Affiliation:
1. Department of Mechanical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India
2. Department of Mechanical Engineering, The University of Akron, Akron, Ohio, USA
Abstract
The exceptional shape memory properties and load-handling capabilities of shape memory alloys makes them perfect for a spectrum of applications, such as actuators in robotics, control of aircraft wings and biomedical applications. Other than nickel-titanium shape memory alloy there are also various combinations of elements that show the same memory effect, namely Cu-Al-Fe, Cu-Al-Zn, and Cu-Al-Fe-Mn. However, a major problem often associated with shape memory alloys is the technical difficulty associated with machining them. Consequently, they do not tend to last longer when put to use. An optimal selection of input process parameters has a direct influence on the physical properties of these compounds. The traditional machining process, such as drilling, milling and grinding, pose a great threat to their properties. Hence, the research community has focused their study towards unconventional processes, such as (i) Wire Electric Discharge Machining, and (ii) Laser Beam Machining. In this research study, machining of the Cu-Al-Fe shape memory alloy was performed using three unconventional machining processes, namely (i) electric discharge machining (EDM), (ii) laser beam machining (LBM), (iii) powder-mixed electric discharge machining (PMEDM) and the effect of input process parameters were analysed. Various characterization tests provide useful information specific to changes in the microstructure and thermal behaviour of the alloy. The results show that the samples machined with PMEDM, EDM and LBM have surface roughness values of 2.377, 3.120 and 3.432 µm, respectively, which proves that PMEDM is best suited for machining the CuAlFe smart alloy.
Funder
Science and Engineering Research Board