Evaluation of fatigue life reliability of steering knuckle using pearson parametric distribution model

Steering module is a part of automotive suspension system which provides a means for an accurate vehicle placement and stability control. Components such as steering knuckle are subjected to fatigue failures due to cyclic loads arising from various driving conditions. This paper intends to give a de...

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Bibliographic Details
Main Authors: Engku Mohamed, Engku Ahmad Azrulhisham, Yusuff, Mohd Asri, Abd Wahab, Dzuraidah, Nik Abdullah, Nik Mohamad Amin, Abdullah, Shahrum, Che Haron, Che Hassan
Format: Article
Language:English
Published: Hindawi Publishing Corporation 2010
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Online Access:http://eprints.utem.edu.my/id/eprint/8026/3/816407.pdf
http://eprints.utem.edu.my/id/eprint/8026/
http://www.hindawi.com/journals/ijqsr/2010/816407/
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Institution: Universiti Teknikal Malaysia Melaka
Language: English
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Summary:Steering module is a part of automotive suspension system which provides a means for an accurate vehicle placement and stability control. Components such as steering knuckle are subjected to fatigue failures due to cyclic loads arising from various driving conditions. This paper intends to give a description of a method used in the fatigue life reliability evaluation of the knuckle used in a passenger car steering system. An accurate representation of Belgian pave service loads in terms of response-time history signal was obtained from accredited test track using road load data acquisition. The acquired service load data was replicated on durability test rig and the SN method was used to estimate the fatigue life. A Pearson system was developed to evaluate the predicted fatigue life reliability by considering the variations in material properties. Considering random loads experiences by the steering knuckle, it is found that shortest life appears to be in the vertical load direction with the lowest fatigue life reliability between 14000–16000 cycles. Taking into account the inconsistency of the material properties, the proposed method is capable of providing the probability of failure of mass-produced parts.