Cutting temperature prediction modeland parametric optimization in turning aluminium matrix composite

Kumar Shantanu Prasad; Diptikanta  Das; Harsh Vardhan Khatri; Chandrika  Samal; Rajesh Kumar Mandal

doi:10.1063/5.0024242

Cutting temperature prediction modeland parametric optimization in turning aluminium matrix composite

Kumar Shantanu Prasad, Diptikanta Das, Harsh Vardhan Khatri, Chandrika Samal, Rajesh Kumar Mandal

School of Physics, Engineering & Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Silicon carbide particles (SiCp) were impregnated in Al 7075 alloy through mechanical stirring assisted liquid state processing method to produce metal matrix composites (MMCs), followed by thermal treatment as per T6 condition. The composite was machined in dry condition using multilayer TiN coated WC inserts to investigate the influence of machining parameters on cutting tool temperature. Results revealed an increase of the tool temperature with escalation of cutting velocity, feed and depth of cut. Response surface quadratic model was generated to predict the cutting temperature at different levels of cutting parameters. The cutting temperature was then optimized using Taguchi approach and the optimized parameters were validated through some confirmation experiments.

Original language	English
Title of host publication	AIP Conference Proceedings 2273: 2nd International Conference on Mechanical Materials and Renewable Energy (ICMMRE 2019), 050001 (2020)
Place of Publication	India
Publisher	American Institute of Physics (AIP)
Volume	2273
Edition	1
ISBN (Electronic)	9780735440036
DOIs	https://doi.org/10.1063/5.0024242
Publication status	Published - 2 Nov 2020

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10.1063/5.0024242

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Prasad, K. S., Das, D., Khatri, H. V., Samal, C., & Mandal, R. K. (2020). Cutting temperature prediction modeland parametric optimization in turning aluminium matrix composite. In AIP Conference Proceedings 2273: 2nd International Conference on Mechanical Materials and Renewable Energy (ICMMRE 2019), 050001 (2020) (1 ed., Vol. 2273). American Institute of Physics (AIP). https://doi.org/10.1063/5.0024242

Prasad, Kumar Shantanu ; Das, Diptikanta ; Khatri, Harsh Vardhan et al. / Cutting temperature prediction modeland parametric optimization in turning aluminium matrix composite. AIP Conference Proceedings 2273: 2nd International Conference on Mechanical Materials and Renewable Energy (ICMMRE 2019), 050001 (2020). Vol. 2273 1. ed. India : American Institute of Physics (AIP), 2020.

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title = "Cutting temperature prediction modeland parametric optimization in turning aluminium matrix composite",

abstract = "Silicon carbide particles (SiCp) were impregnated in Al 7075 alloy through mechanical stirring assisted liquid state processing method to produce metal matrix composites (MMCs), followed by thermal treatment as per T6 condition. The composite was machined in dry condition using multilayer TiN coated WC inserts to investigate the influence of machining parameters on cutting tool temperature. Results revealed an increase of the tool temperature with escalation of cutting velocity, feed and depth of cut. Response surface quadratic model was generated to predict the cutting temperature at different levels of cutting parameters. The cutting temperature was then optimized using Taguchi approach and the optimized parameters were validated through some confirmation experiments. ",

author = "Prasad, {Kumar Shantanu} and Diptikanta Das and Khatri, {Harsh Vardhan} and Chandrika Samal and Mandal, {Rajesh Kumar}",

year = "2020",

month = nov,

day = "2",

doi = "10.1063/5.0024242",

language = "English",

volume = "2273",

booktitle = "AIP Conference Proceedings 2273: 2nd International Conference on Mechanical Materials and Renewable Energy (ICMMRE 2019), 050001 (2020)",

publisher = "American Institute of Physics (AIP)",

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edition = "1",

}

Prasad, KS, Das, D, Khatri, HV, Samal, C & Mandal, RK 2020, Cutting temperature prediction modeland parametric optimization in turning aluminium matrix composite. in AIP Conference Proceedings 2273: 2nd International Conference on Mechanical Materials and Renewable Energy (ICMMRE 2019), 050001 (2020). 1 edn, vol. 2273, American Institute of Physics (AIP), India. https://doi.org/10.1063/5.0024242

Cutting temperature prediction modeland parametric optimization in turning aluminium matrix composite. / Prasad, Kumar Shantanu; Das, Diptikanta ; Khatri, Harsh Vardhan et al.
AIP Conference Proceedings 2273: 2nd International Conference on Mechanical Materials and Renewable Energy (ICMMRE 2019), 050001 (2020). Vol. 2273 1. ed. India: American Institute of Physics (AIP), 2020.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Cutting temperature prediction modeland parametric optimization in turning aluminium matrix composite

AU - Prasad, Kumar Shantanu

AU - Das, Diptikanta

AU - Khatri, Harsh Vardhan

AU - Samal, Chandrika

AU - Mandal, Rajesh Kumar

PY - 2020/11/2

Y1 - 2020/11/2

N2 - Silicon carbide particles (SiCp) were impregnated in Al 7075 alloy through mechanical stirring assisted liquid state processing method to produce metal matrix composites (MMCs), followed by thermal treatment as per T6 condition. The composite was machined in dry condition using multilayer TiN coated WC inserts to investigate the influence of machining parameters on cutting tool temperature. Results revealed an increase of the tool temperature with escalation of cutting velocity, feed and depth of cut. Response surface quadratic model was generated to predict the cutting temperature at different levels of cutting parameters. The cutting temperature was then optimized using Taguchi approach and the optimized parameters were validated through some confirmation experiments.

AB - Silicon carbide particles (SiCp) were impregnated in Al 7075 alloy through mechanical stirring assisted liquid state processing method to produce metal matrix composites (MMCs), followed by thermal treatment as per T6 condition. The composite was machined in dry condition using multilayer TiN coated WC inserts to investigate the influence of machining parameters on cutting tool temperature. Results revealed an increase of the tool temperature with escalation of cutting velocity, feed and depth of cut. Response surface quadratic model was generated to predict the cutting temperature at different levels of cutting parameters. The cutting temperature was then optimized using Taguchi approach and the optimized parameters were validated through some confirmation experiments.

U2 - 10.1063/5.0024242

DO - 10.1063/5.0024242

M3 - Conference contribution

VL - 2273

BT - AIP Conference Proceedings 2273: 2nd International Conference on Mechanical Materials and Renewable Energy (ICMMRE 2019), 050001 (2020)

PB - American Institute of Physics (AIP)

CY - India

ER -

Prasad KS, Das D, Khatri HV, Samal C, Mandal RK. Cutting temperature prediction modeland parametric optimization in turning aluminium matrix composite. In AIP Conference Proceedings 2273: 2nd International Conference on Mechanical Materials and Renewable Energy (ICMMRE 2019), 050001 (2020). 1 ed. Vol. 2273. India: American Institute of Physics (AIP). 2020 doi: 10.1063/5.0024242

Cutting temperature prediction modeland parametric optimization in turning aluminium matrix composite

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