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), 2024. Published by Science Publishing Group

Keywords

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

References

[1]	Aggarwal, A. and Singh, H., Optimization of Machining Techniques – A retrospective and literature review, Sadhana, Vol. 30, Part 6, 2005, 699–711.
[2]	Kumar, N. and Uppal, N., A Review on Various Optimization Techniques used in Turning Operation for Improving Surface Roughness, Mechanica Confab, Vol. 2, No. 4 (2013) 45-51.
[3]	Ozcakar, N. and Kasapogu, O. A., Modeling of Surface Roughness in Machining, Yontim, Yil 20, Saya 64, 2009, 27–40.
[4]	Abhang, L. B. and Hameedullah, M., Power Prediction Model for Turning EN-31 Steel Using Response Surface Methodology, Journal of Engineering Science and Technology Review, Vol. 3, No. 1, 2010, 116–122.
[5]	Sahoo, P., Optimization of Turning Parameters for Surface Roughness using RSM and GA, Advances in Production Engineering and Management, Vol. 6, No. 3, 2011, 197–208.
[6]	Abhang, L. B. and Hameedullah, M., Optimization of Power Consumption by Desirability Function Approach, International journal on Resent Trends in Engineering and Technology. Vol. 6, No. 2, 2011, 287–290.
[7]	Sastry, M. N. and Devi, K. D., Optimization of Performance Measures in CNC Turning using Design of experiment (RSM), Science Insight: An International Journal, Vol.1, No. 1, 2011, 1–5.
[8]	Srinivasan, A., Arunachalam, R. M., Ramesh, S. and Senthilkumaar, J. S., Machining Performance Study on Metal matrix Composites – A Response Surface Methodology Approach, American Journal of Applied Science, Vol. 9, No. 4, 2012, 478–483.
[9]	Ramudu, C. and Sastry, M. N., Analysis and Optimization of Turning Process Parameters using Design of Experiment, International journal of Engineering Research and Applications, Vol. 2, issue 6, 2012, 020-027.
[10]	Aruna, M. and Dhanalaksmi, V., Design Optimization of Cutting Parameters when Turning Inconel 718 with Cermet Inserts, International Journal of Mechanical and Aerospace Engineering, vol. 6, 2012, 187–190.
[11]	Chomamutr, K. and Jongprasithporn, S., Optimization Parameters of Tool Life Model using the Taguchi Approach and Response Methodology, International Journal of Computer Science Issues, Vol. 9, Issue 1, No. 3, 2012, 120–125.
[12]	Abhang, L. B. and Hameedullah, M., Optimal Machining Parameters for Achieving the Desired Surface Roughness in Turning of Steel, Technical Journal of Engineering Research (TJER), Vol. 9, No. 1, 2013, 37–45.
[13]	Manu, R., Akbar, B. S., and Sharmas, V. S., Predictive Machinability Model of Hardened Steel Material in Turning Operation by Response Surface Regression Method, International Journal of Applications or Innovation in Engineering and Management, Vol. 2, Issue 10, 2013, 330–334.
[14]	Makadia, A. J. and Nanavati, J. I., Optimization of Machining Parameters for Turning Operations Based on Response Surface Methodology, Measurement, Elsevier, Vol. 46, 2013, 1521–1529.
[15]	Kannan, A., Esakkiraja, K. and Mataraj, M., Modeling and Analysis for Cutting Temperature in Turning of Aluminium 6063 using Response Surface Methodology, Journal of Mechanical and Civil Engineering, Vol. 9, Issue 4, 2013, 59–64.
[16]	Phate, M. and Tatwawadi, V. H., Formulation of a Field Data Based Model for a surface Roughness using Response Surface Method, International Journal of Science, Engineering and Technology Research, Vol. 2, Issue 4, 2013, 793–798.
[17]	Bhulyan, T. H. and Ahmed, I., Optimization of Cutting Parameters in Turning Process, Journal of Production Engineering, Vol. 16, No. 2, 2013, 11–19.
[18]	Manohar, M., Joseph, J., Selvaraj, T. and Sivakumar, D., Application of Box Behnken Design to Optimize the Parameters for Turning Inconel 718 using Carbide Tools, International Journal of scientific and Engineering Research, Vol. 4, Issue 4, 2013, 620–642.
[19]	Thiyagu, M., Karunamoorthy, L. and Arunkumar, N., Experimental Studies in machining Duplex Stainless Steel using Response Surface Methodology, International Journal of Mechanical Engineering, Vol. 14, No. 3, 2014, 48–61.
[20]	Saini, P. and Parkash, S., A Multi Response Optimization of Machining Parameters for Surface Roughness and MRR in High Speed CNC Turning of EN-24 Alloy Steel using Response Surface Methodology, International Journal of Engineering Science and Research Technology, Vol. 3, Issue 9, 2014, 333–345.
[21]	Saini, P., Parkash, S. and Choudhary, D., Experimental Investigation of Machining Parameters for Surface Roughness in High Speed CNC Turning of EN-24 Alloy Steel using Response Surface Methodology, International Journal of Engineering Research and Applications, Vol. 4, Issue 5, 2014, 153–160.
[22]	Soni, V., Mondal, S. and Singh, B., Process Parameters Optimization in Turning of Aluminum using a New Hybrid Approach, International Journal of Innovative Science, Engineering, and Technology, Vol. 1, Issue 3, 2014, 418–423.
[23]	Shunmugesh, K., Panneerselvam, K. and Amal, G., Optimization of Turning Parameters with Carbide Tool for Surface Roughness Analysis using Response Surface Methodology, International journal of research in Aeronautical and Mechanical Engineering, Vol. 2, Issue 6, 2014, 17–27.
