This paper presents the test technique about measurement of electrical resistance changes of thin films during tensile testing. In this work, we used a real-time digital image correlation strain measurement system coupled with micro-tensile testing unit and voltage/current sourcemeter. This system has the advantage of real time displacement monitoring with a resolution of 50 nm during the micro-tensile testing, with the ability to measure the variation in electrical resistance of the specimen at the same time. We obtained the complete testing data for the stress-strain curve and associated electrical resistance-strain curve for 1 and 2 μm-thick freestanding gold films. Young's modulus was about 61~69 GPa and 0.2% offset yield strength was about 361~402 MPa. In case of the electrical resistance, rapid change was observed under the elastic regime, while less obvious under the plastic regime. We also conducted finite element analysis, and this result implied that the electrical resistivity would not be constant during micro-tensile testing.

An artificial neural network (ANN) model was applied to simulate the phase volume fraction oftitanium alloy under isothermal and non-isothermal hot forging condition. For isothermal hot forging process, equilibrium phase volume fraction at specific temperature was predicted. For this purpose, chemical composition of six alloy elements (i.e. AI, Y, Fe, 0, N, and C) and specimen temperature were chosen as input parameter. After that, phase volume fraction under non-isothermal condition was simulated again. Input parameters consist of initial phase volume fraction, equilibrium phase volume fraction at specific temperature, cooling rate, and temperature.The ANN model was coupled with the FE simulation in order to predict the variation of phase volume fraction during non-isothermal forging. Ti-6AI-4Y alloy was forged under isothermal and non-isothermal condition and then, the resulting microstructureswere compared with simulated data.

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