In order to show the effect of UCT on the TWSME stability, an ECAE processed sample was trained under the same conditions, except this time using a lower UCT of 240 °C. All the subsequent thermomechanical characterization tests including 10 stress-free thermal cycles (Section 8.2), isobaric cooling-heating test under compressive stresses (Section 8.3) and 10 thermal cycles under 50 MPa compressive stress (Section 8.4) were also carried out using this lower UCT value. The results of these tests are illustrated in Figure 8.5.
(a) (b)
(c) (d)
Figure 8.5 Repeating of the thermomechanical characterization tests for the ECAE processed sample at a lower UCT of 240°C. (a) 100-cycle thermomechanical training under 150 MPa, (b) 10 stress-free thermal cycles, (c) isobaric cooling-heat test under various compressive stresses and (d) 10 thermal cycles under 50 MPa compressive stress.
A 40 °C decrease in the UCT barely affected the evolution of shape memory characteristics during training (Figure 8.5a). First cycle irr remained unchanged at 0.10
%, while the total irrecoverable strain decreased from 0.65 % to 0.58 %. The change in other shape memory properties, such as T, rec and transformation temperatures
followed a similar trend as described in Section 8.1 with slightly different values. T stayed constant at 25 °C, while increased from 171 °C to 181 °C. rec of the first cycle was found to be 2.13 % and increased to 2.23 % at the end of 100 cycles.
The functional stability during stress-free thermal cycling was also marginally affected by the lower UCT choice. Cold-shape strain decreased by 0.13 %, while hot-shape strain decreased by 0.06 %, resulting in a TWSM strain degradation from 1.76 % to 1.66 % in 10 cycles. Nevertheless, the stability of the TWSME work output was significantly increased when a lower UCT was used during thermal cycling. The amount of opposing stress required to fully suppress the TWSME increased, leading to larger overall work output levels (Figure 8.5c). In addition, the degradation of the TWSME during thermal cycling under a constant stress level significantly decreased (Figure 8.5d).
8.6 Summary and Conclusions
In this chapter, Ti50.5Ni24.5Pd25 HTSMA was subjected to a training procedure consisting of 100 thermal cycles under different stress levels (i.e., 80, 150, and 200 MPa). The resulting TWSME was characterized in terms of its stability during both stress-free and load-biased thermal cycling. The effect of ECAE on the stability of the TWSME was also studied. Major findings and conclusions that can be drawn from this study are as follows:
1. TWSME could resist moderate opposing stresses, resulting in a maximum external work of 0.12 J/g after training under 200 MPa. This level of work output
was significantly higher than that attributed to a TWSME developed in conventional TiNi and Cu-based SMAs. A maximum work output for the TWSME was achieved at an opposing stress of approximately 50 MPa, regardless of the training stress used to develop the TWSME. Additional increases in stress beyond this peak resulted in a substantial degradation in the TWSM strain and consequently work output of the TWSME. In the as-received material an opposing stress of greater than 75MPa was necessary to suppress the TWSME.
2. The effect of ECAE prior to training on the magnitude and stability of the TWSME was also studied. A stable TWSME with small degradations in cold and hot shape-strains upon stress-free thermal cycling was obtained, but due to the nature of the training and the already induced defect structure in the ECAE processed-material, the magnitude of the resulting transformation strain and work output generated by the TWSME effect was far less than that developed in the as-received material. Another reason for this might be the larger overheating above the temperature used during the thermal cycling of the ECAE processed material and easier relaxation of the internal stresses due to the heavily deformed microstructure.
3. The stability of TWSME during stress-free thermal cycling was not reflected to the stability under stressed conditions. For both as-received and ECAE processed materials, the cold-shape strains decreased considerably upon repeated thermal
cycling under a constant opposing stress, leading to large degradations in TWSM strains.
4. Similar to the conclusion made in the previous chapter, UCT was demonstrated to have a major role on the outcome of the TWSME stability during both stress-free and load-biased thermal cycling. A higher UCT value was shown to result in faster and larger degradation of the TWSME in the ECAE processed material.
CHAPTER IX
INFLUENCE OF MICROSTRUCTURE ON THE EVOLUTION OF SHAPE MEMORY BEHAVIOR DURING THERMOMECHANICAL TRAINING
The previous two chapters investigated the stability and work output of the TWSME in trained SMAs. The evolution of shape memory behavior during thermomechanical training is equally important as the characterization of the trained material. Most SMAs display a similar evolution of shape memory behavior during training. Transformation temperatures, hot and cold-shape strains change considerably in the early cycles, the amount of change decreasing with number of cycles. Eventually, an almost stable behavior is obtained upon which further cycling results in only minor changes in transformation temperatures and hot and cold-shape strains. Since conventional training procedures are long and costly, it is desired that the trained SMAs reach stability in as few cycles as possible.
The objective of this chapter is to explain the role of microstructural parameters, with a special focus on the crystallographic compatibility between transforming phases, on the response of SMA to training. The influence of training parameters such as applied stress or UCT has to some extent been investigated in previous chapters and is also covered in the literature. With an understanding of the role of the microstructural parameters on evolutionary load-bias behavior, the material behavior during training can easily be predicted. This will greatly help in choosing the right SMA composition for a
specific application and choosing the right training parameters to obtain stability in as few cycles as possible.