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Microstructure pattern in alloy metals is highly dependent on processing of the metal. Slight processing variation of a metal alloy can yield entirely different microstructure patterns. Microstructure has a direct influence on material properties, and therefore it is crucial to know how process affects the material’s properties. In the material Process- Structure-Property (PSP) linkage, this project will focus on the Process-Structure correlation of silver-aluminum-copper ternary eutectic alloy microstructure patterns during phase solidification. A dataset of 2-dimensional microstructures obtained from a phase-field model will be used to correlate different process parameters with intermediate and final microstructures. The three final phases of the ternary eutectic alloy are Al, Al₂Cu, and Ag₂Al. A statistical definition of microstructure will be created using spatial 2-point statistics to quantify the microstructure. Furthermore, a reduced- order of microstructure patterns will be represented using Principles Component Analysis (PCA), which will be used to track the effect of processing parameters on microstructure patterns. Then, process-structure linkages will be built using machine learning techniques to create a model used to predict possible microstructure patterns based on process parameters.