Abstract
This paper proposes an effective integration of the Taguchi method (TM), Adaptive neuro-fuzzy inference system (ANFIS) and Teaching learning-based optimization (TLBO) for CNC turning optimization of S45C carbon steel. The TM plays two main roles: it reduces the number of experiments and identifies the most appropriate membership functions (MFs) and suitable learning procedure for the ANFIS. To determine the suitable ANFIS structure, we optimize the root mean squared error, a performance criterion of the ANFIS. Then, taking the established ANFIS structure, we form the virtual mathematical relations between the geometric parameters and the roughness surfaces. The results found that the triangular-shaped MFs and π-shaped MFs are the best for the Ra and Rz roughness surfaces, respectively. The optimal parameters for ANFIS structure of Ra are found in terms of the number of input MFs of 3, the trimf MFs, hybrid learning method, and linear output MFs. The optimal parameters for ANFIS structure of Rz are determined at the number of input MFs of 3, the pimf MFs, hybrid learning method, and linear output MFs. Based on the improved ANFIS establishments and optimal parameters of TLBO, the TLBO-based ANFIS is used to optimize the design parameters of the turning. We apply analysis of variance to determine the significant contribution of each factor. The results show a relative decrease in the roughness surfaces compared to those predicted by other algorithms. Therefore, the proposed optimization approach is a robust and effective tool for engineering applications.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aouici H, Yallese MA, Chaoui K, Mabrouki T, Rigal J-F (2012) Analysis of surface roughness and cutting force components in hard turning with CBN tool: prediction model and cutting conditions optimization. Measurement 45:344–353. https://doi.org/10.1016/j.measurement.2011.11.011
Asiltürk İ, Akkuş H (2011) Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method. Measurement 44(9):1697–1704. https://doi.org/10.1016/j.measurement.2011.07.003
Asiltürk İ, Çunkaş M (2011) Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Syst Appl 38:5826–5832. https://doi.org/10.1016/j.eswa.2010.11.041
Chau NL, Dang VA, Le HG, Dao TP (2017) Robust parameter design and analysis of a leaf compliant joint for micropositioning systems. Arab J Sci Eng 42(11):4811–4823. https://doi.org/10.1007/s13369-017-2682-0
Chen M-Y (2013) A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering. Inf Sci 220:180–195. https://doi.org/10.1016/j.ins.2011.09.013
Cheng C-H, Wei L-Y (2009) One step-ahead ANFIS time series model for forecasting electricity loads. Optim Eng 11:303–317. https://doi.org/10.1007/s11081-009-9091-5
Dadgarnia A, Heidari AA (2010) A fast systematic approach for microstrip antenna design and optimization using ANFIS and GA. J Electromag Waves Appl 24:2207–2221. https://doi.org/10.1163/156939310793699037
Dao TP, Huang S-C, Thang PT (2017) Hybrid Taguchi-cuckoo search algorithm for optimization of a compliant focus positioning platform. Appl Soft Comput 57:526–538. https://doi.org/10.1016/j.asoc.2017.04.038
Dao TP, Huang SC, Chau NL (2018) Robust parameter design for a compliant microgripper based on hybrid Taguchi-differential evolution algorithm. Microsyst Technol 24(3):1461–1477. https://doi.org/10.1007/s00542-017-3534-2
Dinh-Cong Du, Vo-Duy T, Nguyen-Thoi T (2018) Damage assessment in truss structures with limited sensors using a two-stage method and model reduction. Appl Soft Comput 66:264–277. https://doi.org/10.1016/j.asoc.2018.02.028
Hamlaoui N, Azzouz S, Chaoui K, Azari Z, Yallese MA (2017) Machining of tough polyethylene pipe material: surface roughness and cutting temperature optimization. Int J Adv Manuf Technol 92:2231–2245. https://doi.org/10.1007/s00170-017-0275-4
Hasçalık A, Çaydaş U (2007) Optimization of turning parameters for surface roughness and tool life based on the Taguchi method. Int J Adv Manuf Technol 38:896–903. https://doi.org/10.1007/s00170-007-1147-0
Hashmi KH, Zakria G, Raza MB, Khalil S (2015) Optimization of process parameters for high speed machining of Ti–6Al–4V using response surface methodology. Int J Adv Manuf Technol 85:1847–1856. https://doi.org/10.1007/s00170-015-8057-3
Karkalos NE, Galanis NI, Markopoulos AP (2016) Surface roughness prediction for the milling of Ti–6Al–4V ELI alloy with the use of statistical and soft computing techniques. Measurement 90:25–35. https://doi.org/10.1016/j.measurement.2016.04.039
Kong X, Yang L, Zhang H, Chi G, Wang Y (2016) Optimization of surface roughness in laser-assisted machining of metal matrix composites using Taguchi method. Int J Adv Manuf Technol 89:529–542. https://doi.org/10.1007/s00170-016-9115-1
Liu Y-T, Zhang L (2016) An investigation into the aspheric ultraprecision machining using the response surface methodology. Precis Eng 44:203–210. https://doi.org/10.1016/j.precisioneng.2015.12.006
Pousinho HMI, Mendes VMF, Catalão JPS (2011) A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal. Energy Convers Manag 52:397–402. https://doi.org/10.1016/j.enconman.2010.07.015
Rao RV (2016a) Teaching learning based optimization algorithm and its engineering applications. Springer, Basel. https://doi.org/10.5267/j.ijiec.2015.8.004. ISBN 978-3-319-22732-0
Rao RV (2016b) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput 7(1):19–34. https://doi.org/10.5267/j.ijiec.2015.8.004
Rao RV (2018) Jaya: an advanced optimization algorithm and its engineering applications. Springer, Basel. ISBN 978-3-319-78922-4
Rao RV, Savsani VJ, Vakharia DP (2011) Teaching–learning-based optimization: a novel method for constrained mechanical design optimization problems. Comput Aided Des 43(3):303–315. https://doi.org/10.1016/j.cad.2010.12.015
Rashid WB, Goel S, Davim JP, Joshi SN (2015) Parametric design optimization of hard turning of AISI 4340 steel (69 HRC). Int J Adv Manuf Technol 82:451–462. https://doi.org/10.1007/s00170-015-7337-2
Zerti O, Yallese MA, Khettabi R, Chaoui K, Mabrouki T (2016) Design optimization for minimum technological parameters when dry turning of AISI D3 steel using Taguchi method. Int J Adv Manuf Technol 89:1915–1934. https://doi.org/10.1007/s00170-016-9162-7
Acknowledgements
This research is funded by Vietnam National Foundation for Science and Technology Development (NAFOSTED) under Grant No. 107.01-2016.20.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Le Chau, N., Nguyen, MQ., Dao, TP. et al. An effective approach of adaptive neuro-fuzzy inference system-integrated teaching learning-based optimization for use in machining optimization of S45C CNC turning. Optim Eng 20, 811–832 (2019). https://doi.org/10.1007/s11081-018-09418-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11081-018-09418-x