Investigation on Wire Electro Discharge Machining Characteristics of TiNiCu Shape Memory Alloys

Abstract

Shape memory alloys are well known across academia and industries due to their unique functional capabilities, such as shape memory effect and superelasticity besides other useful properties. They are also known for their toughness, resistance to corrosion, improved fatigue life and damping capabilities. Shape memory effect is exhibited by these group of alloys due to reverse martensitic phase transformation which transforms de-twinned martensites back to twinned martensites. This phase transformation of shape memory alloys occurs without any change in state of the material, which contextually known as diffusionless transformation. Superelasticity, on the other hand is exhibited by these alloys, when the alloy is handled at an operating temperature higher than its austenitic temperature. Ni rich NiTi shape memory alloy for example can be processed to be superelastic at room temperature. These incredible qualities qualify shape memory alloys as potential materials for smart applications such as sensors and actuators. A vast majority of these alloys exhibit shape memory effect due to thermal load and some of them are also influenced by a magnetic field. Thermally induced shape memory alloys have formed wide applicability due to ease of use and economic factor. Among these alloys, TiNi based shape memory alloys are most widely researched and put into applications compared to Cu-based or Fe-based alloys. Phase transformation temperature of TiNi based shape memory alloys lie within a nominal operating temperature range (60⁰C-100⁰C) which makes them more suitable for sensing and actuating applications. However, with addition of a ternary element, phase transformation temperature of these alloys can be tailored to specific needs. Addition of Cu as ternary element in TiNi binary alloy system was found to reduce its phase transformation temperature and narrow transformation hysteresis. Cu addition also facilitates thermal conductivity making it more sensitive to change in thermal flux. Therefore, TiNiCu ternary shape memory alloys could be used for much sensitive applications. Major challenge these alloys impose is poor machinability with conventional machining techniques. High tool wear, poor machined surface quality and additional post-machining processes compromise finish quality, accuracy of the end product and increase the cost involved. This is where non-conventional machining techniques proved as an added advantage to process these functional alloys and soon became a more popular choice over conventional machining techniques. Non-conventional machining process like laser beammachining (LBM), water jet machining (WJM), electrochemical machining (ECM) and electrodischarge machining (EDM) result to better machining characteristics compared to conventional machining techniques. due to non-contact nature of the tool-workpiece interface. However, thick recast layer, oxidation, burr formation are some of machining defects that non-conventional machining techniques exhibit. Wire electrodischarge machining (WEDM) is a variant of traditional electrodischarge machine (EDM) where machining is carried out using an wire electrode. Sparking between wire electrode and workpiece results in removal of workpiece material through local melting. Advantage of WEDM over EDM is that through CNC any desired profile can be cut imposing minimum damage to workpiece material. Sensors and actuators incorporating shape memory effect are generally micro shaped components which undergoes microscopic shape change. Major aim of this study is to investigate WEDM characteristics of various homologous TiNiCu shape memory alloys and to optimize machining responses so as to produce components without compromising accuracy and quality. Six different TiNiCu shape memory alloys were vacuum melted and characterized in terms of microstructure, phases present, phase transformation temperatures and microhardness. Optical microscope with image analyzer, X-ray diffractrometer, differential scanning calorimeter and microhardness tester were used to perform aforementioned characterization. Further, to determine the quality of machining, the following output responses namely material removal rate (MRR), surface roughness (SR), kerf width (KW), recast layer thickness (RLT), machined surface microhardness (MH) and machined surface morphology were studied and reported. Ti50Ni25Cu25 exhibited least thermal hysteresis (~6⁰C) which indicates its suitability as ideal material for sensor and actuator applications. Due to varying thermal conductivity of vacuum melted homologous TiNiCu shape memory alloys, variation in WEDM responses were observed. Thereafter, prediction of WEDM responses was carried out using Artificial Neural Network (ANN) and optimization of WEDM responses was performed using Genetic Algorithm (GA). After a thorough investigation, WEDM process parameters to machine homologous TiNiCu shape memory alloys were reported and discussed in detail.