Laser & Optoelectronics Progress, Volume. 60, Issue 19, 1900003(2023)

Research Status and Development Trend of Laser Cladding Process Optimization Method

Jiangtao Gong1, Linsen Shu1,2、*, Jiasheng Wang1,2, Jiahao Li1, and Jingpeng Qin1
Author Affiliations
  • 1School of Mechanical Engineering, Shaanxi University of Technology, Hanzhong 723001, Shaanxi , China
  • 2Shaanxi Key Laboratory of Industrial Automation, Hanzhong 723001, Shaanxi , China
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    References(67)

    [1] Liang W X, Yang Y, Jin K et al. Morphology prediction of coaxial powder feeding multichannel laser cladding layer based on response surface[J]. Laser & Optoelectronics Progress, 59, 0114012(2022).

    [2] Qiu H X, Yu W B, Song J L et al. Numerical simulation of laser cladding 316L/H13+20%WC composite coating on H13 steel surface[J]. Laser & Optoelectronics Progress, 59, 0314002(2022).

    [3] Janicki D. Laser cladding of Inconel 625-based composite coatings reinforced by porous chromium carbide particles[J]. Optics & Laser Technology, 94, 6-14(2017).

    [4] Cui L J, Li H Y, Guo S R et al. Laser cladding cracks recognition based on deep learning combined convolutional block attention module[J]. Laser & Optoelectronics Progress, 58, 2014001(2021).

    [5] Chen Z X, Zhou H M, Xu C X. Cladding crack in laser cladding: a review[J]. Laser & Optoelectronics Progress, 58, 0700006(2021).

    [6] Wang Z, Sun W L, Huang H B et al. Effect of ultrasonic vibrations on quality of laser cladding layer with low overlap rate[J]. Laser & Optoelectronics Progress, 56, 141402(2019).

    [7] Tong T, Zhu J L, Liang X. Research on multilayer and multipass laser cladding process for U75V rails[J]. Laser & Optoelectronics Progress, 58, 0714005(2021).

    [8] Du Y B, Zhou Z J, Xu L et al. Laser cladding process parameter optimization method based on grey relational analysis and ACDE algorithm[J]. Computer Integrated Manufacturing Systems, 28, 149-160(2022).

    [9] Zhao Y, Yu G, He X L et al. Research on laser cladding processing for 38MnVS6 by PCA-TOPSIS method[J]. Acta Armamentarii, 40, 2537-2544(2019).

    [10] Bax B, Rajput R, Kellet R et al. Systematic evaluation of process parameter maps for laser cladding and directed energy deposition[J]. Additive Manufacturing, 21, 487-494(2018).

    [11] Lian G F, Yao M P, Chen C R et al. Control of the quality and efficiency of multi-track overlapping laser cladding[J]. Surface Technology, 47, 229-239(2018).

    [12] Wang D S, Yang Y W, Tian Z J et al. Process optimization of thick nanostructured ceramic coating by laser multi-layer cladding based on neural network and genetic algorithm[J]. Chinese Journal of Lasers, 40, 0903001(2013).

    [13] Zhao R H. Experimental research on laser cladding molten pool detection and process[D](2021).

    [14] Gao J, Song D Y, Feng J W. Influence of processing parameters on geometrical features of CBN coatings by laser cladding on titanium alloy surface[J]. Surface Technology, 44(2015).

    [15] Zhao S G, Li C L, Cheng C. Effect of laser cladding parameters on quality of coating and optimization selection of parameters[J]. Hot Working Technology, 44, 82-85(2015).

    [16] Jiang J B, Cheng Y, Huang X et al. Performance of WC reinforced Ni-based coating on 45 steel surface by laser cladding[J]. Applied Laser, 39, 24-34(2019).

    [17] Lei J F, Qi W J, Xie Y D et al. Optimization of process parameters of laser cladding Ni60-25%WC coating on U71Mn steel[J]. Surface Technology, 47, 66-71(2018).

    [18] Chen Y G. Study on wear resistance of Ni-based alloy coating reinforced with WC particles by laser cladding[J]. Hot Working Technology, 51, 106-109(2022).

    [19] Ji X. The optimization of processing parameters and experimental investigation on metal components fabricated by laser cladding[D](2008).

    [20] Lin J Y. Multi-objective optimization for hardness and wear resistance of laser cladding layer on die[D](2021).

    [21] Ma R B, Dong L H, Wang H D et al. Research on contact fatigue life prediction of thermally sprayed coating based on central composite design[J]. Acta Armamentarii, 38, 561-567(2017).

