Optimization and Selection of Boring Process Parameters for IS 2062 E250 Steel Plates Using Hybrid Taguchi-Pareto Box Behnken-Genetic Algorithm Method

Yakubu Umar Abdullahi, Sunday Ayoola Oke

Abstract


The integrated Taguchi-Pareto-Box-Behnken design (TP-BBD) method has been recognized as an effective method for boring operation optimization. Yet it has further optimization opportunities even with less information availability. In this study, the genetic algorithm (GA) was integrated with the TP-BBD method to form a novel TP-BBD-GA method to effectively deal with the paucity of boring data and generate multiple optimal solutions. Numerical simulation coupled with experimental data analysis was conducted to ascertain the effectiveness of the proposed method using literature data. To combine the procedure of the constituent methods, the authors analysed the literature data with the Taguchi-Pareto method. Then the output was used as inputs to the Box Behnken design method. Afterwards, linear programs with objective functions and constraints were formulated and introduced into the genetic algorithm structure and then solved using the python language. The results revealed that the proposed method exhibits good performance for boring operations as it predicts the best parameter i.e. speed, feed rate, depth of cut and noise radius values for optimal surface roughness values. This article offers a unique contribution to the boring literature since it examines an additional optimization procedure to the existing one. The study analyzes the optimization behaviour of the IS 2062 E250 steel plates in the boring process and gives an easy procedure for process engineers on improving the boring operations.

Keywords


Genetic algorithm, boring operation, optimization, selection, steel plates

Full Text:

PDF

References


Abdullahi Y.U. and Oke S.A. (2022). Optimizing the boring parameters on CNC machine using IS 2062 E250 steel plates: Taguchi-Pareto-Box Behnken design and Taguchi-ABC-Box Behnken design perspectives, Engineering Access, 8(2), 219-241.

Ahmad, N., Tanaka, T., & Saito, Y. (2005). Optimization of cutting parameters for end milling operation by soap based genetic algorithm. Power , 318(2), 5.

Atia M., Khalil J., Mokhtar M. (2017). A cost estimation model for machining operations: An ANN parametric approach, Journal of Al-Azhar University Engineering Sector, 12(44), 878-885. https://doi.org/10.21608/auej.2017.19195

Čuboňová, N., Dodok, T., & Ságová, Z. (2019). Optimisation of the machining process using genetic algorithm. Scientific Journal of Silesian University of Technology. Series Transport. 104, 15-25. http://dx.doi.org/10.20858/sjsutst.2019.104.2

Dave, S., Vora, J. J., Thakkar, N., Singh, A., Srivastava, S., Gadhvi, B., Patel, V.V., Kumar, A. (2016). Optimization of EDM drilling parameters for aluminium 2024 alloy using response surface methodology and genetic algorithm. Key Engineering Materials, 706, pp. 3–8. http://dx.doi.org/10.4028/www.scientific.net/KEM.706.3

Dennison M.S., Abdul Khadar S.D., Karuppusami 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, 2(4), pp. 544-548

Dhavamani, C., & Alwarsamy, T. (2012). Optimization of machining parameters for aluminium and silicon carbide composite using genetic algorithm. Procedia Engineering, 38, 1994–2004. https://doi.org/10.1016/j.proeng.2012.06.241

Ganesan, H., Mohankumar, G., Ganesan, K., & Ramesh Kumar, K. (2011). Optimization of machining parameters in turning process using genetic algorithm and particle swarm optimization with experimental verification. International Journal of Engineering Science and Technology , 3(2), 1091–1102.

Izelu, C.O., Essien, E.I., Okwu, M.O., Garba, D.K. & Agunobi-Ozoekwe, C.N. (2021). Lathe boring operation on ASTM A304 steel parameter optimization using response surface methodology, Australian Journal of Mechanical Engineering, 19(5), 544-558. https://doi.org/10.1080/14484846.2019.1662534

Khundrakpam, N. S., Brar, G. S., & Deepak, D. (2018). Genetic algorithm approach for optimizing surface roughness of Near dry EDM. IOP Conference Series: Materials Science and Engineering, 376(1), p. 012130. http://dx.doi.org/10.1088/1757-899X/376/1/012130

Kilickap, E., & Huseyinoglu, M. (2010). Selection of optimum drilling parameters on burr height using response surface methodology and genetic algorithm in drilling of AISI 304 stainless steel. Materials and Manufacturing Processes, 25(10), 1068–1076. https://doi.org/10.1080/10426911003720854

Kilickap, E., Huseyinoglu, M., & Yardimeden, A. (2011). Optimization of drilling parameters on surface roughness in drilling of AISI 1045 using response surface methodology and genetic algorithm. The International Journal of Advanced Manufacturing Technology , 52(1), 79–88. http://dx.doi.org/10.1007/s00170-010-2710-7

Kumar, S., Meenu, Satsangi, P.S. (2012). A genetic algorithmic approach for optimization of surface roughness prediction model in turning using UD-GFRP composite. 19(6), 386-396.

