Modeling and Optimization of Hard Turning Operation on 41Cr4 Alloy Steel Using Response Surface Methodology

Christopher Okechukwu Izelu; Samuel Chikezie Eze; Festus Ifeanyi Ashiedu

doi:doi:10.11648/j.ijmea.20160402.18

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Modeling and Optimization of Hard Turning Operation on 41Cr4 Alloy Steel Using Response Surface Methodology

Christopher Okechukwu Izelu, Samuel Chikezie Eze, Festus Ifeanyi Ashiedu

Published in International Journal of Mechanical Engineering and Applications (Volume 4, Issue 2)

Received: 3 May 2016 Accepted: 14 May 2016 Published: 30 May 2016

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Abstract

Product quality, productivity and organizational goodwill are often the major concern of every production or manufacturing unit. These criteria, more especially product quality, cannot readily and effectively be met through dependence on the skills of an operator. Hence, the need for optimization in order to identify the best process condition, derived from parametric combinations of process variables, for the manufacturing process. The work presented concerns an aspect of a series of hard turning experiments on 41Cr4 alloy structural steel conducted to model, predict and optimize the machining induced vibration, and the surface roughness as functions of the cutting speed, feed rate, and the tool nose radius. The response surface methodology, based on the central composite design of experiment is employed in the study, and analysis of the generated data performed with the aid of Design expert 9 software. A quadratic regression model was suggested as best fits for both the machining induced vibration and surface roughness data. These were confirmed by analyses of variance, which also revealed the tool nose radius and cutting speed, as well as the feed rate and cutting speed to be important factors that determine changes in the machining induced vibration and surface roughness, respectively. The optimum setting of the tool nose radius at 1.72301 mm, feed rate at 0.15 mm/rev, and the cutting speed at 311.075 rev/min minimized the machining induced vibration to a value of 0.08 mm/min² and the surface roughness to a value of 4.74 µmm.

Published in	International Journal of Mechanical Engineering and Applications (Volume 4, Issue 2)
DOI	10.11648/j.ijmea.20160402.18
Page(s)	88-102
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Tool Nose Radius, Feed Rate, Cutting Speed, Machining Induced Vibration, Surface Roughness, Turning Experiment, Response Surface Methodology

References

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APA Style

Christopher Okechukwu Izelu, Samuel Chikezie Eze, Festus Ifeanyi Ashiedu. (2016). Modeling and Optimization of Hard Turning Operation on 41Cr4 Alloy Steel Using Response Surface Methodology. International Journal of Mechanical Engineering and Applications, 4(2), 88-102. https://doi.org/10.11648/j.ijmea.20160402.18

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ACS Style

Christopher Okechukwu Izelu; Samuel Chikezie Eze; Festus Ifeanyi Ashiedu. Modeling and Optimization of Hard Turning Operation on 41Cr4 Alloy Steel Using Response Surface Methodology. Int. J. Mech. Eng. Appl. 2016, 4(2), 88-102. doi: 10.11648/j.ijmea.20160402.18

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AMA Style

Christopher Okechukwu Izelu, Samuel Chikezie Eze, Festus Ifeanyi Ashiedu. Modeling and Optimization of Hard Turning Operation on 41Cr4 Alloy Steel Using Response Surface Methodology. Int J Mech Eng Appl. 2016;4(2):88-102. doi: 10.11648/j.ijmea.20160402.18

