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Thermomechanical fatigue life prediction of cast iron materials for cylinder head based on self-supervision adversarial training algorithm
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Bowen PU1, 2, Xingyue SUN1, Tianguo ZHOU1, Junchao WEI3, Genquan WANG2, 3, Xu CHEN1
Journal of Mechanical Strength | 2025, 47(9) : 241 - 249
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Journal of Mechanical Strength | 2025, 47(9): 241-249
Thermomechanical fatigue life prediction of cast iron materials for cylinder head based on self-supervision adversarial training algorithm
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Bowen PU1, 2, Xingyue SUN1, Tianguo ZHOU1, Junchao WEI3, Genquan WANG2, 3, Xu CHEN1
Affiliations
  • 1.School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
  • 2.National Key Laboratory of Vehicle Power System, China North Engine Research Institute, Tianjin 300400, China
  • 3.Structural Technology Department, China North Engine Research Institute, Tianjin 300400, China
Published: 2025-09-15 doi: 10.16579/j.issn.1001.9669.2025.09.024
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Taking cast iron material of cylinder head as the research object, a series of thermo-mechanical fatigue experiments under different temperature ranges were conducted through bulk sampling. The results show that the fatigue test of cast iron materials exhibits three stages: cyclic softening, cyclic stability and rapid failure. Additionally, the fatigue life of materials under inverse phase loading is significantly shorter than that under positive phase loading. Six typical supervised learning models, including artificial neural networks (ANN) and random forest (RF), were used to predict the fatigue life of the experimental data. However, the results indicate that these models failed to learn the fatigue life distribution trend of the materials. For this problem, the prediction of the thermal mechanical fatigue life of cast iron materials for cylinder heads was achieved by using the self-supervised algorithm based on the generative adversarial network (GAN), and it showed a good prediction effect under the condition of small samples. This research has strong guiding significance and reference value for cylinder head design and fatigue analysis.

Self-supervised algorithm  /  Deep learning  /  Thermo-mechanical fatigue  /  Pulse fatigue  /  Life prediction
Bowen PU, Xingyue SUN, Tianguo ZHOU, Junchao WEI, Genquan WANG, Xu CHEN. Thermomechanical fatigue life prediction of cast iron materials for cylinder head based on self-supervision adversarial training algorithm[J]. Journal of Mechanical Strength, 2025 , 47 (9) : 241 -249 . DOI: 10.16579/j.issn.1001.9669.2025.09.024
Funding
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  • China Postdoctoral Science Foundation-Tianjin Joint Support Program(2024T029TJ)
  • National Natural Science Foundation of China(12302098)
Appendix
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Year 2025 volume 47 Issue 9
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Article Info
doi: 10.16579/j.issn.1001.9669.2025.09.024
  • Receive Date：2024-08-27
  • Online Date：2026-03-20
  • Published：2025-09-15
Article Data
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History
  • Received：2024-08-27
Funding
China Postdoctoral Science Foundation-Tianjin Joint Support Program(2024T029TJ)
National Natural Science Foundation of China(12302098)
Affiliations
    1.School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China
    2.National Key Laboratory of Vehicle Power System, China North Engine Research Institute, Tianjin 300400, China
    3.Structural Technology Department, China North Engine Research Institute, Tianjin 300400, China

Corresponding:

SUN Xingyue, E-mail：
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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