    [22] Yang G B. Optimization design of switched reluctance motor based on response surface method and genetic algorithm[D](2019).

    [23] Han B Y, Xu W W, Zhu S et al. Research on multi-factor parameter optimization methods for quality control of plasma spraying coatings: a review[J]. Materials Reports, 35, 21105-21112(2021).

    [24] Lian G F, Yang S, Chen C R et al. Study on single-track profile control method for laser cladding trajectory[J]. Laser & Infrared, 48, 438-446(2018).

    [25] Xu X C, Wen H J, Wang J Y et al. Optimization of laser cladding conditions in response surface method for repairing damaged alloy parts[J]. Chinese Journal of Vacuum Science and Technology, 38, 615-620(2018).

    [26] Wu S, Liu Z H, Huang X F et al. Process parameter optimization and EBSD analysis of Ni60A-25% WC laser cladding[J]. International Journal of Refractory Metals and Hard Materials, 101, 105675(2021).

    [27] Wu T, Shi W Q, Xie L Y et al. Forming quality control method of laser cladding Fe-based TiC composite coating[J]. Laser Technology, 46, 344-354(2022).

    [28] Yu T B, Yang L, Zhao Y et al. Experimental research and multi-response multi-parameter optimization of laser cladding Fe313[J]. Optics & Laser Technology, 108, 321-332(2018).

    [29] Zhao D D, Jiao F. Optimization of laser cladding process parameters of 35CrMoV piston rod based on grey correlation analysis[J]. Acta Armamentarii, 39, 2073-2080(2018).

    [30] Lü Y B[M]. Systems engineering, 184-186(2006).

    [31] Liang W X, Yang Y, Qi K et al. Quality evaluation of multi-path laser cladding coatings based on integrated fuzzy comprehensive evaluation and improved analytical hierarchy process method[J]. Surface and Coatings Technology, 427, 127816(2021).

    [32] 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, 97, 180-186(2017).

    [33] Shayanfar P, Daneshmanesh H, Janghorban K. Parameters optimization for laser cladding of Inconel 625 on ASTM A592 steel[J]. Journal of Materials Research and Technology, 9, 8258-8265(2020).

    [34] Parandoush P, Hossain A. A review of modeling and simulation of laser beam machining[J]. International Journal of Machine Tools and Manufacture, 85, 135-145(2014).

    [35] Gao Y L, Yang Q W, Wang X F et al. Overview of new swarm intelligent optimization algorithms[J]. Journal of Zhengzhou University (Engineering Science), 43, 21-30(2022).

    [36] Bakhtiyari A N, Wang Z W, Wang L Y et al. A review on applications of artificial intelligence in modeling and optimization of laser beam machining[J]. Optics & Laser Technology, 135, 106721(2021).

    [37] Casalino G. Computational intelligence for smart laser materials processing[J]. Optics & Laser Technology, 100, 165-175(2018).

    [38] Jin F, Fan J J, Tan Y D[M]. Principle and application of neural network and neural computer(1991).

    [39] Wang L, Zhou G X, Wu Q D. Artificial neural network theory application in control field[J]. Journal of Tongji University, 29, 357-361(2001).

    [40] Feenstra D R, Molotnikov A, Birbilis N. Utilisation of artificial neural networks to rationalise processing windows in directed energy deposition applications[J]. Materials & Design, 198, 109342(2021).

    [41] Ahmed W A M, Saad E S M, Aziz E S A. Modified back propagation algorithm for learning artificial neural networks[C], 345-352(2001).

    [42] Yang Z K, Wang H J, Liu M et al. Optimization of spraying process parameters for Fe-based alloy based on BP neural network model[J]. Surface Technology, 44, 1-6(2015).

    [43] Aggarwal K, Urbanic R J, Saqib S M. Development of predictive models for effective process parameter selection for single and overlapping laser clad bead geometry[J]. Rapid Prototyping Journal, 24, 214-228(2018).

    [44] Fan P F, Zhang G. Prediction on geometrical characteristics of cermet laser cladding based on linear regression and neural network[J]. Surface Technology, 48(2019).

    [45] Jiang W W, Fu G Y, Zhang J P et al. Prediction of geometrical shape of coaxial wire feeding cladding in three-beam[J]. Infrared and Laser Engineering, 49, 0305005(2020).

    [46] Akbari M, Saedodin S, Panjehpour A et al. Numerical simulation and designing artificial neural network for estimating melt pool geometry and temperature distribution in laser welding of Ti6Al4V alloy[J]. Optik, 127, 11161-11172(2016).