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@article{10.11648/j.ijmea.20160402.18,
  author = {Christopher Okechukwu Izelu and Samuel Chikezie Eze and Festus Ifeanyi Ashiedu},
  title = {Modeling and Optimization of Hard Turning Operation on 41Cr4 Alloy Steel Using Response Surface Methodology},
  journal = {International Journal of Mechanical Engineering and Applications},
  volume = {4},
  number = {2},
  pages = {88-102},
  doi = {10.11648/j.ijmea.20160402.18},
  url = {https://doi.org/10.11648/j.ijmea.20160402.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20160402.18},
  abstract = {Product quality, productivity and organizational goodwill are often the major concern of every production or manufacturing unit. These criteria, more especially product quality, cannot readily and effectively be met through dependence on the skills of an operator. Hence, the need for optimization in order to identify the best process condition, derived from parametric combinations of process variables, for the manufacturing process. The work presented concerns an aspect of a series of hard turning experiments on 41Cr4 alloy structural steel conducted to model, predict and optimize the machining induced vibration, and the surface roughness as functions of the cutting speed, feed rate, and the tool nose radius. The response surface methodology, based on the central composite design of experiment is employed in the study, and analysis of the generated data performed with the aid of Design expert 9 software. A quadratic regression model was suggested as best fits for both the machining induced vibration and surface roughness data. These were confirmed by analyses of variance, which also revealed the tool nose radius and cutting speed, as well as the feed rate and cutting speed to be important factors that determine changes in the machining induced vibration and surface roughness, respectively. The optimum setting of the tool nose radius at 1.72301 mm, feed rate at 0.15 mm/rev, and the cutting speed at 311.075 rev/min minimized the machining induced vibration to a value of 0.08 mm/min2 and the surface roughness to a value of 4.74 µmm.},
 year = {2016}
}

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TY  - JOUR
T1  - Modeling and Optimization of Hard Turning Operation on 41Cr4 Alloy Steel Using Response Surface Methodology
AU  - Christopher Okechukwu Izelu
AU  - Samuel Chikezie Eze
AU  - Festus Ifeanyi Ashiedu
Y1  - 2016/05/30
PY  - 2016
N1  - https://doi.org/10.11648/j.ijmea.20160402.18
DO  - 10.11648/j.ijmea.20160402.18
T2  - International Journal of Mechanical Engineering and Applications
JF  - International Journal of Mechanical Engineering and Applications
JO  - International Journal of Mechanical Engineering and Applications
SP  - 88
EP  - 102
PB  - Science Publishing Group
SN  - 2330-0248
UR  - https://doi.org/10.11648/j.ijmea.20160402.18
AB  - Product quality, productivity and organizational goodwill are often the major concern of every production or manufacturing unit. These criteria, more especially product quality, cannot readily and effectively be met through dependence on the skills of an operator. Hence, the need for optimization in order to identify the best process condition, derived from parametric combinations of process variables, for the manufacturing process. The work presented concerns an aspect of a series of hard turning experiments on 41Cr4 alloy structural steel conducted to model, predict and optimize the machining induced vibration, and the surface roughness as functions of the cutting speed, feed rate, and the tool nose radius. The response surface methodology, based on the central composite design of experiment is employed in the study, and analysis of the generated data performed with the aid of Design expert 9 software. A quadratic regression model was suggested as best fits for both the machining induced vibration and surface roughness data. These were confirmed by analyses of variance, which also revealed the tool nose radius and cutting speed, as well as the feed rate and cutting speed to be important factors that determine changes in the machining induced vibration and surface roughness, respectively. The optimum setting of the tool nose radius at 1.72301 mm, feed rate at 0.15 mm/rev, and the cutting speed at 311.075 rev/min minimized the machining induced vibration to a value of 0.08 mm/min2 and the surface roughness to a value of 4.74 µmm.
VL  - 4
IS  - 2
ER  -

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Author Information

Christopher Okechukwu Izelu

Department of Mechanical Engineering, Federal University of Petroleum Resources, Effurun, Nigeria
Samuel Chikezie Eze

Samez Engineering and Consultancy Services Limited, Kaduna, Nigeria
Festus Ifeanyi Ashiedu

Department of Mechanical Engineering, Federal University of Petroleum Resources, Effurun, Nigeria

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APA Style

Christopher Okechukwu Izelu, Samuel Chikezie Eze, Festus Ifeanyi Ashiedu. (2016). Modeling and Optimization of Hard Turning Operation on 41Cr4 Alloy Steel Using Response Surface Methodology. International Journal of Mechanical Engineering and Applications, 4(2), 88-102. https://doi.org/10.11648/j.ijmea.20160402.18

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ACS Style

Christopher Okechukwu Izelu; Samuel Chikezie Eze; Festus Ifeanyi Ashiedu. Modeling and Optimization of Hard Turning Operation on 41Cr4 Alloy Steel Using Response Surface Methodology. Int. J. Mech. Eng. Appl. 2016, 4(2), 88-102. doi: 10.11648/j.ijmea.20160402.18

