a review. Opt. Laser Technol. 181, 111700 (2025). https://doi.org/10.1016/j.optlastec.2024.11170010.1016/j.optlastec.2024.111700 ↗
46. H. Deng, J. Dong, F. Boi et al., Magnetic field generated during electric current-assisted sintering: from health and safety issues to Lorentz force effects. Metals 10(12), 1653 (2020). https://doi.org/10.3390/met1012165310.3390/met10121653 ↗
47. H. Jiang, X. Zhao, H. Song et al., The effect of high-velocity air-fuel WC-12Co coatings on the wear and corrosion resistance of TC18 titanium alloy. Coatings 13(4), 755 (2023). https://doi.org/10.3390/coatings1304075510.3390/coatings13040755 ↗
48. C. Aliferis, G. Simon, Overfitting, underfitting and general model overconfidence and under-performance pitfalls and best practices in machine learning and AI. Artificial intelligence and machine learning in health care and medical sciences: Best Pract Pitfalls. 477–524 (2024). https://doi.org/10.1007/978-3-031-39355-6_1010.1007/978-3-031-39355-6_10 ↗
49. H. Ghasemzadeh, R.E. Hillman, D.D. Mehta, Toward generalizable machine learning models in speech, language, and hearing sciences: estimating sample size and reducing overfitting. J. Speech Lang. Hear. Res. 67(3), 753–781 (2024). https://doi.org/10.1044/2023_JSLHR-23-0027310.1044/2023_JSLHR-23-00273 ↗
50. A. Gangwal, A. Ansari, I. Ahmad et al., Current strategies to address data scarcity in artificial intelligence-based drug discovery: a comprehensive review. Comput. Biol. Med. 179, 108734 (2024). https://doi.org/10.1016/j.compbiomed.2024.10873410.1016/j.compbiomed.2024.108734 ↗
51. S. Fayaz, S.Z. Ahmad. Shah, NMud din et al., Advancements in data augmentation and transfer learning: a comprehensive survey to address data scarcity challenges. Recent Adv. Comput. Sci. Commun. 17(8), 14–35 (2024). https://doi.org/10.2174/012666255828687523121505432410.2174/0126662558286875231215054324 ↗
52. M. Pivezhandi, A.A. Saifullah, Distribution-Aware, Flow-Matching for Generating Unstructured Data for Few-Shot Reinforcement Learning[J]. arxiv preprint arxiv:2409.14178, 2024. https://doi.org/10.48550/arXiv.2409.1417810.48550/arXiv.2409.14178 ↗
53. C. Zhang, S. Du, H. Song et al., A Hybrid Framework for Spatial Interpolation: Merging Data-driven with Domain Knowledge[J]. arxiv preprint arxiv:2409.00125, 2024. https://doi.org/10.48550/arXiv.2409.0012510.48550/arXiv.2409.00125 ↗
54. B.I. Afolayan, A. Ghosh, J.F. Calderin et al., Emerging trends in machine learning assisted optimization techniques across intelligent transportation Systems[J]. IEEE Access. (2024). https://doi.org/10.1109/access.2024.350177510.1109/access.2024.3501775 ↗
55. J.I. Failenschmid, L.V.D.E. Vogelsmeier, J. Mulder et al., Modelling non-linear psychological processes: reviewing and evaluating non‐parametric approaches and their applicability to intensive longitudinal data[J]. Br. J. Math. Stat. Psychol. (2025). https://doi.org/10.1111/bmsp.1239710.1111/bmsp.12397 ↗
56. W. Li, P. Xu, X. Wang et al., Novel symmetric divergence based importance measures for engineering simulation models under uncertainty. Probabilistic Eng. Mech. 80, 103753 (2025). https://doi.org/10.1016/j.probengmech.2025.10375310.1016/j.probengmech.2025.103753 ↗
57. S.M. Al-Selwi, M.F. Hassan, S.J. Abdulkadir et al., RNN-LSTM: from applications to modeling techniques and beyond—systematic review. J. King Saud Univ. , 102068 (2024). https://doi.org/10.1016/j.jksuci.2024.10206810.1016/j.jksuci.2024.102068 ↗
58. I.D. Mienye, T.G. Swart, G. Obaido, Recurrent neural networks: a comprehensive review of architectures, variants, and applications. Information 15(9), 517 (2024). https://doi.org/10.3390/info1509051710.3390/info15090517 ↗
59. A. Wan, S. Peng, A.L.B. Khalil et al., A novel hybrid BWO-BiLSTM-ATT framework for accurate offshore wind power prediction[J]. Ocean Eng. 312, 119227 (2024). https://doi.org/10.1016/j.oceaneng.2024.11922710.1016/j.oceaneng.2024.119227 ↗
