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Received: 2019-05-15

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Bio-Design and Manufacturing  2019 Vol.2 No.3 P.153-160

http://doi.org/10.1007/s42242-019-00044-9


Constitutive modeling of ultra-fine-grained titanium flow stress for machining temperature prediction


Author(s):  Jinqiang Ning, Vinh Nguyen, Yong Huang, Karl T. Hartwig, Steven Y. Liang

Affiliation(s):  George W. Woodruf School of Mechanical Engineering, Georgia Institute of Technology, 801 Ferst Drive, Atlanta, GA 30332, USA; more

Corresponding email(s):   jinqiangning@gatech.edu

Key Words:  Ultra-fine-grained titanium, Analytical modeling, High computational efficiency, Johnson–Cook model , Cutting mechanics


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Jinqiang Ning, Vinh Nguyen, Yong Huang, Karl T. Hartwig, Steven Y. Liang . Constitutive modeling of ultra-fine-grained titanium flow stress for machining temperature prediction[J]. Journal of Zhejiang University Science D, 2019, 2(3): 153-160.

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Abstract: 
This work investigates the machining temperatures of ultra-fine-grained titanium (UFG Ti), prepared by equal channel angular extrusion, through analytical modeling. UFG Ti has great usefulness in biomedical applications because of its high mechanical strength, sufficient manufacturability, and high biocompatibility. The temperatures were predicted using a physics-based predictive model based on material constitutive relation and mechanics of the orthogonal cutting process. The minimization between the stress calculated using Johnson–Cook constitutive model and the same stress calculated using mechanics model yields the estimation of machining temperatures at two deformation zones. Good agreements are observed upon validation to the values reported in the literature. The machinability of UFG Ti is investigated by comparing its machining temperature to that of Ti–6Al–4V alloy under the same cutting conditions. Significantly lower temperatures are observed in machining UFG Ti. The computational efficiency of the presented model is investigated by comparing its average computational time (~ 0.5 s) to that of a widely used modified chip formation model (8900 s) with comparable prediction accuracy. This work extends the applicability of the presented temperature model to a broader class of materials, specifically ultra-fine-grained metals. The high computational efficiency allows the in situ temperature prediction and optimization of temperature condition with process parameters planning.

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