Abstract
Metals and coating materials, with best material properties are highly desired in the field of engineering to control the stresses and strains, that play important role in design strategies. The crankshaft, an important part of tank engine, depends on the cranking mechanism in the internal combustion engine. The function of the crankshaft is to convert the sliding motion of the piston into rotary motion. Since most industrial mechanisms and processes make more efficient use of rotary motions rather than displacements, the function of crankshaft becomes very vital. To understand the reason behind the fatigue and heavy load cycles, a mathematical model is simulated. The complex geometry of crankshaft is developed in a numerical solver. The model is simulated relative to several material properties to mimic the resulting fatigue. The numerical data was optimized using the python Bayesian optimization tools, to forecast the threshold values relative to longer duration. In the recent literature, different nano-composites are acquiring at- tention as the coating materials, to tackle with the friction load. The hybrid modeling approach used during this research can help to simulate the stresses, resulting from dif- ferent nanocomposite coatings.