PARE, Vikas ;AGNIHOTRI, Geeta ;KRISHNA, Chimata . Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques – a Comparative Study. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 61, n.3, p. 176-186, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/selection-of-optimum-process-parameters-in-high-speed-cnc-end-milling-of-composite-materials-using-meta-heuristic-techniques-a-comparative-study/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2014.1914.
Pare, V., Agnihotri, G., & Krishna, C. (2015). Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques – a Comparative Study. Strojniški vestnik - Journal of Mechanical Engineering, 61(3), 176-186. doi:http://dx.doi.org/10.5545/sv-jme.2014.1914
@article{sv-jmesv-jme.2014.1914,
author = {Vikas Pare and Geeta Agnihotri and Chimata Krishna},
title = {Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques – a Comparative Study},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {61},
number = {3},
year = {2015},
keywords = {High speed machining, metal matrix composite, surface roughness, GSA, TLBO.},
abstract = {The machining of aluminium metal matrix composites in CNC high speed conditions is significant because such composites have diverse applications in the aeronautics industry. Because that industry requires high quality outcomes, the prediction of surface roughness, which depends on input process parameters, assumes significance in the maintaining quality of products. Even though many researchers have worked in the area of conventional machining, very few of them have explored optimization techniques, such as teaching-learning-based optimization (TLBO) and gravitational search algorithms (GSA) in high speed environments. In this research, an attempt is made to determine the optimum machining conditions for the end-milling of composite materials using GSA. Input process parameters, such as cutting speed, feed, the depth of cut and the step-over ratio are taken as independent variables, and surface roughness is taken as dependent variable. Experiments were conducted on Al2O3 + SiC metal matrix composite by considering selected variations in the input process parameters. Surface roughness is measured in each of cases, and the required data is obtained for further analysis. An empirical relationship is established between dependent and independent variables in the form of linear and non-linear regression equations, and the results are analysed. The results showed that GSA gives better results for surface roughness when compared to the genetic algorithm, simulated annealing and TLBO methods. An additional set of experiments was conducted to validate the results obtained.},
issn = {0039-2480}, pages = {176-186}, doi = {10.5545/sv-jme.2014.1914},
url = {https://www.sv-jme.eu/article/selection-of-optimum-process-parameters-in-high-speed-cnc-end-milling-of-composite-materials-using-meta-heuristic-techniques-a-comparative-study/}
}
Pare, V.,Agnihotri, G.,Krishna, C. 2015 June 61. Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques – a Comparative Study. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 61:3
%A Pare, Vikas %A Agnihotri, Geeta %A Krishna, Chimata %D 2015 %T Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques – a Comparative Study %B 2015 %9 High speed machining, metal matrix composite, surface roughness, GSA, TLBO. %! Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques – a Comparative Study %K High speed machining, metal matrix composite, surface roughness, GSA, TLBO. %X The machining of aluminium metal matrix composites in CNC high speed conditions is significant because such composites have diverse applications in the aeronautics industry. Because that industry requires high quality outcomes, the prediction of surface roughness, which depends on input process parameters, assumes significance in the maintaining quality of products. Even though many researchers have worked in the area of conventional machining, very few of them have explored optimization techniques, such as teaching-learning-based optimization (TLBO) and gravitational search algorithms (GSA) in high speed environments. In this research, an attempt is made to determine the optimum machining conditions for the end-milling of composite materials using GSA. Input process parameters, such as cutting speed, feed, the depth of cut and the step-over ratio are taken as independent variables, and surface roughness is taken as dependent variable. Experiments were conducted on Al2O3 + SiC metal matrix composite by considering selected variations in the input process parameters. Surface roughness is measured in each of cases, and the required data is obtained for further analysis. An empirical relationship is established between dependent and independent variables in the form of linear and non-linear regression equations, and the results are analysed. The results showed that GSA gives better results for surface roughness when compared to the genetic algorithm, simulated annealing and TLBO methods. An additional set of experiments was conducted to validate the results obtained. %U https://www.sv-jme.eu/article/selection-of-optimum-process-parameters-in-high-speed-cnc-end-milling-of-composite-materials-using-meta-heuristic-techniques-a-comparative-study/ %0 Journal Article %R 10.5545/sv-jme.2014.1914 %& 176 %P 11 %J Strojniški vestnik - Journal of Mechanical Engineering %V 61 %N 3 %@ 0039-2480 %8 2018-06-27 %7 2018-06-27
Pare, Vikas, Geeta Agnihotri, & Chimata Krishna. "Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques – a Comparative Study." Strojniški vestnik - Journal of Mechanical Engineering [Online], 61.3 (2015): 176-186. Web. 20 Dec. 2024
