Modeling and surface morphological study in drilling of Al 2024 alloy

Abstract

With concern to profuse usability of drilling operations in various industries, almost 1/3rd of the all the machining operations, the main focus is to obtain a proper quality of hole in less time at optimum cost. Aluminium alloy 2024 (Al 2024) is largely applied in various industries specially, aircraft, automotive, marine etc. because of its outstanding union towards strength, corrosion resistance, and weldability. Cutting time, cutting temperature and surface morphological investigation during drilling is very much necessary for obtaining a quality hole that has not been substantially researched so much. Therefore, a proper and thorough study of these responses is quite important regarding the hole quality. With this view, in this work, drilling of Al 2024 alloy is performed with three process parameters (input) viz. spindle speed (v), feed rate (f) and drill bit diameter (d) and performance parameters (output) as cutting time (s), cutting temperature (°C) and surface roughness (μm). Three different drill bit diameters of high speed steel (HSS) of size viz. 6 mm, 8 mm and 10 mm are used in drilling. Each process parameter has three levels. The surface morphological study is also conducted to know the depth of hole morphology details after drilling. To make the drilling operation environment friendly, it is performed in dry atmosphere. Using full factorial design, total 27 experiments are conducted. The experimental results showed that high spindle speed along with high feed rate decreases the cutting time, whereas it increases the cutting temperature. For better surface finish medium spindle speed along with low feed rate is recommended. From analysis of variance (ANOVA), all the three models developed for cutting time, cutting temperature and surface roughness are found statistically significant. Spindle speed and feed are the influential parameters to cutting time, cutting temperature and surface roughness. A mathematical model is developed using response surface methodology (RSM) to predict the responses cutting time, cutting temperature and surface roughness with better accuracy. The coefficient of determination (R2) for cutting time, cutting temperature and surface roughness is found as 96.65%, 95.88% and 93.65% respectively, which indicated that the developed models could be used for prediction with better accuracy. The morphological study shows that scratches, trenches, abrasive wear are visible on the hole surface. At high spindle speed surface grain refinement is observed more which causes a better surface finish.