[SY-F8] Simultaneous Transformation Kinetics Model for Additive Manufacturing
Metal additive manufacturing is disrupting the traditional approaches in the manufacturing sector. However, there are uncertainties associated with the qualification of fabricated components owed to the difficulty in predicting the process-structure relationship in the PSPP linkage. Understanding the process-structure linkage includes understanding the (a) liquid-solid phase transformation and (b) solid-solid phase transformation. Previously solidified layers are affected by the thermal cycles during the melting of the subsequent layers. During processing in AM, the conditions are highly non-isothermal, non-equilibrium and understanding this highly transient condition is crucial to understand the precipitation kinetics. The temperature of the substrate can also be varied in the machine. This affects the solid-state phase transformation of a given alloy system. Multiple phases can simultaneously precipitate from the product phase as a function of thermal cycle. In this work, we develop a model based on simultaneous transformation kinetics theory and the developed phenomenological model is then coupled with the numerical thermal model to predict the solid-state phase transformation and volume fraction of multiple phases as a function of thermal cycles and processing conditions. Numerical thermal model is used to predict the thermal cycles as a function of input processing conditions. The thermal cycle is then discretized into set of small isothermal steps. The extent of simultaneous precipitation/dissolution of the phases will be calculated at each of the discretized time step. Volume fraction of each of the phases is then updated and similar procedure is repeated for rest of the discretized isothermal time steps. Appropriate thermodynamic framework, database will be used to calculate the thermodynamic properties as a function of alloy system and is used as the input to the model. It is a semi-empirical model and the input from experimental results are used to calibrate the model. Some of the other calibration parameters include nucleation site densities and surface energies. Advantages and limitations of this approach will be discussed in detail.