Abstract
Laser cladding is a complex manufacturing process involving more than 19 variables related to laser source, workpiece movement, powder-substrate material combinations, clad geometry, powder flow dynamics, shrouding gas flow and so on. Significant research efforts have been directed to analytical-numerical-empirical modelling of laser cladding and also in-process monitoring and control of the process. Still, due to complicated physics there is a dearth of simple analytical model for estimation of dilution in laser cladding. Its experimental measurement requires suitable micrographs of the clad cross section perpendicular to the clad path. This is a time-consuming and destructive way of measurement. Numerical models are time consuming to evaluate and hence not suitable for fast decision making or real-time control implementation. The analytical models available, despite having many approximations, are a little complicated, require fair amount computer programming and often need suitable prior guessing of range of output parameters for adjustment of constant values in the models. This poses some challenges for use and having an intuitive guidance, for a beginner/unskilled operator. Besides, their complexity may erect barrier in the way of their implementation for real time monitoring and control. This work proposes a simple linear regression model, formed based on energy balance approach, to estimate dilution in laser cladding. After fitting to a set of data, within a suitable process parameter-window, for a particular clad-substrate material combination, this model can estimate dilution as a function of input/easily measureable parameters, viz. laser power, scan speed, clad width and clad height. The model fitted well to the experimental data taken from literature.
Similar content being viewed by others
Abbreviations
- \( \lambda \) :
-
dilution (fraction)
- \( A_{\text{c}} \) :
-
clad cross sectional area above initial substrate surface
- \( A_{\text{s}} \) :
-
cross sectional area of clad layer dilution below initial substrate surface
- \( h_{\text{c}} \) :
-
clad height above initial substrate surface
- \( h_{\text{s}} \) :
-
clad layer dilution depth below initial substrate surface
- \( w_{\text{p}} \) :
-
width of the clad pass (perpendicular to the clad velocity vector)
- \( l_{\text{c}} \) :
-
length of the clad pass (along the clad velocity vector)
- \( \eta_{1} \) :
-
fraction of the laser energy being coupled into the cladding process
- \( P \) :
-
laser power
- \( v \) :
-
laser scan speed
- \( \rho^{\text{c}} \) :
-
density of the clad material in solid state
- \( C_{\text{p}}^{\text{c}} \) :
-
specific heat of the clad material in solid state
- \( \rho^{\text{c'}} \) :
-
density of the clad material in liquid state
- \( C_{\text{p}}^{{{\text{c}}{\prime }}} \) :
-
specific heat of the clad material in liquid state
- \( T_{\text{m}}^{\text{c}} \) :
-
melting temperature of the clad material
- \( T_{0}^{\text{c}} \) :
-
initial temperature of the clad material
- \( \rho^{\text{s}} \) :
-
density of the substrate material in solid state
- \( C_{\text{p}}^{\text{s}} \) :
-
specific heat of the substrate material in solid state
- \( T_{\text{m}}^{\text{s}} \) :
-
melting temperature of the substrate material
- \( T_{0}^{\text{s}} \) :
-
initial temperature of the substrate material
- \( \eta_{2} \) :
-
fraction of input energy being conducted into the substrate
- \( C_{1} \), \( C_{2} \), and \( C_{3} \) :
-
constants of regression
References
Majumdar J D and Manna I 2003 Laser processing of materials. Sadhana 28: 495–562
Kim J D and Peng Y 2000 Melt pool shape and dilution of laser cladding with wire feeding. J. Mater. Process. Tech. 104: 284–293
Toyserkani E, Khajepour A and Corbin S F 2004 Laser cladding. CRC press, Durgapur, India, pp. 21
Fathi A, Toyserkani E, Khajepour A and Durali M 2006 Prediction of melt pool depth and dilution in laser powder deposition. J. Phys. D Appl. Phys. 39: 2613–2623
Pinkerton A J and Li L 2004 Modelling the geometry of a moving laser melt pool and deposition track via energy and mass balances. J. Phys. D Appl. Phys. 37: 1885–1895
Bamberger M, Kaplan W D, Medres B and Shepeleva L 1998 Calculation of process parameters for laser alloying and cladding. J. Laser. Appl. 10: 29–33
Colaco R, Costa L, Guerra R and Vilar R 1996 A simple correlation between the geometry of laser cladding tracks and the process parameters. In: Laser processing: surface treatment and film deposition. Springer, Dordrecht, pp. 421–429
Kahlen F J and Kar A 2001 Tensile strengths for laser-fabricated parts and similarity parameters for rapid manufacturing. J. Manuf. Sci. E.-T. ASME 12: 38–44
Hu D and Kovacevic R 2003 Sensing, modeling and control for laser-based additive manufacturing. Int. J. Mach. Tool Manuf. 43: 51–60
Toyserkani E, Khajepour A and Corbin S 2003 Three-dimensional finite element modeling of laser cladding by powder injection: effects of powder feedrate and travel speed on the process. J. Laser Appl. 15: 153–160
Hoadley A and Rappaz M 1992 A thermal model of laser cladding by powder injection. Metall. Trans. B 23: 631-642
Boddu M R, Musti S, Landers R G, Agarwal S and Liou F W 2001 Empirical modeling and vision based control for laser aided metal deposition process. In: Proceedings of the Solid Freeform Fabrication Symposium, pp. 452–459
Farshidianfar M H, Khajepour A and Gerlich A 2016 Real-time control of microstructure in laser additive manufacturing. Int. J. Adv. Manuf. Technol. 82: 1173–1186
Cao X and Ayalew B 2015 Multivariable predictive control of laser-aided powder deposition processes. In: American Control Conference (ACC), IEEE, pp. 3625–3630
Song L, Bagavath-Singh V, Dutta B and Mazumder J 2012 Control of melt pool temperature and deposition height during direct metal deposition process. Int. J. Adv. Manuf. Technol. 58: 247–256
Rodriguez-Araujo J, Rodriguez-Andina J J, Farina J, Vidal F, Mato J L and Montealegre M A 2012 Industrial laser cladding systems: FPGA-based adaptive control. IEEE Ind. Electron. Mag. 6(4): pp. 35–46
Muvvala G, Karmakar D P and Nath A K 2017 Online monitoring of thermo-cycles and its correlation with microstructure in laser cladding of nickel based super alloy. Opt. Laser Eng. 88: 139–152
Muvvala G, Karmakar D P and Nath A K 2017 Online assessment of TiC decomposition in laser cladding of metal matrix composite coating. Mater. Des. 121: 310–320
Muvvala G, Karmakar D P and Nath A K 2017 Monitoring and assessment of tungsten carbide wettability in laser cladded metal matrix composite coating using an IR pyrometer. J. Alloy. Compd. 714: 514–521
Muvvala G, Karmakar, D P and Nath A K 2018 In-process detection of microstructural changes in laser cladding of in-situ Inconel 718/TiC metal matrix composite coating. J. Alloy. Compd. 740: 545–558
Qu C C, Li J, Bai L L, Shao J Z, Song R and Chen J L 2015 Effects of the thickness of the pre-placed layer on microstructural evolution and mechanical properties of the laser-clad coatings. J. Alloy. Compd. 644: 450–463
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Chakraborty, S.S., Dutta, S. Estimation of dilution in laser cladding based on energy balance approach using regression analysis. Sādhanā 44, 150 (2019). https://doi.org/10.1007/s12046-019-1134-9
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s12046-019-1134-9