s. k. bhat

There have recently been several initiatives to develop eco-sustainable materials because of the growing concern over ecological contamination caused by the overuse of synthetic materials. Previous studies in literature have explored the development of rice straw waste-based epoxy composites. Another waste hazard of concern in the current times is that of waste tire rubber. The present study investigates the physico-mechanical properties of a unique hybrid composite consisting of recycled waste tire rubber and rice straw reinforced composite, which has not been investigated in the literature. Their density, water absorption, hardness, tensile and flexural strengths were examined with variations in the proportion of rubber particles and rice husk. Increase in the rubber content resulted in proportional rise in the water uptake, hardness, tensile strength, and flexural strength. The composite with 25 wt.% of rubber and 5 wt.% of rubber showed the highest tensile strength and strain of 12.5 MPa and 0.015, respectively. The composite with 15% RS and WTR showed a 14.26% increase in flexural strength, with the neat composite exhibiting the highest strength. The composite material can be used as structural panels for instruments or devices in low load bearing applications. The developed sustainable material with waste generated from used tires and rice husk can aid in decreasing the harmful effects caused on the environment and human health during their disposal.

Cold Atmospheric Pressure Plasma (CAP) is very potent and impactful technology implemented for both technological and biomedical applications. This paper focuses on the implementation of artificial neural network (ANN) for a novel double ring electrode based cold atmospheric pressure plasma which is to operated only in the glow discharge region for its application in biomedical field. ANN inherently helps in visualizing the effective output parameters such as peak discharge current, power consumed, jet length (with sleeve) and jet length (without sleeve) for given set of input parameters of supply voltage and supply frequency using machine learning model. The capability of the ANN model is demonstrated by predicting the output parameters of the CAP beyond the experimental range. Finally, the optimized settings of supply voltage and supply frequency will be determined using the composite desirability function approach to simultaneously maximize the peak discharge current, jet length (with sleeve) and jet length (without sleeve), and minimize the power consumption.

Soft robotics using Pneumatic Network actuators (Pneu-Net) is a developing field that has a promising future for variety of applications involving delicate operations such as biomedical assistance. The interaction between geometry and the performance of the actuator is an important topic which has been studied by many researchers in this field. However, there is a lack of investigation on the relationship between gripping capability and geometrical parameters of soft actuators. Especially, there is a need to shed more light on the effects of wall thicknesses on the gripping force developed. In the present study, a semi-cylindrical chambered PneuNet soft actuator is numerically investigated to evaluate the effects of pressure and wall thickness variations on its performance characteristics. The results revealed that increasing the restraining layer thickness (RLT) aids the bending capability of the actuator whereas increasing the chamber wall thickness reduces it. Therefore, maximum bending of the actuator is achieved at the combinations of minimum wall thickness and maximum RLT. At these geometrical configurations of maximum bending, the deformation-pressure relationships followed a sigmoidal function and tended towards linearity with increasing wall thickness and decreasing RLT. The gripping force showed an exponential increase with increasing working pressures and wall thicknesses. The maximum gripping force increased cubically with increasing wall thicknesses at their respective maximum working pressures, which was modeled using a polynomial regression model (R2=99.79%).

Wire-Electric Discharge (WED) Machining is one of the most suitable machining techniques for machining hard-to-cut materials such as Titanium, with precision. It is of utmost importance to optimize the control parameters to achieve the desired levels of machining performance characteristics. Considering this goal, this research investigates the effects of current, pulse on time (Ton) and pulse off time (Toff) on the material removal rate, surface roughness and kerf width of WED machined Ti-6Al-4V. The results of optimization showed that, current – 5.19 A, Ton – 20 µs, Toff – 30 µs, is the optimized settings for machining of Ti-6Al-4V alloy using molybdenum electrode for the best machining performance. Based on the analysis of grey relational grades, the order of influence of the control parameter is ranked as: Ton – I, Toff – II and Current – III. The efficacy of GRA based approach was evaluated through confirmation experiments wherein the theoretical predictions showed errors < 3%.