A comparative study on modelling material removal rate by ANFIS and polynomial methods in electrical discharge machining process. (January 2015)

• Record Type: Journal Article
• Authors: Al-Ghamdi, Khalid; Taylan, Osman
• Is Part Of: Computers & industrial engineering. Volume 79(2015)
• Page Start: 27
• Page End: 41
• Publication Date: 2015-01
• Subjects: EDM -- MRR -- Polynomial model -- Neuro-fuzzy model -- Non-conventional machining; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2014.10.023
• Languages: English
• ISSNs: 0360-8352

Abstract

Graphical abstract: Highlights: Non-conventional machining processes provide effective alternatives to the conventional ones in dealing with machining titanium alloys. Electrical Discharge Machining (EDM) is the most widely known and used process for the manufacture of engineering components. Modelling EDM processes was accomplished using adaptive neuro-fuzzy systems ANFIS and polynomial modelling approaches. The ANFIS model outperformed in terms of prediction error, residuals range and the correlation coefficient between the experimental and predicted MRR values. Abstract: Due to the controversy associated with modelling Electrical Discharge Machining (EDM) processes based on physical laws; this task is predominantly accomplished using empirical modelling methods. The modelling studies reported in the literature deal predominantly with quantitative parameters i.e. ones with numerical levels. In fact, modelling categorical parameters has been devoted a scant attention. This study reports the results of an EDM experiment conducted on the Ti–6Al–4V alloy. Its aim was to model the relationship between the Material Removal Rate (MRR) and the parameters of the process, namely, current, pulse on-time and pulse off-time along with a categorical factor (electrode material). The modelling process was accomplished using adaptive neuro-fuzzy inference system (ANFIS) and polynomial modelling approaches. In fact, one purpose of this study was to compare the performance of these modelling approaches as no study was found contrasting their prediction capability in the literature. Regarding the polynomial model, two numerical parameters (current and pulse on-time) were declared significant in the ANOVA together with the electrode material and its interaction with pulse on-time. Thus, they were all incorporated in the developed polynomial model. Furthermore, five ANFIS models with 6, 9, 19, 21 and 51 rules were developed utilizing the first order Sugeno fuzzy approach by back-propagation neural networks training algorithm. Of these, the ANFIS model with 21 rules was the best. This model also outperformed the polynomial model remarkably in terms of predicting error, residuals range and the correlation coefficient between the experimental and predicted MRR values. The study sheds light on the powerful learning capability of ANFIS models and its superiority over the conventional polynomial models in terms of modelling complex non-linear machining processes.