Residual stress prediction in laser-assisted milling considering recrystallization effects
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An analytical predictive model for residual stress in laser-assisted milling considering material recrystallization caused by laser preheating is proposed. The laser preheating temperature field is predicted by treating the laser beam as a heat source on top surface following Gaussian distribution. Heat convection between top surface and the environment is factored into account. Isothermal boundary conditions are assumed for the area of interest with conduction within the material. Next, the milling configuration is treated as orthogonal cutting at each instance. All process parameters including cutting depth, cutting speed, and tool geometry are therefore transferred. The recrystallization effect and thus the grain growth are considered through calibrated models describing the dependency of strain rate, strain, and temperature on dynamic recrystallization process for specific alloys using exponent functions. The residual stress is then predicted through the calculation of elastic stress distribution in loading process, actual stress with kinematic hardening, and the stress change during relaxation. The proposed model is validated through experimental measurements on the laser-assisted milling of Si3N4 and Ti-6Al-4V. The predictive model matches the trends of experimental measurements with agreement to an average error less than 30% in all cases. The proposed analytical model is valuable for providing a fast, credible, and physics-based method for the prediction of residual stress in laser-assisted milling of various materials.
KeywordsResidual stress Modeling Laser-assisted milling
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- 6.Le Coz G et al (2010) Residual stresses after dry machining of Inconel 718, experimental results and numerical simulationGoogle Scholar
- 10.Zhou R, Yang W (2016) Analytical modeling of residual stress in helical end milling of nickel-aluminum bronze. Int J Adv Manuf Technol 89(1–4):987–996Google Scholar
- 16.Huang D et al (2001) Computer simulation of microstructure evolution during hot forging of waspaloy and nickel alloy 718Google Scholar
- 17.Furrer D, Goetz R, Shen G (2010) Modeling and simulation of alloy 718 microstructure and mechanical properties. In 7th International Symposium on Superalloy 718 & Derivatives. The Minerals,Metals&Materials Society: Pittsburgh, PennsylvaniaGoogle Scholar
- 19.Pan Z, Feng Y, Liang SY (2017) Material microstructure affected machining: a review. Manuf Rev 4:5Google Scholar
- 20.Pan Z, Feng Y, Lu YT, Lin YF, Hung TP, Hsu FC, Lin CF, Lu YC, Liang SY (2017) Microstructure-sensitive flow stress modeling for force prediction in laser assisted milling of Inconel 718. Manuf Rev 4:6Google Scholar
- 22.Feng Y, Lu YT, Lin YF, Hung TP, Hsu FC, Lin CF, Lu YC, Liang SY (2018) Inverse analysis of the cutting force in laser-assisted milling on Inconel 718. Int J Adv Manuf TechnolGoogle Scholar
- 26.Shen X, Lei S (2010) Experimental study on operating temperature in laser-assisted milling of silicon nitride ceramics. Int J Adv Manuf Technol 52(1–4):143–154Google Scholar
- 27.Hedberg GK (2013) Laser assisted milling of difficult to machine materials. Purdue UniversityGoogle Scholar
- 29.Pan Z et al (2017) Turning force prediction of AISI 4130 considering dynamic recrystallization. 2017(50725):V001T02A040Google Scholar