Research on the Accelerated Fatigue Experiment Method of the Crankshaft Based on a Modified Particle Filtering Algorithm and the Fatigue Crack Growth Property

Crankshafts are among the most important parts of modern internal combustion engines. Owing to the power transmission demand, sufficiently high strength is usually necessary for the application of the component. In this paper, a new crankshaft bending experimental method was proposed to shorten the corresponding test. A modified particle filtering algorithm approach was proposed for predicting the remaining fatigue life of a crankshaft during bending fatigue experiments. The predicted fatigue life was used to replace the actual experimental results for further analysis if the accuracy requirements were fulfilled; in this way, the experimental duration was obviously shortened. The main conclusion drawn from the research is that, compared with the traditional particle filtering algorithm approach, the modified particle algorithm approach proposed in this paper can more accurately predict the remaining fatigue life of a crankshaft using less experimental data, which makes it possible to circumvent actual bending fatigue experiments of crankshafts in providing theoretical guidance for the design process.

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Publication Details

Journal
Materials
Published
2026-01-25
DOI
https://doi.org/10.3390/ma19030481
Primary Topic
Mechanical Failure Analysis and Simulation
Type
article
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article

Research on the Accelerated Fatigue Experiment Method of the Crankshaft Based on a Modified Particle Filtering Algorithm and the Fatigue Crack Growth Property

Songsong Sun, Shanshan Shen, Jiahong Fu, Xiaolin Gong et al.
Materials
Mechanical Failure Analysis and Simulation
article

Research on the Accelerated Fatigue Experiment Method of the Crankshaft Based on a Modified Particle Filtering Algorithm and the Fatigue Crack Growth Property

Songsong Sun, Shanshan Shen, Jiahong Fu, Xiaolin Gong, Jianmin Juan, Nana Jiang
article en

Abstract

Crankshafts are among the most important parts of modern internal combustion engines. Owing to the power transmission demand, sufficiently high strength is usually necessary for the application of the component. In this paper, a new crankshaft bending experimental method was proposed to shorten the corresponding test. A modified particle filtering algorithm approach was proposed for predicting the remaining fatigue life of a crankshaft during bending fatigue experiments. The predicted fatigue life was used to replace the actual experimental results for further analysis if the accuracy requirements were fulfilled; in this way, the experimental duration was obviously shortened. The main conclusion drawn from the research is that, compared with the traditional particle filtering algorithm approach, the modified particle algorithm approach proposed in this paper can more accurately predict the remaining fatigue life of a crankshaft using less experimental data, which makes it possible to circumvent actual bending fatigue experiments of crankshafts in providing theoretical guidance for the design process.

MaterialsVol. 19(3)
Nanjing Forestry University (CN), Zhejiang Industry Polytechnic College (CN), Zhejiang Medicine (China) (CN), Hangzhou City University
Openalex Percentile: Top 77%
Mechanical Failure Analysis and Simulation
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