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Identification of Most Affected Parameter for Design for Remanufacturing of Scrap Piston by Taguchi Desirability Function Analysis

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Man-Machine Interactions 5 (ICMMI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 659))

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Abstract

For sustainable production, we consider remanufacturing as an important part because of its effectiveness in making products very similar to new or better than the original one. The Taguchi method is not only used to improve industrial product quality by optimizing an arbitrary objective function, but also used to minimize the sensitivity of engineering designs towards ungovernable factors or noise. Taguchi transforms objective function readings to signal-to-noise (S/N) ratio for the readings of performance characteristics levels of control factors. We used Taguchi optimization technique to figure out the most effective parameter of designing to reuse the piston after 15 years for remanufacturing and also used to figure out the best piston for remanufacturing. For this purpose, we used dimensional parameters such as outer diameter, height, pinhole diameter, grooves and defects as input parameters for six 15 years old pistons and calculated output parameters like total deformation, shear stress, heat flux, and temperature, von Miser using Taguchi optimization and then ranked them accordingly. From the results we see that groove thickness and pin hole diameter do not have any significant effect on the output parameters when compared to height and outer diameter after the life time of the piston. Also as per the analysis, the piston ranking top having significantly same dimensions compare with new piston.

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Authors and Affiliations

  1. MPSTME, SVKM’S NMIMS, Dhule, Maharashtra, India

    M. Ramachandran & Nidhi Agarwal

Authors
  1. M. Ramachandran
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  2. Nidhi Agarwal
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Corresponding author

Correspondence to M. Ramachandran .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Aleksandra Gruca

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Tadeusz Czachórski

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Katarzyna Harezlak

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Agnieszka Piotrowska

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Ramachandran, M., Agarwal, N. (2018). Identification of Most Affected Parameter for Design for Remanufacturing of Scrap Piston by Taguchi Desirability Function Analysis. In: Gruca, A., Czachórski, T., Harezlak, K., Kozielski, S., Piotrowska, A. (eds) Man-Machine Interactions 5. ICMMI 2017. Advances in Intelligent Systems and Computing, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-319-67792-7_32

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  • DOI: https://doi.org/10.1007/978-3-319-67792-7_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67791-0

  • Online ISBN: 978-3-319-67792-7

  • eBook Packages: EngineeringEngineering (R0)

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