Wire electrical discharge machining (WEDM) is a specialized thermal machining process capable of accurately machining parts of hard materials with complex shapes. Parts having sharp edges that pose difficulties to be machined by the main stream machining processes can be easily machined by WEDM process. Technology of the WEDM process is based on the conventional EDM sparking phenomenon utilizing the widely accepted non-contact technique of material removal with a difference that spark is generated at wire and work piece gap. Since the introduction of the process, WEDM has evolved as a simple means of making tools and dies to the best alternative of producing micro-scale parts with the highest degree of dimensional accuracy and surface finish. This paper outlines the development of a model and its application to optimize WEDM machining parameters. Experiments are conducted to test the model and satisfactory results are obtained. The methodology described here is expected to be highly beneficial to manufacturing industries, and also other areas such as aerospace, automobile and tool making industries

The effect of addition of silicon carbide (SiC) filler in different weight percentages on physical properties, mechanical properties, and thermal properties of chopped glass fiber-reinforced epoxy composites has been investigated. Physical and mechanical properties, i.e., hardness, tensile strength, flexural strength, interlaminar shear strength, and impact strength, are determined with the change in filler content to notice the behavior of composite material subjected to loading. Thermo-mechanical properties of the material are measured with the help of a dynamic mechanical analyzer. The result shows that the physical and mechanical properties of SiC-filled glass fiber-reinforced epoxy composites are better than unfilled glass fiber-reinforced epoxy composites. Viscoelastic analysis for different compositions indicate that adding too much SiC content results in degradation in energy absorption capacity of the material and hence overall performance of the composites, whereas adding too much (more than 10 wt.%) SiC content increases the elastic behavior of the composite.

Solid particle erosion of polymer composites is a complex surface damage process, strongly affected by material properties and operational conditions. To avoid repeated experimentation, it is important to develop predictive equations to assess material loss due to erosion wear under any impact conditions. This paper presents the development of a mathematical model for estimating erosion damage caused by solid particle impact on flyash filled glass fiber reinforced polyester composites. The model is based upon conservation of particle kinetic energy and relates the erosion rate with composite properties and test conditions. Another correlation derived from the results of Taguchi experimental design is proposed as a predictive equation for erosion wear of these fiber reinforced composites. Further, considering the complexity and high degree of nonlinearity in the erosion process, an artificial neural networks (ANN) technique is implemented as an effective tool for prediction of wear response of these composites in a larger space. Finally, the results of a mathematical model and the ANN model are compared with those obtained from experimentation.