[24]	Kumar, M. S., A Detailed Comparison among Dry, Wet and Gas Cooled Machining of Super Duplex Stainless Steel, Global Journal of Researches in Engineering: A Mechanical and Mechanics Engineering, Vol. 14, Issue 7, 2014, 17–25.
[25]	Sastry, M. N., Devi, K. D. and Reddy, K. M., Analysis and Optimization of machining Parameters using Design of Experiments, Industrial Engineering Letters, Vol. 2, No. 9,2012, 23–32.
[26]	Revankar, G. D., Shetty, R., Rao, S. S., and Gaitonde, V. N., Response Surface Model for Surface Roughness during Finish Turning of Titanium Alloy under Minimum Quantity Lubrication, International Conference on Emerging Trends in Engineering and Technology, Dec. 7 – 8, 2013, 78–84.
[27]	Mahajan, C. K., Mote, M. L., Patil, B. V. and Patil, H. G., Formulation and Simulation of a Field Data Based Model for the Turning process by using Response Surface Method, International Journal of Advanced Scientific and Technical Research, Vol. 2, Issue 3, 2013, 355–370.
[28]	Shihab, S. K., Khan, Z. A., Mohammad, A. and Siddiquee, A. N., Optimization of Surface Integrity in Dry Hard Turning using RSM, Sadhand, Vol. 39, Part 5, 2014, 1035–1053.
[29]	Gupta, U. and Kohi, A., Experimental Investigation of Surface Roughness in Dry Turning of AISI 4340 Alloy Steel using PVD- and CVD-Coated Carbide Inserts, International Journal of Innovations in Engineering and Technology, Vol. 4, Issue 1, 2014, 94 – 103
[30]	Khan, M. A., Kittur, J. K. and Kohir, V. D., Study and Analysis of Effect of Cutting Parameters on Cutting Forces and Surface Roughness, Advanced Engineering and Applied Sciences,: An International Journal, vol. 5, No. 3, 2015, 63 – 73
[31]	Devkumar, V., Sreedhar, E. and Prabakaran, M. P., Optimization of Machining Parameters on AL 6061 Alloy using Response Surface methodology, International Journal of Applied Research, Vol. 1, No. 7, 2015, 01–04.
[32]	Devi, K. D., Babu, K. S. and Reddy, K. H., Mathematical Modeling and Optimization of Turning process Parameters using Response Surface Methodology, International Journal of Applied Science and Engineering, Vol. 13, No. 1, 2015, 55–68.
[33]	Rajpoot, B. S., Moond, D. R. and Shrivastava, S., Investigating the effect of Cutting Parameters on the Average Surface Roughness and materials Removal Rate during Turning of Metal Matrix Composite using Response Surface Methodology, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 3, Issue 1, 2015, 241–247.
[34]	Khidhir, B. A., A-Oqaiel, W. and Kareem, P. M., Prediction Models by Response Surface Methodology for Turning Operation, American Journal of Modeling and Optimization, Vol. 3, No. 1, 2015, 1–6.
[35]	Agrawal, S., Guar, M. K., Kasdekar, D. K., Agrawal, S. and Malvi, C. S., Optimal Machining Condition for Turning of Hard Porcelain using Response Surface Methodology, European Journal of Advances in Engineering and Technology, Vol. 2, No. 5, 2015, 44–51.
[36]	Ranganath, M. S., Vipin, Kumar, N., and Kumar, R., Experimental Analysis of Surface Roughness in CNC Turning of Aluminum using Response Surface Methodology, International Journal of Advanced Research and Innovation, Vol. 3, Issue 1, 2015, 45–49.
[37]	Chandra, B. S. and Prasad, M. V. R. D., Parameter Optimization while Dry Turning AISI 1045 Steel using CBN Tool by Response Surface Methodology, GE International Journal of Engineering Research, Vol. 3, Issue 7, 2015, 69–82.
[38]	Kassab, S. Y. and Khoshnaw, Y. K., The Effect of Cutting Tool Vibration on Surface Roughness of Work-piece in Dry Turning Operation, Engineering and technology, Vol. 25, No. 7, 2007, 879–889.
[39]	Han, X., Wang, M. and Ouyang, H., Vibration of Work-Pieces during Turning Operations, Journal of Physics: Conference Series 181, http://iopscience.iop.org/1742-6596/181/1/012032, 2009, 1–7.
[40]	Cahuc, O., K’nevez, J Y., Gerard, A., Darnis, P., Albert, G., Bisu, C. F., and Gerard, C., Self-Excited Vibrations in Turning: Cutting Moment Analysis, International Journal of Advanced manufacturing Technology, version 1 – 9, 2010, 1–9.
[41]	Delijaicov, S., Leonardi, F., Bordinassi, E. C., and Batalha, G. F., Improved Model to predict Machined Surface Roughness based on the Cutting Vibrations signal during Hard Turning, Archives of Materials Science and Engineering, Vol. 45, Issue 2, 2010, 102–107.
[42]	Rogov, V. A. and Siamak, G., Optimization of Surface Roughness and Vibration in Turning of Aluminum Alloy AA2024 Using Taguchi Technique, International Journal of Mechanical, Aerospace, Industrial, Mechatronic and Manufacturing Engineering Vol. 7, No. 11, 2013, 2330–2339.
[43]	Rogov, V. A. and Siamak, G., The Effect of Tool Construction and Cutting Parameters on Surface Roughness and Vibration in Turning of AISI 1045 Steel Using Taguchi Method, Modern Mechanical Engineering, 4, 2014, 8–18.

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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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