scholarly journals A NOVEL METHOD FOR SELECTING THE OPTIMAL EDM PROCESS FOR HASTELLOY B2 USING THE MODIFIED-ADDITIVE RATIO ASSESSMENT METHOD (M-ARAS) BASED ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS)

2021 ◽  
Vol 55 (6) ◽  
Author(s):  
Ramasubbu Narasimmalu ◽  
Ramabalan Sundaresan
Keyword(s):  
Process Parameters ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Metal Removal ◽  
Optimization Problems ◽  
Assessment Method ◽  
Electrode Wear ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Edm Process

Electrode wear and metal removal exhibited nonlinear behavior in the Electrical Discharge Machining (EDM) of Hastelloy B2 plate. Hence, mathematical modeling was used to solve this problem. The hole size, pulse duration, duty cycle, and current were selected as inputs. Squareness and taper angle were considered as responses. Therefore, the Modified-Additive Ratio Assessment Method (M-ARAS) based Adaptive Neuro Fuzzy Inference System (ANFIS) method was used to find the optimum EDM process parameters. The overall analysis showed that the M-ARAS-based ANFIS algorithm provided a good fit for optimization of the process parameters and could be used for further multi-objective optimization problems.  

Genetic algorithm-based drilling burr minimization using adaptive neuro-fuzzy inference system and support vector regression

2019 ◽  
Vol 234 (5) ◽  
pp. 956-968
Author(s):  
Nripen Mondal ◽  
Madhab Chandra Mandal ◽  
Bishal Dey ◽  
Santanu Das
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Regression ◽  
Process Parameters ◽  
Predictive Models ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Support Vector ◽  
Inference System ◽  
Burr Height ◽  
Neuro Fuzzy

Burrs are undesirable materials beyond the work piece surface during drilling or other machining processes, thus this should be as less as possible during manufacturing. The experimental study has been conducted according to the full factorial design method. A total of 27 experiments were conducted by drilling on an Aluminum 6061T6 plate by choosing three factors and three levels of process parameters like drill diameter, point angle and spindle speed. In this research article, two predictive models, namely, adaptive neuro-fuzzy inference system and support vector regression, are developed using experimental data to estimate burr height and burr thickness. Then, these predictive models have been used to find out optimum process parameters for minimum burr height and burr thickness using genetic algorithm. It has been found that both the models are able to predict burr size and thickness with good accuracy, while the adaptive neuro-fuzzy inference system performs better than support vector regression.

scholarly journals A Novel Methodology for Prediction Urban Water Demand by Wavelet Denoising and Adaptive Neuro-Fuzzy Inference System Approach

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1628 ◽  
Author(s):  
Salah Zubaidi ◽  
Hussein Al-Bugharbee ◽  
Sandra Ortega-Martorell ◽  
Sadik Gharghan ◽  
Ivan Olier ◽  
...  
Keyword(s):  
Fuzzy Inference System ◽  
Water Demand ◽  
Fuzzy Inference ◽  
Optimization Problems ◽  
Water System ◽  
Discrete Wavelet ◽  
Urban Water ◽  
Inference System ◽  
Urban Water Demand ◽  
Neuro Fuzzy

Accurate and reliable urban water demand prediction is imperative for providing the basis to design, operate, and manage water system, especially under the scarcity of the natural water resources. A new methodology combining discrete wavelet transform (DWT) with an adaptive neuro-fuzzy inference system (ANFIS) is proposed to predict monthly urban water demand based on several intervals of historical water consumption. This ANFIS model is evaluated against a hybrid crow search algorithm and artificial neural network (CSA-ANN), since these methods have been successfully used recently to tackle a range of engineering optimization problems. The study outcomes reveal that (1) data preprocessing is essential for denoising raw time series and choosing the model inputs to render the highest model performance; (2) both methodologies, ANFIS and CSA-ANN, are statistically equivalent and capable of accurately predicting monthly urban water demand with high accuracy based on several statistical metric measures such as coefficient of efficiency (0.974, 0.971, respectively). This study could help policymakers to manage extensions of urban water system in response to the increasing demand with low risk related to a decision.

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