Acta Scientific Medical Sciences (ASMS)(ISSN: 2582-0931)

Research Article Volume 7 Issue 1

Probabilistic Shaft Design Using Corrective Factors Methodology Versus Binary Synthesis Methodology

Manuel Baro-Tijerina*

Docent at the Industrial and Technology Department, Tecnológico Nacional de México Campus Nuevo Casas Grandes, Cd. Nuevo Casas Grandes, Chih., México

*Corresponding Author: Manuel Baro-Tijerina, Docent at the Industrial and Technology Department, Tecnológico Nacional de México Campus Nuevo Casas Grandes, Cd. Nuevo Casas Grandes, Chih., México.

Received: November 24, 2022; Published: December 28, 2022


This article compares the traditional static and dynamic (fatigue) methodologies for the mechanical design of a shaft used in the speed reducer of an industrial fan against the probabilistic design methodology based on the binary synthesis method. To make the comparison, the case of the design of the diameter of the intermediate shaft of a speed reducer used between a motor and a fan used to dry grains is analyzed. The main objective is to show the advantage that the probabilistic approach offers over traditional axis design approaches. The effectiveness of the results of the methodologies is validated with the torsional rigidity allowed for the design of shafts. The diameter obtained by the static methodology and the one obtained by the fatigue methodology is smaller than the diameter obtained by the torsional stiffness, on the contrary, the diameter obtained by the binary synthesis probabilistic methodology is greater than the diameter of the torsional stiffness.

Keywords: Probabilistic Shaft Design; Von Mises Theory; Soderberg Theory; Binary Synthesis; Torsional Stiffness


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Citation: Manuel Baro-Tijerina. “Probabilistic Shaft Design Using Corrective Factors Methodology Versus Binary Synthesis Methodology”.Acta Scientific Medical Sciences 7.1 (2023): 151-161.


Copyright: © 2022 Manuel Baro-Tijerina. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


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