Experimental Investigation and Optimization of Machining Parameters in Milling of Al6351 Using Hybrid – Artificial Bee Colony Algorithm

P. Hema*, G. Padmanabhan**, T. Eswar***
*_*** Department of Mechanical Engineering, S. V. University college of Engineering, Tirupati, Andhra Pradesh, India.
Periodicity:May - July'2019
DOI : https://doi.org/10.26634/jme.9.3.16059

Abstract

Simplifying any process of machining is a profoundly difficult, since it basically includes forecasts of ideal cutting parameters and working requirements that are unpredictable and extremely non-linear in nature which influence the overall production costs and workpiece quality. One of the Nature Inspired Algorithms (NIA) is Artificial Bee Colony (ABC) algorithm for process optimization that imitates honey bees intelligent foraging behavior. This paper describes an experimental study of cutting parameters optimization like Cutting Speed, Feed rate, Aluminum Alloy 6351 response depth cut by using Swarm-based optimization. Based on Taguchi design of experiments L18 orthogonal array is selected with three levels of input parameters at various machining conditions. The experiments are performed and predicted the responses like Surface Roughness, Material Removal Rate, Resultant Forces and Temperature. A recent evolutionary heuristic swarm intelligence algorithm called the Hybrid Artificial Bee Colony (HABC) is used to optimize conventional milling processes. This algorithm is used to minimize responses by estimating the optimum parameters of the process. Comparison of the results with the Harmony Search Algorithm (HSA), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are done to examine the performances of various methods. The results suggest that the HABC algorithm outperforms the solution's HSA, PSO and GA quality. Additionally, Multi-Objective Optimization is performed and a combined normalized objective function (Z) is formulated by considering equal weightages to all the objectives. The optimized values of milling parameters are obtained through the HABC algorithm. Confirmatory experiments reveal that the experimental values are moderately close with optimized values.

Keywords

Milling Machine, Nature Inspired Algorithms, Hybrid Artificial Bee Colony (HABC) Algorithm, Multi – Objective Optimization, Aluminium Alloy 6351.

How to Cite this Article?

Hema, P., Padmanabhan, G., and Eswar, T. (2019). Experimental Investigation and Optimization of Machining Parameters in Milling of Al6351 Using Hybrid – Artificial Bee Colony Algorithm. i-manager’s Journal on Mechanical Engineering, 9(3),9-18. https://doi.org/10.26634/jme.9.3.16059

References

[1]. Agarwal, N. (2012). Surface roughness modeling with machining parameters (speed, feed & depth of cut) in CNC milling. MIT International Journal of Mechanical Engineering, 2(1), 55-61.
[2]. Bahirie, S., & Pothar, V. (2014). Optimization of milling conditions by using particle swarm optimization technique: a review. International Journal of Engineering Trends and Technology, 18(6), 248-251.
[3]. Chavan, S. Y., & Jadhav, V. S. (2013). Determination of optimum cutting parameters for multiperformance characteristics in CNC end milling of Al-Si7Mg aluminum alloy. International Journal of Engineering and Technical Research (IJETR), 1(6),15-21.
[4]. Hricova, J., Kovac, M., & Sugar, P. (2014). Experimental investigation of high speed milling of aluminium alloy. Technical Gazette, 21(4), 773-777.
[5]. Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization (Vol. 200, pp.1-10). Technical report-tr06, Erciyes University, Computer Engineering Department.
[6]. Karaboga, D., & Basturk, B. (2007, June). Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In Melin P., Castillo O., Aguilar L.T., Kacprzyk J., & Pedrycz W. (Eds) International fuzzy systems association world congress (pp. 789-798). Berlin, Heidelberg: Springer https://doi.org/10.1007/978-3-540- 72950-1_77
[7]. Manjarres, D., Landa-Torres, I., Gil-Lopez, S., Del Ser, J., Bilbao, M. N., Salcedo-Sanz, S., & Geem, Z. W. (2013). A survey on applications of the harmony search algorithm. Engineering Applications of Artificial Intelligence, 26(8), 1818-1831. https://doi.org/10.1016/j.engappai.2013. 05.008
[8]. Prajapati, V. M., Thakkar, K. H., Thakkar, S.A. , & Parikh, H. B. (2013). Study and investigate effects of cutting parameters in CNC milling process for aluminium alloy- 8011h14 through taguchi design method. International Journal of Innovative Research in Science, Engineering and Technology, 2(1), 3271-3276.
[9]. Prasanth, R. S. S. & Raj, K. H. (2015). Artificial bee colony – Differential evolution (ABC-DE) algorithm for CNC turning process optimization. GE-International Journal of Engineering Research, 3(5), 18-39.
[10]. Rao, S. S. (2009). Engineering optimization (4 Ed.). Hoboken, New Jersey: John Wiley & Sons.
[11]. Reddy, M. N., Reddy, M. N., Kumar, K. V., & Garre, P. (2014). Experimental investigation of tensile strength on Al 6351 to the aerospace structural applications. International Journal of Mechanical Engineering and Technology (IJMET), 5(2), 110-114.
[12]. Selvam, M. D., Dawood, D. A. S., & Karuppusami, D. G. (2012). Optimization of machining parameters for face milling operation in a vertical CNC milling machine using genetic algorithm. IRACST-Engineering Science and Technology: An International Journal (ESTIJ), 2(4), 544-548.
[13]. Sequeira, A. A, Prabhu, R. & Sriram, N. S.(2012). Effect of cutting parameters on cutting force and surface roughness of aluminium components using face milling process- A taguchi approach. IOSR Journal of Mechanical and Civil Engineering, 3(4), 7-13.
[14]. Solaiyappan, A., Mani, K., & Gopalan, V. (2014). Multiobjective optimization of process parameters for electrochemical machining of 6061Al/10% Wt Al2O3/5% Wt SiC composite using hybrid fuzzy-artificial bee colony algorithm. Jordan Journal of Mechanical and Industrial Engineering, 8(5), 323-331.
[15]. Vishnu, A. V., Tilak, K. B. G., Naidu, G. G., & Raju, D. G. J. (2015). Optimization of different process parameters of aluminium Alloy 6351 in CNC milling using Taguchi method. International Journal of Engineering Research and General Science, 3(2),407-413.
[16]. Yildiz, A. R. (2013). Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach. Information Sciences, 220, 399-407. https://doi.org/10.1016/j.ins.2012.07.012
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.