60. Y. Zhang, J. Lu, C. Zhao et al., Chaos optimization algorithms: a survey. Int. J. Bifurc. Chaos 34(16), 2450205 (2024). https://doi.org/10.1142/S021812742450205510.1142/S0218127424502055 ↗
61. Z. Lv, J. Luo, L. Li et al., Weak fault feature extraction method for RV reducer based on CPO-MNAD and LCPSO-BP neural network. IEEE Sens. J. (2025). https://doi.org/10.1109/jsen.2025.357100510.1109/jsen.2025.3571005 ↗
62. A. Ayodeji, Z. Wang, W. Wang et al., Causal augmented convnet: a temporal memory dilated convolution model for long-sequence time series prediction. ISA Trans. 123, 200–217 (2022). https://doi.org/10.1016/j.isatra.2021.05.02610.1016/j.isatra.2021.05.026 ↗
63. L. Plonsky, H. Ghanbar, Multiple regression in L2 research: a methodological synthesis and guide to interpreting R2 values. Mod. Lang. J. 102(4), 713–731 (2018). https://doi.org/10.1111/modl.1250910.1111/modl.12509 ↗
64. W. Wang, Y. Lu, Analysis of the mean absolute error (MAE) and the root mean square error (RMSE) in assessing rounding model[C]//IOP conference series: materials science and engineering. IOP Publishing 324, 012049 (2018). https://doi.org/10.1088/1757-899x/324/1/01204910.1088/1757-899x/324/1/012049 ↗
65. A. Nouri, J. Frisch, van C. Treeck, Statistical methodologies for verification of building energy performance simulation. Building Simulation. IBPSA 17, 1719–1726 (2021). https://doi.org/10.26868/25222708.2021.30538.10.26868/25222708.2021.30538 ↗
66. A. Menchaca-Méndez, E. Montero, S. Zapotecas-Martínez, An improved S-metric selection evolutionary multi-objective algorithm with adaptive resource allocation. IEEE Access 6, 63382–63401 (2018). https://doi.org/10.1109/access.2018.287740210.1109/access.2018.2877402 ↗
67. B. Yuan, P. Chen, E. Wang et al., A systematic optimization method for permanent magnet synchronous motors based on SMS-EMOA. Sensors 24(9), 2956 (2024). https://doi.org/10.3390/s2409295610.3390/s24092956 ↗
68. V.A. Turchenko, S.V. Trukhanov, V.G. Kostishin, F. Damay, F. Porcher, D.S. Klygach, M.G. Vakhitov, D. Lyakhov, D. Michels, B. Bozzo, I. Fina, M.A. Almessiere, Y. Slimani, A. Baykal, D. Zhou, A.V. Trukhanov, Features of structure, magnetic state and electrodynamic performance of SrFe12-xInxO19. Sci. Rep. 11, 18342 (2021). https://doi.org/10.1038/s41598-021-97684-810.1038/s41598-021-97684-8 ↗
69. S.V. Trukhanov, Investigation of stability of ordered manganites. J. Exp. Theor. Phys. 101, 513–520 (2005). https://doi.org/10.1134/1.210322010.1134/1.2103220 ↗
70. L. Falch, C.W. de Silva, Improvement of VIKOR method with application to multi-objective design problems. Int. J. Inform. Technol. Decis. Mak. 22(02), 777–802 (2023). https://doi.org/10.1142/S021962202250049310.1142/S0219622022500493 ↗
71. P. Chatterjee, S. Chakraborty, A comparative analysis of VIKOR method and its variants[J]. Decis. Sci. Lett. 5(4), 469–486 (2016). https://doi.org/10.5267/j.dsl.2016.5.00410.5267/j.dsl.2016.5.004 ↗
72. A.V. Lotov, V.A. Bushenkov, G.K. Kamenev, Interactive Decision Maps: Approximation and Visualization of Pareto frontier[M] (Springer Science & Business Media, 2004)10.1007/978-1-4419-8851-5 ↗
73. Y. Zhu, C. Yu, J. Wu, et al., Mechanism-enhanced data-driven method for the joint optimization of boiler combustion and selective catalytic reduction systems considering gas temperature deviations[J]. Energy. 291, 130432 (2024). https://doi.org/10.1016/j.energy.2024.13043210.1016/j.energy.2024.130432 ↗
74. C.S. Wasson, System Engineering analysis, design, and Development: Concepts, principles, and practices. Wiley (2015)
75. S.V. Trukhanov, Peculiarities of the magnetic state in the system La0.70Sr0.30MnO3-γ (0 ≤ γ ≤ 0.25). J. Exp. Theor. Phys. 100, 95–105 (2005). https://doi.org/10.1134/1.186620210.1134/1.1866202 ↗
76. K. Shang, H. Ishibuchi, W. Chen et al., Hypervolume-optimal µ-distributions on line/plane-based Pareto fronts in three dimensions[J]. IEEE Trans. Evol. Comput. 26(2), 349–363 (2021). https://doi.org/10.1109/tevc.2021.309311410.1109/tevc.2021.3093114 ↗