TY - JOUR AU - Pare, Vikas AU - Agnihotri, Geeta AU - Krishna, Chimata PY - 2015 TI - Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques – a Comparative Study JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2014.1914 KW - High speed machining, metal matrix composite, surface roughness, GSA, TLBO. N2 - The machining of aluminium metal matrix composites in CNC high speed conditions is significant because such composites have diverse applications in the aeronautics industry. Because that industry requires high quality outcomes, the prediction of surface roughness, which depends on input process parameters, assumes significance in the maintaining quality of products. Even though many researchers have worked in the area of conventional machining, very few of them have explored optimization techniques, such as teaching-learning-based optimization (TLBO) and gravitational search algorithms (GSA) in high speed environments. In this research, an attempt is made to determine the optimum machining conditions for the end-milling of composite materials using GSA. Input process parameters, such as cutting speed, feed, the depth of cut and the step-over ratio are taken as independent variables, and surface roughness is taken as dependent variable. Experiments were conducted on Al2O3 + SiC metal matrix composite by considering selected variations in the input process parameters. Surface roughness is measured in each of cases, and the required data is obtained for further analysis. An empirical relationship is established between dependent and independent variables in the form of linear and non-linear regression equations, and the results are analysed. The results showed that GSA gives better results for surface roughness when compared to the genetic algorithm, simulated annealing and TLBO methods. An additional set of experiments was conducted to validate the results obtained. UR - https://www.sv-jme.eu/article/selection-of-optimum-process-parameters-in-high-speed-cnc-end-milling-of-composite-materials-using-meta-heuristic-techniques-a-comparative-study/
@article{{sv-jme}{sv-jme.2014.1914},
author = {Pare, V., Agnihotri, G., Krishna, C.},
title = {Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques – a Comparative Study},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {61},
number = {3},
year = {2015},
doi = {10.5545/sv-jme.2014.1914},
url = {https://www.sv-jme.eu/article/selection-of-optimum-process-parameters-in-high-speed-cnc-end-milling-of-composite-materials-using-meta-heuristic-techniques-a-comparative-study/}
}
TY - JOUR AU - Pare, Vikas AU - Agnihotri, Geeta AU - Krishna, Chimata PY - 2018/06/27 TI - Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques – a Comparative Study JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 61, No 3 (2015): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2014.1914 KW - High speed machining, metal matrix composite, surface roughness, GSA, TLBO. N2 - The machining of aluminium metal matrix composites in CNC high speed conditions is significant because such composites have diverse applications in the aeronautics industry. Because that industry requires high quality outcomes, the prediction of surface roughness, which depends on input process parameters, assumes significance in the maintaining quality of products. Even though many researchers have worked in the area of conventional machining, very few of them have explored optimization techniques, such as teaching-learning-based optimization (TLBO) and gravitational search algorithms (GSA) in high speed environments. In this research, an attempt is made to determine the optimum machining conditions for the end-milling of composite materials using GSA. Input process parameters, such as cutting speed, feed, the depth of cut and the step-over ratio are taken as independent variables, and surface roughness is taken as dependent variable. Experiments were conducted on Al2O3 + SiC metal matrix composite by considering selected variations in the input process parameters. Surface roughness is measured in each of cases, and the required data is obtained for further analysis. An empirical relationship is established between dependent and independent variables in the form of linear and non-linear regression equations, and the results are analysed. The results showed that GSA gives better results for surface roughness when compared to the genetic algorithm, simulated annealing and TLBO methods. An additional set of experiments was conducted to validate the results obtained. UR - https://www.sv-jme.eu/article/selection-of-optimum-process-parameters-in-high-speed-cnc-end-milling-of-composite-materials-using-meta-heuristic-techniques-a-comparative-study/
Pare, Vikas, Agnihotri, Geeta, AND Krishna, Chimata. "Selection of Optimum Process Parameters in High Speed CNC End-Milling of Composite Materials Using Meta Heuristic Techniques – a Comparative Study" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 61 Number 3 (27 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 61(2015)3, 176-186
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
The machining of aluminium metal matrix composites in CNC high speed conditions is significant because such composites have diverse applications in the aeronautics industry. Because that industry requires high quality outcomes, the prediction of surface roughness, which depends on input process parameters, assumes significance in the maintaining quality of products. Even though many researchers have worked in the area of conventional machining, very few of them have explored optimization techniques, such as teaching-learning-based optimization (TLBO) and gravitational search algorithms (GSA) in high speed environments. In this research, an attempt is made to determine the optimum machining conditions for the end-milling of composite materials using GSA. Input process parameters, such as cutting speed, feed, the depth of cut and the step-over ratio are taken as independent variables, and surface roughness is taken as dependent variable. Experiments were conducted on Al2O3 + SiC metal matrix composite by considering selected variations in the input process parameters. Surface roughness is measured in each of cases, and the required data is obtained for further analysis. An empirical relationship is established between dependent and independent variables in the form of linear and non-linear regression equations, and the results are analysed. The results showed that GSA gives better results for surface roughness when compared to the genetic algorithm, simulated annealing and TLBO methods. An additional set of experiments was conducted to validate the results obtained.