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Nickel titanium (NiTi) Shape memory alloys (SMAs) are finding relevance in many engineering and biomedical applications the last few decades. NiTi alloys are replacing most of the standalone titanium alloys used in orthopedic applications such as bone plate, artificial bone implants, mandible fracture plates etc. Machining of these SMAs through traditional machining process do not yield desirable results, and this study therefore attempts to study the wire electric discharge machining (wire EDM) characteristics of NiTi medical grade alloy. Full factorial design (FFD) of four input process parameters along with three levels was used for planning the experiments and the interaction effects of different input process parameters such as pulse time (Ton), pause time (Toff), wire feed (WF) and servo voltage (SV) on the output responses such as surface roughness (SR), material removal rate (MRR) and tool wear rate (TWR) were studied through developing response surface methodology (RSM) based second order quadratic mathematical models. Scanning electron microscope (SEM) analysis was carried out to better understanding the nature of machining and surface morphology of the machined samples during wire EDM of medical grade NiTi alloy. Differential evolution (DE) optimization was developed to understand the optimal combination of parameters to obtain required SR, MRR and minimal TWR. The optimal SR was observed for the combination of higher SV, middle Ton along with lower Toff and WF. The optimal MRR was attained at middle value of SV and Ton, along with lower values of Toff and WF. The optimal TWR was observed for the combination of middle value of Ton, higher value of Toff and lower values of SV and WF. [ABSTRACT FROM AUTHOR]

In the current investigation, the dry sliding wear characteristics of cenosphere/epoxy composites have been studied under ambient conditions. The sliding wear analysis has been performed on wear rate, specific wear rate and coefficient of friction considering four parameters, namely, sliding velocity, normal load, sliding distance and % filler content. The wear trials were conducted as per full factorial design (FFD) and wear behavior was analyzed through modeling based on artificial neural network (ANN). Proposed models were further employed with differential evolution (DE) optimization to determine the best combination parameters like sliding velocity, normal load and sliding distance for a specified reinforcement, which results in minimal wear. [ABSTRACT FROM AUTHOR]