Experimental and Predictive Study on WEDM of Aluminium Alloys Using Taguchi and ANFIS

The attributes of aluminium alloys are acknowledged in the varied usage of these alloys because they have excellent mechanical properties and good ductility while being less vulnerable to fatigue. These alloys are widely applied in engineering disciplines. The aerospace and automotive industries are important industries using these alloys, and many other progressive engineering applications continue to embrace the use of these alloys. Their resistance to corrosion makes them even better suited for usage in some harsh environments. Unfortunately, producing parts with very complicated geometries through conventional machining techniques is still a challenge. To try to solve this limitation, there have been several advanced machining processes. Among them is Wire Electrical Discharge Machining (WEDM) - a specific form of Electrical Discharge Machining (EDM) for an effective solution for complex and hard-to-machine components. In this study, efforts have been made to study the WEDM process using Taguchi's design of experiments for machining aluminium alloys. The study analyses the important process parameters affecting machining results. The performance measures evaluated include material removal rate (MRR), surface roughness (SR), and dimensional accuracy errors. ANOVA was used to model the statistical significance of each process factor. In addition, a hybrid Grey-based Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed to predict the output of the multi-performance index. Results of the analysis confirmed that the proposed model provides accurate and trustworthy predictions and can immensely help manufacturers in forecasting and even optimizing improved performance outputs.