Dynamic structural analysis of connecting rod through adaptive mesh refinement for different materials
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Abstract
In this research dynamic structural behavior of connecting rod made of three different materials has been investigated to choose best suitable and light weighted material for manufacturing of connecting rod without affecting its strength. So in this aspect dynamic structural behavior of connecting rod is analyzed through Finite Element Methods (FEM) by applying two different meshing schemes known as conventional and Adaptive Mesh Refinement (AMR) method. Simulation has been conducted by using ANSYS workbench module. Simulation results have been validated against experimental from literature. From the results of Dynamic structural analysis of connecting rod it is revealed that on the compressive loading maximum stresses occurred in the shank near small end and at its big end. Whereas in case of tensile loading maximum stresses occurred at the mid of inside surface near the smaller end. On the other hand maximum deformation occurs at big end of connecting rod for both compressive and tensile loads. Dynamic structural behavior comparison for connecting rod made of three different materials has also been carried out and it is observed from obtained results that Aluminum alloy experienced highest deformation followed by chromium steel alloy 410. Whereas lowest deformation was found in forged steel material. While comparison of stresses distribution, deformation and weight analysis results for connecting rod made from three different materials, it is concluded that chromium steel alloy 410 connecting rod is lighter in weight as compare to forged steel connecting rod and shown enough strength to sustain the maximum load as compare to aluminum alloy connecting rod. Therefore it is attributed that chromium steel alloy 410 would be better option than the aluminum alloy and forged steel for manufacturing of connecting rod. Furthermore, by comparing the meshing schemes, it is concluded that AMR method provide good results with less computational resources and within less CPU time.