An effective approach of adaptive neuro-fuzzy inference system-integrated teaching learning-based optimization for use in machining optimization of S45C CNC turning

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.

[1]  Tarek Mabrouki,et al.  Analysis of surface roughness and cutting force components in hard turning with CBN tool: Prediction model and cutting conditions optimization , 2012 .

[2]  Tarek Mabrouki,et al.  Design optimization for minimum technological parameters when dry turning of AISI D3 steel using Taguchi method , 2017 .

[3]  Angelos P. Markopoulos,et al.  Surface roughness prediction for the milling of Ti–6Al–4V ELI alloy with the use of statistical and soft computing techniques , 2016 .

[4]  Thanh-Phong Dao,et al.  Hybrid Taguchi-cuckoo search algorithm for optimization of a compliant focus positioning platform , 2017, Appl. Soft Comput..

[5]  J. Paulo Davim,et al.  Parametric design optimization of hard turning of AISI 4340 steel (69 HRC) , 2016 .

[6]  İlhan Asiltürk,et al.  Determining the effect of cutting parameters on surface roughness in hard turning using the Taguchi method , 2011 .

[7]  A. Dadgarnia,et al.  A Fast Systematic Approach for Microstrip Antenna Design and Optimization using ANFIS and GA , 2010 .

[8]  Joao P. S. Catalao,et al.  A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal , 2011 .

[9]  Ching-Hsue Cheng,et al.  One step-ahead ANFIS time series model for forecasting electricity loads , 2010 .

[10]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[11]  Ravipudi Venkata Rao,et al.  Jaya: An Advanced Optimization Algorithm and its Engineering Applications , 2018 .

[12]  Thanh-Phong Dao,et al.  Robust parameter design for a compliant microgripper based on hybrid Taguchi-differential evolution algorithm , 2018 .

[13]  Yang Wang,et al.  Optimization of surface roughness in laser-assisted machining of metal matrix composites using Taguchi method , 2017 .

[14]  Kamel Chaoui,et al.  Machining of tough polyethylene pipe material: surface roughness and cutting temperature optimization , 2017 .

[15]  K. H. Hashmi,et al.  Optimization of process parameters for high speed machining of Ti-6Al-4V using response surface methodology , 2016 .

[16]  Mehmet Çunkas,et al.  Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method , 2011, Expert Syst. Appl..

[17]  Ulaş Çaydaş,et al.  Optimization of turning parameters for surface roughness and tool life based on the Taguchi method , 2008 .

[18]  R. Venkata Rao,et al.  Teaching Learning Based Optimization Algorithm: And Its Engineering Applications , 2015 .

[19]  Yung-Tien Liu,et al.  An investigation into the aspheric ultraprecision machining using the response surface methodology , 2016 .

[20]  R. Rao.  Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems , 2016 .

[21]  Mu-Yen Chen,et al.  A hybrid ANFIS model for business failure prediction utilizing particle swarm optimization and subtractive clustering , 2013, Inf. Sci..

[22]  Thanh-Phong Dao,et al.  Robust Parameter Design and Analysis of a Leaf Compliant Joint for Micropositioning Systems , 2017 .

[23]  Trung Nguyen-Thoi,et al.  Damage assessment in truss structures with limited sensors using a two-stage method and model reduction , 2018, Appl. Soft Comput..