RESUMEN
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.
RESUMEN
An effective cooling mode with appropriate process parameters can enhance the machinability as well as productivity. In this regard, this study focuses on evaluating the machinability of widely used Ti-6Al-4V alloy with the use of high-pressure coolant jets compared to dry milling. A novel rotary applicator was designed and developed to feed high-pressure coolant jets without any drastic change of solid end mill cutter. Four flutes solid HSS end mill cutter was selected for machining because of its some intensive properties. Biodegradable VG-68 cutting oil was chosen as cutting fluid due to its better thermo-physical properties with higher flash points. Machinability was assessed at 16-32 m/min cutting speeds and feed rates of 0.08-0.16 mm/tooth with a constant depth of cut of 1.0 mm, taking into account the average cutting temperature, resultant cutting force, mean surface roughness, and tool wear. Dry milling produced the worst results for all of the investigated responses, with excessive tool wear due to the lack of cooling and lubrication. Compared to dry milling, high-pressure cooling (HPC) lowered average cutting temperature, resultant cutting force, and mean surface roughness by 11.21-21.57%, 8.63-13.12%, and 6.09-29.6%, respectively, whereas rotary high-pressure cooling (RHPC) reduced these parameters by 15.39-27.27%, 14.05-21.18%, 16.48-41.04%. RHPC's efficient cooling and lubrication increased the machinability of the Ti-6Al-4V alloy. Dry milling showed severe flank wear with increased built-up edge (BUE) development, abrasion, and adhesion, whereas HPC and RHPC dramatically reduced the severity of tool wear. In HPC and RHPC, the tool life was consequently increased by 6.8 and 9 min, respectively.