The demand for fuel is increasing, and the availability of fossil fuel reserves is limited. The amount of concern arising from the emission problems causing the environment and ecosystem are increasing exponentially. It requires the industry to find the optimum solution. Biodiesel can be stored and used as petroleum diesel. It can be used in blended or pure forms without any modification in the engine. Use of bio-diesel has shown a remarkable reduction of toxic emissions and noise and emissions. This research deals with the use of Jatropha oil as biodiesel to improve the emission characteristics; at the same time, the performance characteristics need to be improved. The diesel engine is optimized with different blends of Jatroha oil as biodiesel, compression ratio, and load using L27 orthogonal array of full factorial design of experiment. The emission parameters, such as HC, CO, and CO2 are measured. The performance parameters viz brake power, brake thermal efficiency, specific fuel consumption, and volumetric efficiency are calculated. The entropy method determines the weight. Optimization is performed using multi-criteria decision-making technique with the TOPSIS method.The results show that blend B10 and a compression ratio of 15 found to be the optimum setting for diesel engine using biodiesel blends to optimize the performance.
Stainless steels are widely used to manufacture mechanical components due to excellent mechanical properties. Machining is considered as one of the most critical manufacturing processes in mechanical industry to produce desired shapes and dimensional accuracy of the components. It also affects the performance of the components in its functional requirement. This paper deals with the optimization of cutting parameters in machining operation for AISI 316 austenitic steel with dry and wet environment conditions. The chosen machining parameters in this research are cutting speed, feed rate, and depth of cut as input variables, whereas the response factors are surface roughness and wear rate. Taguchi method with the L9 orthogonal array was used to analyze the process parameters based in dry and wet machining conditions. The Taguchi approach provides the best setting with lower values of surface roughness and wear rate. The regression analysis is performed to obtain a mathematical model of responses in terms of the process parameter. The composite regression optimization gives best setting for dry condition (cutting speed 173 rpm, feed 0.25 mm/rev, and 0.87 mm of the depth of cut) and for wet condition (cutting speed 173 rpm, feed 0.3 mm/rev, and 0.57 mm of the depth of cut). The results show that surface roughness and wear rate are lower in the wet environment than the dry environment.
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