Comparative evaluation and sensitivity analysis of multi-modelling and optimization of milling Ti–6Al–4V alloy with high-pressure coolant jets

An effective cooling method with the proper selection of process parameters can intensify the machining performance by reducing the loss of resources with better quality products. In this regard, modelling is an appropriate way of predicting responses in changing environment and optimization is an efficient tool of selecting the best process parameters based on the specific desire. With a view to enhance the machinability of Ti–6Al–4V alloy, the first attempt of the current study was to predict the performance characteristics of milling such as cutting force (N), specific cutting energy (J/mm3) and surface roughness (μm) with the variation of speed (m/min), feed (mm/min), depth of cut (mm) and cooling approach by developing mathematical models. For the present work, three different predictive models such as response surface methodology (RSM), artificial neural network (ANN), and adaptive neuro fuzzy inference system (ANFIS) was followed. Additionally, a comparative assessment of the used predictive models was carried out and ANFIS was noticed as the most accurate predictive model. After that, optimization of the selected responses was conducted by multiple-objective optimization on the basis of ratio analysis (MOORA) method where the relative weights of each response were defined by principal component analysis (PCA). For milling Ti–6Al–4V alloy within the specific boundary conditions, PCA-MOORA suggested an optimal parameter setting at 32 m/min speed, 22 mm/min feed rate, and 0.75 mm depth of cut with rotary high-pressure cooling. Finally, the sensitivity of the used MOORA method with the variation of unitary ratio was checked out to take a robust decision.