LONG-TERM RELIABILITY OF SHAPE MEMORY WIRES: ANALYSIS OF THE INFLUENCE OF DIFFERENT ACTIVATION PROFILES ON THE DEGRADATION BEHAVIOR AND LIFETIME

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作者
Hess, Philipp [1 ]
Schmelter, Tobias [2 ]
Kuhlenkoetter, Bernd [2 ]
Brackett, Stefan [1 ]
机构
[1] Univ Wuppertal, Chair Reliabil Engn & Risk Analyt, Wuppertal, Germany
[2] Ruhr Univ Bochum, Chair Prod Syst, Bochum, Germany
来源
PROCEEDINGS OF ASME 2024 CONFERENCE ON SMART MATERIALS, ADAPTIVE STRUCTURES AND INTELLIGENT SYSTEMS, SMASIS 2024 | 2024年
关键词
Shape memory alloy; shape memory actuator; smart material actuator; fatigue tests; degradation behavior; degradation analysis; data analysis; reliability analysis; long-term reliability; energy model; pulse shapes;
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摘要
Shape memory alloys offer a good opportunity to replace conventional actuators, as they have a high working capacity with low space requirements and weight, a low number of (moving) components, and work quietly. However, their commercial use is not yet so widespread because the fatigue and degradation behavior of shape memory alloys is highly complex, depends on many influencing factors, and has not yet been fully investigated. This paper presents and analyzes a comprehensive series of experiments (based on a statistical design of experiments) to determine the influences of different electrical activation durations on lifetime and functional properties. In all tests, the same amount of electrical energy is applied to the shape memory elements (by a rectangular profile), but within different activation durations. The focus of the subsequent data evaluation is on the influence of the different energy input on the activation behavior (achieved elongation, activation speed etc.) and on the lifetime. As a result, high energization in a short duration overheats the shape memory elements (rapid failure), long energization with lower current intensity results in a longer lifetime with a lower elongation, as higher losses (e.g. due to convection) occur. Differences and correlations between the individual tests (the measurement data) are analyzed using statistical methods (significance test, distribution models etc.). In this way, this article contributes to increasing the long-term reliability and quality of shape memory actuators and to the analysis of long-term degradation behavior.
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