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AMA Style

Christopher Okechukwu Izelu, Samuel Chikezie Eze, Festus Ifeanyi Ashiedu. Modeling and Optimization of Hard Turning Operation on 41Cr4 Alloy Steel Using Response Surface Methodology. Int J Mech Eng Appl. 2016;4(2):88-102. doi: 10.11648/j.ijmea.20160402.18

Copy | Download

@article{10.11648/j.ijmea.20160402.18,
  author = {Christopher Okechukwu Izelu and Samuel Chikezie Eze and Festus Ifeanyi Ashiedu},
  title = {Modeling and Optimization of Hard Turning Operation on 41Cr4 Alloy Steel Using Response Surface Methodology},
  journal = {International Journal of Mechanical Engineering and Applications},
  volume = {4},
  number = {2},
  pages = {88-102},
  doi = {10.11648/j.ijmea.20160402.18},
  url = {https://doi.org/10.11648/j.ijmea.20160402.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijmea.20160402.18},
  abstract = {Product quality, productivity and organizational goodwill are often the major concern of every production or manufacturing unit. These criteria, more especially product quality, cannot readily and effectively be met through dependence on the skills of an operator. Hence, the need for optimization in order to identify the best process condition, derived from parametric combinations of process variables, for the manufacturing process. The work presented concerns an aspect of a series of hard turning experiments on 41Cr4 alloy structural steel conducted to model, predict and optimize the machining induced vibration, and the surface roughness as functions of the cutting speed, feed rate, and the tool nose radius. The response surface methodology, based on the central composite design of experiment is employed in the study, and analysis of the generated data performed with the aid of Design expert 9 software. A quadratic regression model was suggested as best fits for both the machining induced vibration and surface roughness data. These were confirmed by analyses of variance, which also revealed the tool nose radius and cutting speed, as well as the feed rate and cutting speed to be important factors that determine changes in the machining induced vibration and surface roughness, respectively. The optimum setting of the tool nose radius at 1.72301 mm, feed rate at 0.15 mm/rev, and the cutting speed at 311.075 rev/min minimized the machining induced vibration to a value of 0.08 mm/min2 and the surface roughness to a value of 4.74 µmm.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Modeling and Optimization of Hard Turning Operation on 41Cr4 Alloy Steel Using Response Surface Methodology
AU  - Christopher Okechukwu Izelu
AU  - Samuel Chikezie Eze
AU  - Festus Ifeanyi Ashiedu
Y1  - 2016/05/30
PY  - 2016
N1  - https://doi.org/10.11648/j.ijmea.20160402.18
DO  - 10.11648/j.ijmea.20160402.18
T2  - International Journal of Mechanical Engineering and Applications
JF  - International Journal of Mechanical Engineering and Applications
JO  - International Journal of Mechanical Engineering and Applications
SP  - 88
EP  - 102
PB  - Science Publishing Group
SN  - 2330-0248
UR  - https://doi.org/10.11648/j.ijmea.20160402.18
AB  - Product quality, productivity and organizational goodwill are often the major concern of every production or manufacturing unit. These criteria, more especially product quality, cannot readily and effectively be met through dependence on the skills of an operator. Hence, the need for optimization in order to identify the best process condition, derived from parametric combinations of process variables, for the manufacturing process. The work presented concerns an aspect of a series of hard turning experiments on 41Cr4 alloy structural steel conducted to model, predict and optimize the machining induced vibration, and the surface roughness as functions of the cutting speed, feed rate, and the tool nose radius. The response surface methodology, based on the central composite design of experiment is employed in the study, and analysis of the generated data performed with the aid of Design expert 9 software. A quadratic regression model was suggested as best fits for both the machining induced vibration and surface roughness data. These were confirmed by analyses of variance, which also revealed the tool nose radius and cutting speed, as well as the feed rate and cutting speed to be important factors that determine changes in the machining induced vibration and surface roughness, respectively. The optimum setting of the tool nose radius at 1.72301 mm, feed rate at 0.15 mm/rev, and the cutting speed at 311.075 rev/min minimized the machining induced vibration to a value of 0.08 mm/min2 and the surface roughness to a value of 4.74 µmm.
VL  - 4
IS  - 2
ER  -

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