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.

摘要

工程领域非常需要具有最佳材料性能的金属和涂层材料来控制应力和应变，这在设计策略中起着重要作用。曲轴是坦克发动机的重要组成部分，它依赖于内燃机中的曲轴机构。曲轴的作用是将活塞的滑动运动转化为旋转运动。由于大多数工业机械和过程更有效地利用旋转运动而不是位移，因此曲轴的功能变得非常重要。为了理解疲劳和重载循环背后的原因，模拟了一个数学模型。曲轴的复杂几何形状是在数值求解器中开发的。该模型是相对于几种材料特性进行模拟的，以模拟产生的疲劳。使用 python 贝叶斯优化工具优化数值数据，以预测相对于较长持续时间的阈值。在最近的文献中，不同的纳米复合材料作为涂层材料受到关注，以应对摩擦载荷。本研究中使用的混合建模方法有助于模拟不同纳米复合涂层产生的应力。