Citation: Wang, T.; Zhang, H.; Duan,
Y.; Wang, M.; Qin, D. Research on
Fatigue Life Prediction Method of
Key Component of Turning
Mechanism Based on Improved TCD.
Metals 2022, 12, 506. https://
doi.org/10.3390/met12030506
Academic Editors:
Alberto Campagnolo and
Giovanni Meneghetti
Received: 12 January 2022
Accepted: 14 March 2022
Published: 16 March 2022
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Article
Research on Fatigue Life Prediction Method of Key Component
of Turning Mechanism Based on Improved TCD
Tingting Wang, Han Zhang, Yuechen Duan , Mengjian Wang and Dongchen Qin *
School of Mechanical and Power Engineering, Zhengzhou University, Zhengzhou 450000, China;
wangtingting@zzu.edu.cn (T.W.); zhanghan2019901@163.com (H.Z.); duanyc1984@zzu.edu.cn (Y.D.);
wangmengjian@gs.zzu.edu.cn (M.W.)
* Correspondence: dcqin@zzu.edu.cn; Tel.: +86-2386-8575
Abstract:
The main objective of this paper is to accurately obtain fatigue life prediction for the key
components of a turning mechanism using the improved theory of critical distances (TCD). The
irregularly shaped rotating arm is the central stressed part of the turning mechanism, which contains
notches. It has been found that TCD achieves good results in predicting the fatigue strength or fatigue
life of notched components with regular shape but is less commonly used for notched components
with irregular shape. Therefore, TCD was improved and applied broadly to predict the fatigue life
of an irregularly shaped rotating arm. Firstly, the notch depth and structure net width parameters
were introduced into the low-order and low-accuracy classical TCD function to obtain a novel stress
function with high computational efficiency and high accuracy, whereas the stress concentration
factor was introduced to modify the length of critical distance. Secondly, the improved TCD was
used to predict the fatigue strength of notched components with regular shape, and its accuracy was
demonstrated by a fatigue experiment. Finally, the improved TCD was applied to predict the fatigue
life of an irregularly shaped rotating arm. The deviation between prediction results and experimental
results is less than 18%. The results demonstrate that the improved TCD can be applied effectively
and accurately to predict the fatigue life of key components of turning mechanisms.
Keywords:
rotating arm; fatigue life prediction; theory of critical distances; stress function; critical
distance length; fatigue experiment
1. Introduction
As the kernel component of garbage compression trucks, the primary function of
the turning mechanism is to lift and frequently turn over trash cans to dump trash into
the carriage. This mechanism is subjected to alternating tensile loads, resulting in fatigue
damage, which affects the regular use of the garbage compression truck. Therefore, it is
crucial to accurately predict the fatigue life of key components of the turning mechanism to
ensure the reliability of garbage compression trucks.
Fatigue life analysis of the turning mechanism of garbage compression trucks is often
carried out using the nominal stress method [
1
–
5
]. The method is simple to apply but leads
to inaccurate prediction because it does not take into account the influence of the stress field
around the notch. Considering the above problems, in the 1950s, Neuber and Peterson [
6
,
7
]
proposed the theory of critical distances. The theory suggests that the fatigue failure of
notched components is not only determined by the maximum stress at the notch but is
also affected by the stress field near the notch. Based on this theory, many scholars have
predicted the fatigue performance of notched components under different conditions and
have achieved good results [
8
–
13
]. In the application of conventional TCD, an accurate
description of the stress field around the notch is essential for predicting the results. The
stress field around the notch was first calculated using the complex variable function. Still,
it was later found that it was difficult to obtain an analytical solution using this method,
Metals 2022, 12, 506. https://doi.org/10.3390/met12030506 https://www.mdpi.com/journal/metals