    [47] Li Q, Li T, Wu Z P et al. Prediction of laser cladding layer area and porosity based on neural network[J]. Applied Laser, 40, 29-34(2020).

    [48] Jiang S J, Liu W J, Nan L L. Laser cladding height prediction based on neural network[J]. Journal of Mechanical Engineering, 45(2009).

    [49] Guo S R, Chen Z J, Cai D B et al. Prediction of simulating and experiments for Co-based alloy laser cladding by HPDL[J]. Physics Procedia, 50, 375-382(2013).

    [50] Peng B B, Yan X G, Du J. Surface Quality Prediction Based on BP and RBF neural networks full text replacement[J]. Surface Technology, 49(2020).

    [51] Xu J L, Tan W S, Hu Z R et al. Application of RBF neural network in the prediction of dilution ratio of laser cladding cobalt based alloy coating[J]. Applied Laser, 41, 752-757(2021).

    [52] Zeinali M, Khajepour A. Development of an adaptive fuzzy logic-based inverse dynamic model for laser cladding process[J]. Engineering Applications of Artificial Intelligence, 23, 1408-1419(2010).

    [53] Dong R Y. Research and application of meta-heuristic optimization algorithms[D](2019).

    [54] Zhao C, Liu Y G, Chen L et al. Research and development trend of multi-UAV path planning based on metaheuristic algorithm[J]. Control and Decision, 37, 1102-1115(2022).

    [55] Yang C. Research on feedback multi-agent genetic algorithm based on multi-objective optimization[D](2021).

    [56] Zhao K, Liang X D, Wang W et al. Multi-objective optimization of coaxial powder feeding laser cladding based on NSGA-Ⅱ[J]. Chinese Journal of Lasers, 47, 0102004(2020).

    [57] Wang Y Y, Li J H, Shu L S et al. Multi-objective optimization of laser cladding parameters based on RSM and NSGA-Ⅱ algorithm[J]. Laser & Optoelectronics Progress, 59, 0714004(2022).

    [58] Shao X G, Yang H Z, Chen G. Parameters selection and application of support vector machines based on particle swarm optimization algorithm[J]. Control Theory & Applications, 23(2006).

    [60] Ma M Y, Xiong W J, Lian Y et al. Modeling and optimization for laser cladding via multi-objective quantum-behaved particle swarm optimization algorithm[J]. Surface and Coatings Technology, 381, 125129(2020).

    [61] Yang Y W, Tian Z J, Pan H et al. Geometry quality prediction of Ni-based superalloy coating by laser cladding based on neural network and genetic algorithm[J]. Transactions of the China Welding Institution, 34(2013).

    [62] Wen H J, Meng X L, Xu X C et al. Multi-objective optimization of laser cladding process parameters based on neural network and genetic algorithm[J]. Applied Laser, 39, 734-740(2019).

    [63] Liu G C, Huang B. Prediction of Ni-based alloy cladding coatings topography based on GA-BP neural network[J]. Applied Laser, 38, 527-535(2018).

    [64] Liu H M, Qin X P, Huang S et al. Geometry modeling of single track cladding deposited by high power diode laser with rectangular beam spot[J]. Optics and Lasers in Engineering, 100, 38-46(2018).

    [65] Pang Y F, Fu G Y, Wang M Y et al. Parameter optimization of high deposition rate laser cladding based on the response surface method and genetic neural network model[J]. Chinese Journal of Lasers, 48, 0602112(2021).

    [66] Pant P, Chatterjee D. Prediction of clad characteristics using ANN and combined PSO-ANN algorithms in laser metal deposition process[J]. Surfaces and Interfaces, 21, 100699(2020).

    [67] Ni L B, Liu J C, Wu Y T et al. Optimization of laser cladding process variables based on neural network and particle swarm optimization algorithms[J]. Chinese Journal of Lasers, 38, 0203003(2011).

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    Jiangtao Gong, Linsen Shu, Jiasheng Wang, Jiahao Li, Jingpeng Qin. Research Status and Development Trend of Laser Cladding Process Optimization Method[J]. Laser & Optoelectronics Progress, 2023, 60(19): 1900003

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    Paper Information

    Category: Reviews

    Received: Apr. 25, 2022

    Accepted: Jun. 13, 2022

    Published Online: Sep. 20, 2023

    The Author Email: Linsen Shu (shulinsen19@163.com)

    DOI:10.3788/LOP221408

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