Modeling and Optimization of Carbon Steel AISI 1045 Surface Roughness in CNC Turning Based on Response Surface Methodology and Heuristic Optimization Algorithms

Vijay Nagandran; Tiagrajah V. Janahiraman; Nooraziah Ahmad

doi:doi:10.11648/j.ajnna.20170306.11

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Modeling and Optimization of Carbon Steel AISI 1045 Surface Roughness in CNC Turning Based on Response Surface Methodology and Heuristic Optimization Algorithms

Vijay Nagandran, Tiagrajah V. Janahiraman, Nooraziah Ahmad

Published in American Journal of Neural Networks and Applications (Volume 3, Issue 6)

Received: 30 October 2017 Accepted: 22 November 2017 Published: 11 January 2018

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Abstract

Surface roughness or surface quality is considered to be one of the most crucial requirement of a machined part since it directly influences the mechanical properties of the part. However, the traditional method of choosing cutting parameters’ values to obtain a good surface finish has its own disadvantages. Therefore, an experimental study has been conducted to develop a suitable mathematical model and pair it with an optimization technique that able to produce low surface roughness of carbon steel AISI 1045. Response surface methodology (RSM) is used to develop the mathematical model whereas three types of heuristic optimization methods namely Genetics Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) employed to optimize the model and find the optimal cutting parameters’ values. A brief comparison of the three optimization methods has been made to study their performance to the developed model. Experimental results indicate that the proposed modeling technique and PSO are quite efficient in determining optimal cutting parameters for CNC turning of carbon steel AISI 1045.

Published in	American Journal of Neural Networks and Applications (Volume 3, Issue 6)
DOI	10.11648/j.ajnna.20170306.11
Page(s)	56-62
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), 2018. Published by Science Publishing Group

Keywords

Carbon Steel AISI 1045, Genetic Algorithm, Particle Swarm Optimization, Response Surface Methodology, Simulated Annealing, Surface Roughness

References

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APA Style

Vijay Nagandran, Tiagrajah V. Janahiraman, Nooraziah Ahmad. (2018). Modeling and Optimization of Carbon Steel AISI 1045 Surface Roughness in CNC Turning Based on Response Surface Methodology and Heuristic Optimization Algorithms. American Journal of Neural Networks and Applications, 3(6), 56-62. https://doi.org/10.11648/j.ajnna.20170306.11

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ACS Style

Vijay Nagandran; Tiagrajah V. Janahiraman; Nooraziah Ahmad. Modeling and Optimization of Carbon Steel AISI 1045 Surface Roughness in CNC Turning Based on Response Surface Methodology and Heuristic Optimization Algorithms. Am. J. Neural Netw. Appl. 2018, 3(6), 56-62. doi: 10.11648/j.ajnna.20170306.11

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AMA Style

Vijay Nagandran, Tiagrajah V. Janahiraman, Nooraziah Ahmad. Modeling and Optimization of Carbon Steel AISI 1045 Surface Roughness in CNC Turning Based on Response Surface Methodology and Heuristic Optimization Algorithms. Am J Neural Netw Appl. 2018;3(6):56-62. doi: 10.11648/j.ajnna.20170306.11

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@article{10.11648/j.ajnna.20170306.11,
  author = {Vijay Nagandran and Tiagrajah V. Janahiraman and Nooraziah Ahmad},
  title = {Modeling and Optimization of Carbon Steel AISI 1045 Surface Roughness in CNC Turning Based on Response Surface Methodology and Heuristic Optimization Algorithms},
  journal = {American Journal of Neural Networks and Applications},
  volume = {3},
  number = {6},
  pages = {56-62},
  doi = {10.11648/j.ajnna.20170306.11},
  url = {https://doi.org/10.11648/j.ajnna.20170306.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20170306.11},
  abstract = {Surface roughness or surface quality is considered to be one of the most crucial requirement of a machined part since it directly influences the mechanical properties of the part. However, the traditional method of choosing cutting parameters’ values to obtain a good surface finish has its own disadvantages. Therefore, an experimental study has been conducted to develop a suitable mathematical model and pair it with an optimization technique that able to produce low surface roughness of carbon steel AISI 1045. Response surface methodology (RSM) is used to develop the mathematical model whereas three types of heuristic optimization methods namely Genetics Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) employed to optimize the model and find the optimal cutting parameters’ values. A brief comparison of the three optimization methods has been made to study their performance to the developed model. Experimental results indicate that the proposed modeling technique and PSO are quite efficient in determining optimal cutting parameters for CNC turning of carbon steel AISI 1045.},
 year = {2018}
}

