Numerical Modelling and Optimization of Dry Orthogonal Turning of Al6061 T6 Alloy

Authors

  • Chathakudath Sukumaran Sumesh
    Affiliation

    Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India

  • Ajith Ramesh
    Affiliation

    Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India

https://doi.org/10.3311/PPme.11347

Abstract

In this paper, the influence of machining parameters, Cutting Speed, Feed Rate, and Depth of cut, on surface finish during dry orthogonal turning of Al 6061 – T6 alloy, is studied using the response surface methodology (RSM). This paper proposes a unique way to predict the surface finish in turning, using the effective plastic strain (PEEQ) values obtained from the simulations. A comprehensive finite element model was proposed to predict the surface finish accurately, by correlating the variance of the PEEQ. The Johnson-Cook damage model is used to define the damage criteria and Johnson-Cook material model is used to explain the material constitutive behavior. A dynamic, explicit method is used along with the Adaptive Lagrangian-Eulerian (ALE) method to predict material flow accurately. The influence of machining parameters was studied by assuming Central Composite Design (CCD). The output response, PEEQ, was fitted into analytical quadratic polynomial models using regression analysis, which shows that feed rate was the most dominant factor for PEEQ than the other parameters considered in this study. Using the individual desirability function method, the objective, optimal setting of the machining parameters was obtained for better surface finish.

Keywords:

orthogonal turning, RSM, PEEQ, JC model, ALE, CCD

Citation data from Crossref and Scopus

Published Online

2018-04-06

How to Cite

Sumesh, C. S., Ramesh, A. “Numerical Modelling and Optimization of Dry Orthogonal Turning of Al6061 T6 Alloy”, Periodica Polytechnica Mechanical Engineering, 62(3), pp. 196–202, 2018. https://doi.org/10.3311/PPme.11347

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Articles