Development of stress concentration factors for geared shafts
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Journal Title
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Insinööritieteiden korkeakoulu |
Master's thesis
Authors
Date
2024-01-22
Department
Major/Subject
Solid Mechanics
Mcode
Degree programme
Master's Programme in Mechanical Engineering (MEC)
Language
en
Pages
85
Series
Abstract
The presence of geometrical irregularities in shafts, such as shaft shoulders, grooves, and keyways, disrupts the homogeneity of stress distribution, creating areas with stress concentrations. This intricate connection between geometric complexities and stress gradients is crucial as it significantly impacts both the initiation and propagation of cracks in shafts. Consequently, the development of a precise and comprehensive calculation method for stress concentration is always imperative to ensure the integrity of mechanical design. By comprehending and unifying these interconnected factors, engineers can make well-informed design decisions that contribute to the enhanced reliability and durability of shaft-integrated systems. This thesis investigated modern techniques for assessing stress concentrations in shafts and introduced an Artificial Intelligence (AI) based approach to compute the Stress Concentration Factor (SCF) in geared shafts subjected to the combination of bending, axial, torsional, and shear stresses. The proposed calculation method employs Artificial Neural Network (ANN) models trained on stress datasets obtained from Finite Element Analysis (FEA) of industrial gearbox shafts. These models can predict SCFs for both familiar and unfamiliar geometric irregularities within the confines of the training dataset’s limits. Comparative analyses with results from conventional analytical approaches demonstrated that the stress concentration factors obtained through the proposed AI-based method are both valid and reliable. Notably, this method proves its validity for handling combined stresses and exhibits applicability to complex geometric conditions, including keyways with shaft shoulders, setting it apart from the underlined analytical methods.Description
Supervisor
Remes, HeikkiThesis advisor
Musharraf, MashruraKeywords
stress concentration factor (SCF), artificial neural network (ANN), finite element method (FEM), geared shaft, stress analysis, notch effect