Experimental investigation,prediction and optimization of cylindricity and perpendicularity during drilling of WCB material using grey relational analysis

Manufacturing is always the heart of majority of industries. Drilling is an extremely important and an essential machining process which requires a lot of attention as in most of the cases it is required for assembly purposes. Majority of the holes produced during drilling are made with the help of Vertical Machining Centre (VMC) meant for pin- hole assembly. Though the tolerance is within limit, assembly problems arise due to the improper geometry of these holes. Various geometrical tolerances like cylindricity, circularity, perpendicularity and position errors are responsible for such assembly problems. This investigation is focussed on cylindricity and perpendicularity in the drilling of Wrought Cast Steel Grade B (WCB) material using SOMX 050204 DT insert. In this work, effect of machining variables like cutting speed, feed rate and depth of cut (canned cycle) are investigated and optimized using grey relational analysis (GRA). Reliable experiments are conducted based on a 3³ full factorial, replicated twice. Second order regression models are developed for predicting cylindricity and perpendicularity. The models’ adequacy has been checked by calculating correlation coefficient. It shows that the developed models are well fitted for the prediction of responses within the specific range of input variables.     

Intelligent Energy Utilization Analysis Using IUA-SMD Model Based Optimization Technique for Smart Metering Data

Smart metering has gained considerable attention as a research focus due to its reliability and energy-efficient nature compared to traditional electromechanical metering systems. Existing methods primarily focus on data management,rather than emphasizing efficiency. Accurate prediction of electricity consumption is crucial for enabling intelligent grid operations,including resource planning and demandsupply balancing. Smart metering solutions offer users the benefits of effectively interpreting...     

Experimental investigation on mechanical behaviour,modelling and optimization of wear parameters of B4C and graphite reinforced aluminium hybrid composites

Aluminium alloy (AA) 6061 and 7075 were reinforced with 10 wt.% of boron carbide (B₄C) and 5 wt.% of graphite through liquid casting technique. The Scanning Electron Microscope (SEM) and Energy Dispersive Spectrum (EDS) were used for the characterization of composites. The wear experiment was carried out by using a pin-on-disc apparatus with various input parameters like applied load (10, 20, and 30 N), sliding speed (0.6, 0.8, and 1.0 m/s) and sliding distance (1000, 1500, and 2000 m). Response Surface Methodology (RSM) using MINITAB 14 software was used to analyse the wear rate of hybrid composites and aluminium alloys. The worn surfaces of hybrid composites and base alloys were studied through SEM and EDS systems and some useful conclusions were made.