Mahesh, G., Muthu, S., & Devadasan, S. R. (2015). Prediction of surface roughness of end milling operation using genetic algorithm. The International Journal of Advanced Manufacturing Technology , 77(1-4), 369–381. https://doi.org/10.1007/s00170-014-6425-z

Marimuthu, P., Kumar, K., Raja, S., & Karthikeyan, D. S. (2015). Analyse and optimise machining parameters setting for CNC turning of inconel X-750 using genetic algorithm. 10(55), 3978-3981.

Nugroho, W., Baba, N.B. and Saptari A. (2016). Optimization on surface roughness of boring process by varying damper position, ARPN Journal of Engineering and Applied Sciences, 11(20), 11911-11918

Palanisamy, P., Rajendran, I., & Shanmugasundaram, S. (2007). Optimization of machining parameters using genetic algorithm and experimental validation for end-milling operations. The International Journal of Advanced Manufacturing Technology , 32(7), 644–655. https://doi.org/10.1007/s00170-005-0384-3

Rao, V. D., Raju, K. M., Subbarayan, N. V., & Mahesh, P. (2018). Multi objective optimization of surface roughness and material removal rate in end milling using genetic algorithm. AUT Journal of Mechanical Engineering, 2(1), 117-123, https://doi.org/10.22060/ajme.2018.12581.5373

Reddy, B.S.K., Nagaraju, S.K., Salmanm M.D. (2015), A study on optimisation of resources for multiple projects by using primavera, Journal of Engineering Science and Technology, 10(2), 235–248

Reddy N.S.K. & Rao, P.V. (2005). A genetic algorithmic approach for optimization of surface roughness prediction model in dry milling. Machine Science and Technology, 9, 63–84. https://doi.org/10.1081/MST-200051263

Saffar, R. J., Razfar, M. R., Salimi, A. H., Khani, M. M. (2009). Optimization of machining parameters to minimize tool deflection in the end milling operation using Genetic Algorithm. World Applied Sciences Journal , 6(1), 64–69.

Sangwan, K. S., & Kant, G. (2017). Optimization of machining parameters for improving energy efficiency using integrated response surface methodology and genetic algorithm approach. Procedia CIRP , 61, 517–522, https://doi.org/10.1016/j.procir.2016.11.162

Sardinas, R. Q., Santana, M. R., & Brindis, E. A. (2006). Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes. Engineering Applications of Artificial Intelligence , 19(2), 127–133, https://doi.org/10.1016/j.engappai.2005.06.007

Tien, D.H., Nguyen, N.-T., Trung, D.D., Nguyen, V.C.C., Nguyen, Q., Luat, N.V., Huu, P.N. (2020). Optimization of cutting parameters and cutter helix angle for minimum surface roughness in flat-end milling of Al6061. Optimization, 62(4), pp. 1321-1331.

Zain, A. M., Haron, H., & Sharif, S. (2010). Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process. Expert Systems with Applications, 37, 4650–4659. https://doi.org/10.1016/j.eswa.2009.12.043

Zeelan, B. N., Kumar, S. G., & Mosisa, E. (2013). Determining the Effect of Cutting Parameters on Surface Roughness Using Genetic Algorithm. Science, Technology and Arts Research Journal, 2, 98–101. https://doi.org/10.4314/star.v2i4.17




DOI: http://dx.doi.org/10.22441/ijiem.v3i2.15443

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

IJIEM - Indonesian Journal of Industrial Engineering & Management
Program Pascasarjana Magister Teknik Industri Universitas Mercu Buana
Kampus Menteng - Gedung Tedja Buana, Floor 4th  
Jl. Menteng Raya No. 29  Jakarta Pusat- Indonesia
Tlp.: +62 21 31935454 Fax: +62  21 31934474
http://publikasi.mercubuana.ac.id/index.php/ijiem

Email:  [email protected]

 

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

 

Web Analytics Made Easy - Statcounter View My Stats

The journal is indexed by: