Release of elastic strain energy as acoustic emission during the reverse thermoelastic phase transformation in Au-47.5 at .% Cd alloy

Abstract

INTRODUCTIONThe characteristic behavior of the acoustic emission during thermoelastic phase transformations in Au-Cd alloys has previously been reported (1-3). The estimated energy of the acoustic energy was found to be approximately one order of magnitude larger during the phase transformation on heating than on cooling. No attention has been paid, so far, as to the source of the acoustic activity during martensitic phase transformations. However, it is generally accepted that the rapid release of energy within a material generates transient elastic waves of a certain amplitude and frequency content. This acoustic emission is related to the intrinsic mechanism of the martensitic transformation.Martensite formation occurs by a diffusionless shear mechanism involving the cooperative movement of a large number of atoms accompanied by a release of elastic strain energy. The thermoelastic martensite is typically athermal, where the amount of martensite depends on the formation temperature during cooling. Upon heating, the reverse transformation occurs by the backward movement of the martensitic interfaces, thus annihilating the elastic shape strains introduced during the forward transformation on cooling. The almost perfectly reversible mechanism, characterizing the thermoelastic martensitic transformation (4,5) is paradoxically accompanied by a high asymmetric acoustic energy balance (1-3).The objective of this paper is to present experimental evidence concerning the acoustic energy evolved during the heating and cooling phase changes in Au-47.5 at.% Cd polycrystals. The results are examined from the point of view of the stored elastic strain energy during the martensite formation, and the frictional work that is dissipated by the movement of martensite interfaces in either direction, upon heating and cooling (6-8). EXPERIMENTAL PROCEDURE AND RESULTSPolycrystalline specimens of Au-47.5 at.% Cd alloy were heated and cooled through the transformation temperature range, from 10°C up to 120°C and back, at controlled heating and cooling rates. A quartz rod was used as a waveguide, and coupled to a PZT transducer with a resonant frequency in the 150-300 kHz range. The experimental procedure was described elsewhere (3). Filtered signals, preamplified at a constant gain of 40 dB, were fed into an amplitude detector that characterizes the acoustic emission signals according to their peak-amplitude. Amplitude *on leave from the Materials Engineering Department, Ben Gurion University, Beer Sheva, Israel distribution, with 1 dB resolution, was then performed by a Distribution Analyzer gLV1ng a definite voltage for each channel. The amplitude distribution was chart~recorded.The correlation between the peak amplitude of the signals and the energy release at the transducer face was established by letting hardened steel balls of different mass m fall onto the quartz rod and the transducer face from different heights h. The square of the recorded peak voltage and the potential energy (E = mgh) were found to be linearly related aswh...

Challenges and Accomplishments in Mechanical Testing Instrumented by In Situ Techniques: Infrared Thermography, Digital Image Correlation, and Acoustic Emission

“…An AE technique reflecting the rapid local stress relaxation processes in solids has long been acknowledged by materials scientists as a powerful tool enabling the assessment of the dynamic behaviour of defects in real time scale, and the in situ characterisation of the deformation and fracture processes under load [22]. The method has gained popularity due to its extraordinarily high sensitivity to elementary mechanisms of plastic deformation-dislocation slip [23][24][25], mechanical twinning [26][27][28][29][30], temperatureor deformation-induced phase transformations [31][32][33][34][35], plastic instabilities of various kinds [36][37][38][39][40][41][42][43][44], and, of course, cracks of various types. Robust discrimination between different potential sources of AE signals is an everlasting and extremely challenging problem in the AE field.…”