Prediction of residual stress using RSM during turning of Ti–6Al–4V with the 3D FEM assist and experiments

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Abstract

Machining of titanium alloy is known to be complex due to problems like low thermal conductivity, high thermal and residual stresses, chemical reactions, phase transformation during process, etc. These complexities affect the quality of surface produced and hence the performance of machined components. In the evaluation of surface integrity of machined components, residual stresses are the most critical response as it affects fatigue life of component. Present work aimed at developing prediction model for the residual stress induced due to machining of Ti–6Al–4V. 3D finite element simulations are performed to determine residual stress in turning of Ti–6Al–4V using carbide inserts. Simulations are performed in sequential manner using design of experiments through response surface methodology and an empirical model is developed. Later on actual machining is performed for few of the experimental runs and residual stresses are measured through X-ray diffraction method and the simulation results are compared with experimental results. These results are found in good agreement with average absolute error of 11%. The influence of the feed rate, cutting speed and depth of cut variations on the induced residual stress is investigated and analysed using analysis of variance. Results show that magnitude of surface compressive residual stress increases with cutting speed and depth of cut whereas it reduces with feed rate. Additionally, optimization was done with response surface desirability approach for optimum cutting parameters to maximize surface compressive residual stress. Optimum cutting parameters were found as cutting speed 171.4 m/min (1704.5 RPM), feed rate 0.07 mm/rev and 0.8 mm depth of cut (doc) with corresponding residual stress as − 1495.97 MPa.

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Sahu, N. K., & Andhare, A. B. (2019). Prediction of residual stress using RSM during turning of Ti–6Al–4V with the 3D FEM assist and experiments. SN Applied Sciences, 1(8). https://doi.org/10.1007/s42452-019-0809-5

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