American Journal of Neural Networks and Applications

| Peer-Reviewed |

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

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

Share This Article

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.

DOI 10.11648/j.ajnna.20170306.11
Published in American Journal of Neural Networks and Applications (Volume 3, Issue 6, December 2017)
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), 2024. Published by Science Publishing Group

Keywords

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

References
[1] Makadia, A. J., and Nanavati, J. I., “Optimization of Machining Parameters for Turning Operations Based on Response Surface Methodology”, Measurement, Vol. 46, No. 4, (2012), 1521-1529.
[2] Rao, V., “Advanced Modeling and Optimization of manufacturing processes”, London, Springer, (2011).
[3] Sandor, B., and Bel., K., “Lean Production Planning for 5 Axes CNC Driven Milling Machine”, Machine Learning Research, Vol.2, No.2, (2017), 66-72.
[4] Yang, X. S., “Engineering Optimization: An Introduction with Metaheuristic Applications”, New York, John Wiley and Sons Inc, (2010).
[5] Gao, W. F., and Liu, S. Y., “A Modified Artificial Bee Colony”, Computers & Operations Research, Vol. 39, No. 2, (2012), 687-697.
[6] Haupt, R. L., and Koehler, A. B., “Practical Genetic Algorithms”, New York, John Wiley and Sons Inc, (1998).
[7] Chandrasekaran, K., Marimuthu, P., and Raja, K., “Prediction Model for CNC Turning on AISI316 with Single and Multilayered Cutting Tool Using Box Behnken Design”, International Journal of Engineering, Transactions A: Basics, Vol. 26, No. 4, (2013), 401-410.
[8] Azadi Moghaddam, M., and Kolahan, F., “Modeling and Optimization of Surface Roughness of AISI2312 Hot Worked Steel in EDM based on Mathematical Modeling and Genetic Algorithm”, International Journal of Engineering, Transactions C: Aspects, Vol. 27, No. 3, (2014), 417-424.
[9] Alimirzaloo, V., Modanloo, V., and Babazadeh Asbagh, E., “Experimental Investigation of the Effect of Process Parameters on the Surface Roughness in Finishing Process of Chrome Coated Printing Cylinders”, International Journal of Engineering, Transactions C: Aspects, Vol. 29, No. 12, (2016), 1775-1782.
[10] Kalidass, S., and Mathavaraj Ravikumar, T., “Cutting Force Prediction in End Milling Process of AISI 304 Steel Using Solid Carbide Tools”, International Journal of Engineering, Transactions A: Basics, Vol. 28, No. 7, (2015), 1074-1081.
[11] Shivade, A. S., Bhagat, S., Jagdale, S., Nikam, A., and Londhe, P., “Optimization of Machining Parameters for Turning Using Taguchi Approach”, International Journal of Recent Technology and Engineering, Vol. 3, No. 1, (2014), 145-149.
[12] Khamaruzaman, Y., Nuraddeen, B., Muhammad, M., and Mohamed, I., “Linear Kernel Support Vector Machines for Modeling Pore-Water Pressure Responses”, Journal of Engineering Science and Technology, Vol.12, No.8, (2017), 2202-2212.
[13] Shukurillo, U., “Optimization of the Launching Process in the Electric Drive with the Help of Genetic Algorithm”, Machine Learning Research, Vol.2, No.2, (2017), 61-65.
[14] Michalewicz, Z., “Genetics Algorithm + Data Structures = Evolution Programs (3rd ed.)”, New York, Springer, (1996).
[15] Eberhart, R. C., and Kennedy, J., “A New Optimizer Using Particles Swarm Theory” in 6th International Symposium on Micro Machine and Human Science, Nagoya, Japan, 39-43. (1995).
[16] Eberhart, R. C., and Kennedy, J., “Particle swarm Optimization" in IEEE International Conference on Neural Network, Perth, Australia, 1942-1948, (1995).
[17] Bidya Prakash, M., and Shatendra, S., “A Review on Application of Bio-Geography Based Algorithm and Other Optimization Techniques, International Journal of Management, Information, Technology and Engineering, Vol. 3, No. 6, (2015), 19-28.
[18] Majhi, B. P., and Sahu, S., “A Review on Application of Bio-geography Based Algorithm and Other Optimization Techniques”, International Journal of Management, Information Technology and Engineering, Vol. 3, No. 6, (2015), 19-28.
[19] Balram, S., “Study of Simulated Annealing Based Algorithms for Multiobjective Optimization of a Constrained Problem”, Computers and Chemical Engineering, Vol. 28, No. 10, (2004), 1849-1871.
[20] Nooraziah, A., and Tiagrajah, V. J., “A Study on Regression Model Using Response Surface Methodology”, Applied Mechanics and Materials, Vol. 66, No. 6, (2014), 235-239.
Author Information
  • Department of Mechanical Engineering, Universiti Tenaga Nasional, Selangor, Malaysia

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

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

Cite This Article
  • 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

    Copy | Download

    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

    Copy | Download

    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://download.sciencepg.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

  • Sections