The present research venture focuses on utilization of food and agricultural waste Citrus limetta (musambi) peel by using it as a filler material to develop novel epoxy (EP) composites. Experimental investigation has been performed to analyse the influence of C limetta (musambi) peel particles (CLPP) on mechanical properties (tensile, flexural and impact strength) of the developed composites. Different loadings by weight percentages (0, 5, 10 and 15 wt%) of filler have been used to explain the variation in mechanical properties. Along with effect of different filler percentages, failure characteristics of the prepared samples have also been studied. The results indicate that higher CLPP content in EP matrix has a detrimental effect on mechanical properties of manufactured biocomposites. Thermal characterization was performed to analyse the degradation behaviour and the onset temperature. Morphological analysis of the fractured surfaces was also conducted with the help of a scanning electron microscope. The current experimental investigation has revealed that CLPP can be used as a potential filler for manufacturing biocomposites with applications in general purpose interior fitments in addition to various non-structural components.

This research work presents the study of thermal cycle during friction stir welding of aviation grade aluminum alloy. In addition, mechanical and metallurgical properties of friction stir welded joints are conceptually discussed. Experimentation has been conducted in two stages. Stage I experiments has been conducted as per one-factor-at-a-time approach with varying tilt angle and dwell time. It was concluded that maximum ultimate tensile strength is obtained at 2° tilt angle and 30 s dwell time by one-factor-at-a-time approach. On the basis of stage I results, full-factorial design is used for conducting main experiments by fixing the tilt angle and dwell time. Stage II has been attempted to optimize the most influencing friction stir welding parameters: rotational speed and traverse speed. It is observed that rotational speed is predominant factors for ultimate tensile strength and traverse speed for microhardness. In addition, eight thermocouples (L-shaped k type), four on the advancing side and four on the retreating side, are placed at equal distance from the centerline for measuring the temperature during the process. The optical microscope and energy beam scattered diffraction analysis have been carried out for scrutinizing the macrostructure and microstructure of friction stir welded joints. It is evident from energy beam scattered diffraction analysis that the grain size of nugget zone decreases as compared to base metal.

Purpose: The purpose of this study is to examine the postprocessed wire tool surface using scanning electron microscopy and find out the streamlined conditions of input process variables using multi-objective optimization techniques to get minimum wire wear values.Design/methodology/approach: A federated mode of response surface methodology (RSM) and artificial neural network (ANN) is used to optimize the process variables during the machining of a nickel-based superalloy.Findings: The study explores that with the rise in spark-off time and spark gap voltage, the rate of wire tool consumption also escalates.Originality/value: Most of the researchers used the RSM technique for the optimization of process variables. The RSM generates a second-order regression model during the modeling and optimization of a manufacturing process whose major limitation is to fit the collected data to a second-order polynomial. The leading edge of ANN on the RSM is that it has comprehensive approximation capability, i.e. it can approximate virtually all types of nonlinear functions, including quadratic functions also.

Purpose: In the present study, wire electro-spark machining of Titanium alloy is performed with the machining parameter such as spark-on time, spark-off time, current and servo voltage. The purpose of this study is to model surface roughness using machine learning approach for input/controllable variable. Machined surface examined using scanning electron microscope (SEM) and XRD methods.Design/methodology/approach: Full factorial approach has been used to design the experiments with varying machining parameters into three-level four factors. Obtained surface roughness was modeled using machine learning methods namely Gaussian process regression (GPR) and support vector machine (SVM) methods. These methods were compared for both training and testing data with a coefficient of correlation and root mean square error basis. Machined surface examined using scanned electron microscopy and XRD for surface quality produced and check migration of tool material to workpiece material.Findings: Machine learning algorithms has excellent scope for prediction quality response for the wire electric discharge machining (WEDM) process, resulting in saving of time and cost as it is difficult to find each time experimentally. It has been found that the proposed model with minimum computational time, provides better solution and avoids priority weightage calculation by decision-makers.Originality/value: The proposed modeling provides better predication about surface produced while machining of Ti6Al7Nb using zinc-coated brass wire electrode during WEDM operation.