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TY  - JOUR
T1  - Modeling and Optimization of Carbon Steel AISI 1045 Surface Roughness in CNC Turning Based on Response Surface Methodology and Heuristic Optimization Algorithms
AU  - Vijay Nagandran
AU  - Tiagrajah V. Janahiraman
AU  - Nooraziah Ahmad
Y1  - 2018/01/11
PY  - 2018
N1  - https://doi.org/10.11648/j.ajnna.20170306.11
DO  - 10.11648/j.ajnna.20170306.11
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 56
EP  - 62
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20170306.11
AB  - Surface roughness or surface quality is considered to be one of the most crucial requirement of a machined part since it directly influences the mechanical properties of the part. However, the traditional method of choosing cutting parameters’ values to obtain a good surface finish has its own disadvantages. Therefore, an experimental study has been conducted to develop a suitable mathematical model and pair it with an optimization technique that able to produce low surface roughness of carbon steel AISI 1045. Response surface methodology (RSM) is used to develop the mathematical model whereas three types of heuristic optimization methods namely Genetics Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) employed to optimize the model and find the optimal cutting parameters’ values. A brief comparison of the three optimization methods has been made to study their performance to the developed model. Experimental results indicate that the proposed modeling technique and PSO are quite efficient in determining optimal cutting parameters for CNC turning of carbon steel AISI 1045.
VL  - 3
IS  - 6
ER  -

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Author Information

Vijay Nagandran

Department of Mechanical Engineering, Universiti Tenaga Nasional, Selangor, Malaysia
Tiagrajah V. Janahiraman

Department of Electronics and Communication Engineering, Universiti Tenaga Nasional, Selangor, Malaysia
Nooraziah Ahmad

Faculty of Creative Technology and Heritage, Universiti Malaysia Kelantan, Kelantan, Malaysia

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APA Style

Vijay Nagandran, Tiagrajah V. Janahiraman, Nooraziah Ahmad. (2018). Modeling and Optimization of Carbon Steel AISI 1045 Surface Roughness in CNC Turning Based on Response Surface Methodology and Heuristic Optimization Algorithms. American Journal of Neural Networks and Applications, 3(6), 56-62. https://doi.org/10.11648/j.ajnna.20170306.11

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ACS Style

Vijay Nagandran; Tiagrajah V. Janahiraman; Nooraziah Ahmad. Modeling and Optimization of Carbon Steel AISI 1045 Surface Roughness in CNC Turning Based on Response Surface Methodology and Heuristic Optimization Algorithms. Am. J. Neural Netw. Appl. 2018, 3(6), 56-62. doi: 10.11648/j.ajnna.20170306.11

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AMA Style

Vijay Nagandran, Tiagrajah V. Janahiraman, Nooraziah Ahmad. Modeling and Optimization of Carbon Steel AISI 1045 Surface Roughness in CNC Turning Based on Response Surface Methodology and Heuristic Optimization Algorithms. Am J Neural Netw Appl. 2018;3(6):56-62. doi: 10.11648/j.ajnna.20170306.11

Copy | Download

@article{10.11648/j.ajnna.20170306.11,
  author = {Vijay Nagandran and Tiagrajah V. Janahiraman and Nooraziah Ahmad},
  title = {Modeling and Optimization of Carbon Steel AISI 1045 Surface Roughness in CNC Turning Based on Response Surface Methodology and Heuristic Optimization Algorithms},
  journal = {American Journal of Neural Networks and Applications},
  volume = {3},
  number = {6},
  pages = {56-62},
  doi = {10.11648/j.ajnna.20170306.11},
  url = {https://doi.org/10.11648/j.ajnna.20170306.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20170306.11},
  abstract = {Surface roughness or surface quality is considered to be one of the most crucial requirement of a machined part since it directly influences the mechanical properties of the part. However, the traditional method of choosing cutting parameters’ values to obtain a good surface finish has its own disadvantages. Therefore, an experimental study has been conducted to develop a suitable mathematical model and pair it with an optimization technique that able to produce low surface roughness of carbon steel AISI 1045. Response surface methodology (RSM) is used to develop the mathematical model whereas three types of heuristic optimization methods namely Genetics Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) employed to optimize the model and find the optimal cutting parameters’ values. A brief comparison of the three optimization methods has been made to study their performance to the developed model. Experimental results indicate that the proposed modeling technique and PSO are quite efficient in determining optimal cutting parameters for CNC turning of carbon steel AISI 1045.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - Modeling and Optimization of Carbon Steel AISI 1045 Surface Roughness in CNC Turning Based on Response Surface Methodology and Heuristic Optimization Algorithms
AU  - Vijay Nagandran
AU  - Tiagrajah V. Janahiraman
AU  - Nooraziah Ahmad
Y1  - 2018/01/11
PY  - 2018
N1  - https://doi.org/10.11648/j.ajnna.20170306.11
DO  - 10.11648/j.ajnna.20170306.11
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 56
EP  - 62
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20170306.11
AB  - Surface roughness or surface quality is considered to be one of the most crucial requirement of a machined part since it directly influences the mechanical properties of the part. However, the traditional method of choosing cutting parameters’ values to obtain a good surface finish has its own disadvantages. Therefore, an experimental study has been conducted to develop a suitable mathematical model and pair it with an optimization technique that able to produce low surface roughness of carbon steel AISI 1045. Response surface methodology (RSM) is used to develop the mathematical model whereas three types of heuristic optimization methods namely Genetics Algorithm (GA), Particle Swarm Optimization (PSO) and Simulated Annealing (SA) employed to optimize the model and find the optimal cutting parameters’ values. A brief comparison of the three optimization methods has been made to study their performance to the developed model. Experimental results indicate that the proposed modeling technique and PSO are quite efficient in determining optimal cutting parameters for CNC turning of carbon steel AISI 1045.
VL  - 3
IS  - 6
ER  -

Copy | Download