Shape memory alloys

1. Presented by: Shreyash Pandey
Indian Institute of Engineering Science
and Technology, Shibpur

2. Contents
 Introduction
 Shape Memory Effect and Super Elasticity
 How does it work?
 Transformation Temperatures
 Shape Memory Effect
 Our past Modeling achievements
 Key points of MBS model
 Progress of our recent projects
 Differential Scanning Calorimetry
 DSC in Shape Memory Alloys
 Analysis of the experimental data

3. Introduction
:
1 A class of Smart Materials
2 Discovered in 1932 by Arne Olander
3
4
Exhibit two Unique Properties
 Shape memory effect
 Super Elasticity
Ni-Ti is the most important SMA also
known as NiTiNol.

4. Shape Memory Effect and Super Elasticity

5. How Does it work?
 The reason behind this is
solid state phase
transformation from
martensite( Monoclinic) to
austenite(perfectly cubic)

6. Transformation Temperatures

7. Shape Memory Effect

8. Our Past modeling achievements

9. Key Points of Modified Boltzmann Sigmoidal Model
 Simpler than the pre-existing
Limiting Loop Proximity method
 An ultimate curve-fitting accuracy
of 98% was obtained.

10. Differential Scanning Calorimetry
 It is a thermoanalytical Technique in which
the difference in the amount of heat
required to increase the temperature of a
sample is measured as a function of
Temperature

11. DSC in Shape Memory Alloys
 With the DSC unit that we have in our lab, the data for Ni-Ti alloy has been
generated for 40 cycles.
• Computational Modelling of the generated data is to be done.
• After the modelling, we need to validate our model by conducting experiments at
different conditions.
• The model will be able to predict the transformation behaviour of Ni-Ti based
Shape Memory Alloys at distinct values of parameters involved.

12. Analysis of the experimental data

13. Analysis of the experimental data