An efficient online time-temperature-dependent creep-fatigue rainflow counting algorithm
Source abstract
Abstract
Abstract Reliability assessment of the materials in real applications in which the materials are exposed to the numerous and complicated time-domain cycles has become one of the most important issues facing all reliability engineers. From offline to online useful lifetime estimations of the material, cycle counting such as Rainflow is paramount of importance. Rainflow algorithm plays the role of transferring complicated time domain cycles to the sorted set of data. This set of data applies to the lifetime model of the material and make the researchers evaluate useful life time of the materials. In this paper, a new online time-temperature-dependent creep-fatigue Rainflow counting algorithm has been proposed. In this new algorithm, online counting of half cycles, time-temperature-dependent mean temperature and creep failure mechanism have been all considered leading to the much more accurate cycle counting and reliability assessment, consequently. This study presents that consideration of the time-temperature-dependent mean temperature and creep failure mechanism can thoroughly impress the reliability assessment in the materials exposed to the temperature cycling. This feature has been validated by employing a set of experimental data of a solder joint, namely the constants of Coffin-Manson-Arrhenius and Monkman-Grant, in a power semiconductor. While the solder joint has been exposed to the specified cycles, the results show that the useful lifetime of the solder joint in the power semiconductor can be estimated with 10% increase in accuracy. The results reveal the importance of the creep failure mechanism and the time-temperature-dependent mean temperature on the reliability assessment.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Probabilistic and Robust Engineering Design · Silicon Carbide Semiconductor Technologies · Electrical Fault Detection and Protection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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