Fatigue life prediction of bending polymer films using random forest

[Display omitted] •Fatigue life of bending polymer films under cyclic bending was measured with various test conditions.•The bending fatigue life of crystalline and amorphous polymer films was successfully predicted by a single machine learning model (random forest).•Important factors in the fatigue...

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Bibliographic Details
Published in:International journal of fatigue 2023-01, Vol.166, p.107230, Article 107230
Main Authors: Kishino, Masayuki, Matsumoto, Kohsuke, Kobayashi, Yoshiaki, Taguchi, Ryo, Akamatsu, Norihisa, Shishido, Atsushi
Format: Article
Language:English
Subjects:
Cyclic bending
Fatigue life
Machine learning
Polymer film
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Summary:[Display omitted] •Fatigue life of bending polymer films under cyclic bending was measured with various test conditions.•The bending fatigue life of crystalline and amorphous polymer films was successfully predicted by a single machine learning model (random forest).•Important factors in the fatigue life of bending polymer films were estimated from the calculation of feature importance. The prediction of the fatigue life of polymer film substrates under cyclic bending plays an important role in designing durable flexible devices. Here, the fatigue life of bending polymer film was predicted using machine learning; the required data were collected via fatigue tests under different test conditions. Machine-learning models (linear regression and random forest regression) were constructed using these collected data. The random forest model predicted the fatigue life with a mean absolute percentage error of 22.3% within 1 min. Such accurate and efficient fatigue life predictions can contribute toward the development of flexible devices.
ISSN:0142-1123
1879-3452
DOI:10.1016/j.ijfatigue.2022.107230