Study on fatigue life prediction of thermal barrier coatings for high-power engine pistons. (August 2022)

• Record Type: Journal Article
• Authors: Tan, L.G.; Li, G.L.; Tao, C.; Feng, P.F.
• Is Part Of: Engineering failure analysis. Volume 138(2022)
• Publication Date: 2022-08
• Subjects: TBCs -- Engine piston -- Thermal shock test -- Microcrack growth behavior -- Fatigue life prediction; System failures (Engineering) -- Periodicals; Fracture mechanics -- Periodicals; Reliability (Engineering) -- Periodicals; Pannes -- Périodiques; Rupture, Mécanique de la -- Périodiques; Fiabilité -- Périodiques; Fracture mechanics; Reliability (Engineering); System failures (Engineering); Periodicals; Electronic journals; 620.112
• DOI: 10.1016/j.engfailanal.2022.106335
• Languages: English
• ISSNs: 1350-6307

Abstract

Highlights: The failure mechanism of TBCs samples was analyzed based on SEM. A modified model is developed to evaluate the fatigue life of TBC pistons in high-power engines. The piston TBCs thermal shock test rig was built and life tests were conducted. Piston TBCs lifetime good correlation was achieved between the prediction model and the actual experimental results. The research result can reduce the probability of engine accidents caused by coating damage, guide the process design of piston TBCs, and improve the reliability of high-power engines. Abstract: Thermal barrier coatings (TBCs) have been widely used in high-power engine components because of their excellent thermal insulation properties. In this study, microcrack growth and fatigue life analysis of TBCs were carried out based on thermal shock tests of TBCs samples. Scanning electron microscopy (SEM) was used to observe the spallation and vertical crack growth behavior of TBCs during the tests. The fatigue life prediction model applied to piston TBCs was developed by modifying the Paris equation with the sintering of the coating and the effect of a thermally grown oxide (TGO) layer being considered. The model was calibrated by conducting tests on piston TBCs, and the forecasting error was less than 15%, indicating that the prediction model is both practical and effective. The results of this study provide a service life evaluation and test method for TBCs on pistons, which can be utilized to guide piston TBCs design.