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Modeling of geometrical and machining parameters on temperature rise while machining Al 6351 using response surface methodology and genetic algorithm

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Abstract

This paper focused on the effect of geometrical and machining parameters such as rake angle, nose radius, cutting speed, feed rate, and depth of cut on temperature rise during end milling operation. The experiments were conducted on Al 6351 with high-speed steel end mill cutter. Central composite rotatable experimental design methodology was employed to conduct the experiments. The temperature rise during machining was measured using K-type thermocouple coupled with the thermal indicator. A prediction model was developed using response surface methodology and the adequacy of the model was verified using analysis of variance. The predictive model was used to analyze direct and interaction effect of the machining parameters on temperature rise, which helps to select proper combination of machining parameters to obtain better quality in machining. Genetic algorithm was applied to optimize the machining process parameters to obtain minimum rise in temperature. A program was developed using C language to do the optimization and the best optimal combination of machining parameters gave a value of 0.0105 °C.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Rajalakshmi Engineering College, Chennai, 602105, Tamil Nadu, India

    M. Santhanakrishnan

  2. School of Mechanical Engineering, SASTRA University, Thanjavur, 613 401, Tamil Nadu, India

    P. S. Sivasakthivel

  3. Department of Mechanical Engineering, SNS College of Engineering, Coimbatore, 641107, Tamil Nadu, India

    R. Sudhakaran

Authors
  1. M. Santhanakrishnan
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  2. P. S. Sivasakthivel
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  3. R. Sudhakaran
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Corresponding author

Correspondence to P. S. Sivasakthivel.

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Technical Editor: Alexandre Mendes Abrao.

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Santhanakrishnan, M., Sivasakthivel, P.S. & Sudhakaran, R. Modeling of geometrical and machining parameters on temperature rise while machining Al 6351 using response surface methodology and genetic algorithm. J Braz. Soc. Mech. Sci. Eng. 39, 487–496 (2017). https://doi.org/10.1007/s40430-015-0378-5

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  • DOI: https://doi.org/10.1007/s40430-015-0378-5

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