How to Train Shape Memory Alloys

Glenn researchers have optimized how shape memory alloys (SMAs) are trained by reconceptualizing the entire stabilization process. Whereas prior techniques stabilize SMAs during thermal cycling, under conditions of fixed stress (known as the isobaric response), what Glenn's innovators have done instead is to use mechanical cycling under conditions of fixed temperature (the isothermal response) to achieve stabilization rapidly and efficiently. This novel method uses the isobaric response to establish the stabilization point under conditions identical to those that will be used during service. Once the stabilization point is known, a set of isothermal mechanical cycling experiments is then performed using different levels of applied stress. Each of these mechanical cycling experiments is left to run until the strain response has stabilized. When the stress levels required to achieve stabilization under isothermal conditions are known, they can be used to train the material in a fraction of the time that would be required to train the material using only thermal cycling. As the strain state has been achieved isothermally, the material can be switched back under isobaric conditions, and will remain stabilized during service. In short, Glenn's method of training can be completed in a matter of minutes rather than in days or even weeks, and so SMAs become much more practical to use in a wide range of applications. Shape memory alloys (SMAs), sometimes known as "smart metals," provide a lightweight, solid-state alternative to conventional actuators and switches, such as hydraulic, pneumatic, or motor-based systems. To function properly, SMAs must be "trained" to return to a previous form when heated, and innovators at NASA's Glenn Research Center have developed a remarkable new method of completing this training at a fraction of the time and cost of conventional training techniques. Glenn's technique uses mechanical cycling, rather than more complicated and time-consuming thermal cycling, to train SMAs before implementation. In addition, this new approach to training allows SMAs to be applied to complex geometric components, so that they may be used in a broader number of applications.