ABSTRACT A crankshaft operates under complex and variable cyclic loading conditions, experiencing significant torsional stresses arising from the inertia of rotating masses, along with bending stresses induced by gas pressures within IC engines, leading to catastrophic failure. Finite Element Analysis (FEA) provides a computational approach to simulate and prediction of fatigue life of components with intricate geometries under realistic and complex loading scenarios. In this study, a FEFA of the crankshaft is performed under fully reversed cyclic loading conditions to assess its fatigue performance at each loading cycle. The crankshaft geometry is modeled using CATIA V5, while the fatigue simulation and stress‐life evaluation are conducted using ANSYS Workbench. The influence of design parameters, the crankpin oilway on stress concentration and fatigue performance was systematically investigated. Realistic loading conditions were simulated by applying a 47,713 N bending load and a 517 Nm torsional moment at the crankpin to replicate engine operating conditions. The study also, investigated the fatigue behavior of the three materials (QT 7000, AISI 4130, and Ti‐6Al‐4V) crankshaft materials subjected to maximum bending and twisting loads. The analysis shows that the presence of oilways significantly influenced the stress concentration and fatigue life up to 1.0 × 10⁹ cycles. Among the materials, Ti‐6Al‐4V exhibited higher fatigue safety performance (3.76 and 3.64 for with and without crankpin oilways) due to its high strength‐to‐weight ratio, compared to AISI4130 and QT 7000. The alloy demonstrated a significantly 120% to 130% and 170% to 180% higher fatigue safety factor than AISI 4130 and QT 7000. This study provides valuable insights into optimizing crankshaft design and material selection for improved durability and performance in internal combustion engines.
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P Rawat
Anant Prakash Agrawal
Shahazad Ali
Applied Research
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Rawat et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69be37406e48c4981c676ae7 — DOI: https://doi.org/10.1002/appl.70088