Metal matrix composite Al2O3 particle reinforced Al have become useful engineering materials due to their properties such as low cost, wear-resistant, heat-resistant and low weight. The present study focused on prediction of temperature produced during machining of Al2O3 reinforced with Al7075. In this investigation the percentage of Al2O3 (mesh size of 100-300)was varied 1%, 3%, 5%, 7% and 9% to the base material of Al7075. The temperature was measured using thermal gun at machining tip of the tool at which maximum temperature were measured by varying operational parameters such as depth of cut (0.25, 0.5, 0.75, 1 and 1.25mm), spindle speed (80, 112, 140, 200 and 355rpm) and feed rate (0.10, 0.12, 0.16, 0.2 and 0.25mm/sec). Experimental results revels that the temperature increases with increase in feed rate and depth, whereas, in case of spindle speed there is a fluctuation in temperature for all the combinations considered. The percentage contribution of operational parameters on temperature was determined using ANOVA analysis. Overall analyses for all the combination considered shows that feed rate (45.07%), depth (33.75%) and spindle speed (5.2%) on temperature. The developed models with a P-value are less than 0.05 were considered to be a statistically significant with 95% of confidence interval. A good agreement between experimental and statistical modeling were achieved and comparison of experimental and statistical analysis were drawn.

In India, a densely populated country, fossil fuel depletion affects the energy sector that fulfils the industrial and human needs. Concerning greenhouse gas emissions and pollutants, and sustainability, there is a great demand to search for alternate feedstocks to produce alternate fuels at a low cost. The present work focuses on waste coconut and fish oil as potential inexpensive feedstock for biodiesel production. Two-stage transesterification processes for biodiesel production from hybrid oils mixed in a 1:1 volume ratio by employing solid nano-catalyst Magnesium Oxide (MgO). Response surface methodology (RSM) was used to analyze the effects of the physics of transesterification variables, such as methanol-to-oil molar ratio (M:O), MgO catalyst concentration (MgO CC), and reaction temperature (RT), on biodiesel yield, based on experimental data gathered in accordance with the matrices of central composite design (CCD). MgO CC showed the highest contribution, followed by M:O and RT, to maximize biodiesel yield. All interaction factors showed a significant effect except the M:O with RT. Grasshopper optimization algorithm (GOA) determined optimal conditions (M:O: 10.65; MgO CC: 1.977 wt.%; RT: 80 °C) based on empirical equations, resulting in maximum biodiesel yield conversion experimentally equal to 96.8%. The physical stability of the MgO nano-catalyst and reactivity up to 5 successive cycles can yield 91.5% biodiesel yield, demonstrating its reusability for sustainable biodiesel production at low cost. The optimized biodiesel yield showed better physicochemical properties (tested according to ASTM D6751-15C) to use practically in diesel engines.

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