EDM is a non-contact machining process widely used for shaping electro-conductive materials regardless of their hardness. In EDM material removal takes place by a series of recurring electrical sparks between the tool electrode and workpiece. In this study the effect of variation of discharge current on various machining parameters including Metal removal rate (MRR), Tool removal rate (TRR) and Surface roughness has been considered. A total of 32 experiments were conducted on four different workpieces i.e. Die Steel-D3, En-8, En-19 and Stainless steel (SS-AISI-440C) with the help of Copper and Aluminium electrodes. In this study Die-Sinking EDM has been employed and the results are shown with the help of graphs.

The present study involves the application of a water quality model QUAL2K for developing the BOD-DO model and evaluation of the results for a 50 km stretch of river Ghataprabha near Mudhol town of Bagalkot district, Karnataka. QUAL2K is a modeling framework for simulating river and stream water quality. Arc-GIS technique is used to obtain some hydro-geometric data of the river for input to model QUAL2K. For calibration and validation of the model, the BOD and DO values were monitored at six different locations. The calibrated model was validated to predict water quality using a different set of data under different conditions. The performance of the model was evaluated using statistics based on Standard errors (SE), Normalized Mean Errors (NME) and Mean Multiplicative errors (MME). The SE and MME values for BOD and DO during calibration are, 1.41 (1.12) and1.28 (0.90), respectively. The values in the bracket show MME. Corresponding values for the validation are 1.27 (1.09) and 1.10 (0.96). These results show that the values predicted by the model are in close agreement with measured values.

The present day systems are increasing in complexity in terms of both the size and functionality. Also society demands these systems to be ultra-reliable. Reliability evaluation and optimization techniques play a major role in these regards. However reliability evaluation & optimization techniques do not give any idea about maintenance, risk involved and related cost incurred and criticality of system components or subsystems. Important measures (IM) exist in literature that identify the weak components i.e critical components and give ranking to them. Recently some work has appeared on Cost Importance Measure (CIM). There are number of mistakes/short comings in the paper Cost-related importance measure by Ming et.al. Definition of CIM given in general and the same used for computation of CIM of component xi have appeared differently (Different definitions for CIM). PD(xi),Partial derivative of component xi obtained for most of the components are either inexact or are faulty in expression and computations are wrong. All other mistakes also have not only been pointed but have been corrected also. A New CIM (NCIM) proposed, which highlights the above issues and have done desired calculations. The new CIM which has been advanced is computationally simpler and yields the desired ranking of components.