Fuzzy possibility regression integrated with fuzzy adaptive neural network for predicting and optimizing electrical discharge machining parameters

Abstract An electrical discharge machining (EDM) is one of the special production methods that are widely used in moldings, repairs and production of specific industrial components. Due to extensive production costs, optimal machining specifications are significant. Machining specifications are effective on output quality and thus attract more customers leading to higher profits. In this study, the impact of EDM parameters on surface roughness, material removal rate and electrode corrosion percentage have been investigated. In order to consider uncertainty of real production environments, the fuzzy theory is employed. Also, using the design of experiment (DOE) parameters calibration is performed and mathematical programming approach is applied for optimization purpose. The relationship between the machining parameters and the output process specification is examined by a fuzzy possibility regression model. Then, the mathematical relation of exact inputs and fuzzy outputs of the EDM process are extracted. The effectiveness of the three outputs is evaluated by interfacing models and fuzzy hypothesis testing. To determine the optimal levels of each output, a fuzzy adaptive neural network is used and appropriate models are prepared to be adapted with a fitted model of fuzzy possibility regression for comparison purposes. Validation tests imply the effectiveness of the proposed method. The integrated model is implemented in real case study. The results show that, fitted models can predict the material removal rate, surface fineness, and corrosion percentage of the electrode. The prediction accuracy of the proposed method is shown in comparison with the optimal fuzzy adaptive neural network outputs considering error value. Also, the proposed method is successful in identifying the optimal process parameters for EDM with reliable accuracy. The proposed integrated prediction and optimization model can be used as a calibration decision support in production systems to handle dynamic data structures and provide real time machining specifications to increase the output quality.

[1]  Xuan-Phuong Dang,et al.  Constrained multi-objective optimization of EDM process parameters using kriging model and particle swarm algorithm , 2018 .

[2]  Mohammad Hessam Olya.  Applying Dijkstra’s algorithm for general shortest path problem with normal probability distribution arc length , 2014 .

[3]  Laith Abdullah Al-Juboori,et al.  Optimization of Electro Discharge Machining Process Parameters With Fuzzy Logic for Stainless Steel 304 (ASTM A240) , 2018 .

[4]  Harshit K. Dave,et al.  End wear compensation during planetary EDM of Ti–6Al–4V by adaptive neuro fuzzy inference system , 2018, Prod. Eng..

[5]  Alireza Arabpour,et al.  ESTIMATING THE PARAMETERS OF A FUZZY LINEAR REGRESSION MODEL , 2008 .

[6]  Nikola Kasabov,et al.  Foundations Of Neural Networks, Fuzzy Systems, And Knowledge Engineering [Books in Brief] , 1996, IEEE Transactions on Neural Networks.

[7]  S. N. Musa,et al.  GEP-based method to formulate adhesion strength and hardness of Nb PVD coated on Ti-6Al-7Nb aimed at developing mixed oxide nanotubular arrays. , 2016, Journal of the mechanical behavior of biomedical materials.

[8]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[9]  Arash Mohammadi,et al.  Fuzzy-control-based five-step Li-ion battery charger by using AC impedance technique , 2012, International Conference on Machine Vision.

[10]  Prabhu Sethuramalingam,et al.  Adaptive Neuro-Fuzzy Interference System Modelling of EDM Process Using CNT Infused Copper Electrode , 2017 .

[11]  R. Karthikeyan,et al.  Mathematical modelling for electric discharge machining of aluminium–silicon carbide particulate composites , 1999 .

[12]  Zack J. Butler,et al.  Tracking a moving object with a binary sensor network , 2003, SenSys '03.

[13]  Bernard De Baets,et al.  A comparison of fuzzy regression methods for the estimation of the implied volatility smile function , 2015, Fuzzy Sets Syst..

[14]  Jeyraj Selvaraj,et al.  Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming , 2017 .

[15]  Mohammad Hessam Olya.  Finding shortest path in a combined exponential – gamma probability distribution arc length , 2014 .

[16]  S. Hr. Aghay Kaboli,et al.  Rain-fall optimization algorithm: A population based algorithm for solving constrained optimization problems , 2017, J. Comput. Sci..

[17]  Ahmad Amiri,et al.  Toward improved mechanical, tribological, corrosion and in-vitro bioactivity properties of mixed oxide nanotubes on Ti-6Al-7Nb implant using multi-objective PSO. , 2017, Journal of the mechanical behavior of biomedical materials.

[18]  Ali Azadeh,et al.  A fuzzy regression approach for improvement of gasoline consumption estimation with uncertain data , 2013 .

[19]  Mohammad Mehdi Rashidi,et al.  Parametric analysis and optimization of regenerative Clausius and organic Rankine cycles with two feedwater heaters using artificial bees colony and artificial neural network , 2011 .

[20]  P. M. George,et al.  EDM machining of carbon–carbon composite—a Taguchi approach , 2004 .

[21]  Nasrudin Abd Rahim,et al.  Long-term electric energy consumption forecasting via artificial cooperative search algorithm , 2016 .

[22]  Ankit Thakkar,et al.  A new hybrid method for face recognition , 2013, 2013 Nirma University International Conference on Engineering (NUiCONE).

[24]  Mostafa Modiri-Delshad,et al.  Backtracking search algorithm for solving economic dispatch problems with valve-point effects and multiple fuel options , 2016 .

[25]  Saad Mekhilef,et al.  A PSO-DQ Current Control Scheme for Performance Enhancement of Z-Source Matrix Converter to Drive IM Fed by Abnormal Voltage , 2018, IEEE Transactions on Power Electronics.