This research work focuses on the impact of graphite addition to the Al/SiC hybrid metal matrix composites manufactured by stir casting process. Three specimens were prepared with Al6061 alloy + 5 wt. % SiC and varying weight percent of graphite. Al6061 matrix material is suitable for machining and welding applications commonly used in construction of aircraft wings and fuselages. Machining of composite materials is often required to meet dimensional tolerances, surface roughness and to create holes. A layered SiC/Gr composite material is used in gas turbine combustor can. Machining of Al/SiC/Gr hybrid composite was done in Esteem ETM 356 lathe using tungsten carbide insert. Optimization of three machining parameters cutting speed, feed rate and depth of cut, was done using four levels. From ANOVA results feed rate (35.83%) is the important factor influencing surface roughness. Grey Relational analysis was done for simultaneous improvement of Material Removal Rate and surface roughness.
Misfire detection in an internal combustion engine is an important activity. Any undetected misfire can lead to loss of fuel and power in the automobile. As the fuel cost is more, one cannot afford to waste money because of the misfire. Even if one is ready to spend more money on fuel, the power of the engine comes down; thereby, the vehicle performance falls drastically because of the misfire in IC engines. Hence, researchers paid a lot of attention to detect the misfire in IC engines and rectify it. Drawbacks of conventional diagnostic techniques include the requirement of high level of human intelligence and professional expertise in the field, which made the researchers look for intelligent and automatic diagnostic tools. There are many techniques suggested by researchers to detect the misfire in IC engines. This paper proposes the use of transfer learning technology to detect the misfire in the IC engine. First, the vibration signals were collected from the engine head and plots are made which will work as input to the deep learning algorithms. The deep learning algorithms have the capability to learn from the plots of vibration signals and classify the state of the misfire in the IC engines. In the present work, the pretrained networks such as AlexNet, VGG-16, GoogLeNet, and ResNet-50 are employed to identify the misfire state of the engine. In the pretrained networks, the effect of hyperparameters such as back size, solver, learning rate, and train-test split ratio was studied and the best performing network was suggested for misfire detection.
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