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Surface engineering by additive manufacturing of multi-principal element alloys (MPEAs) has generated significant attention recently for the range of remarkable material properties that can be achieved. A challenge exists in determining the optimum processing parameters for fabricating alloys of various compositions, as they govern the quality of the deposited material. Nevertheless, only limited models are available to predict the initial parameter window for the processing parameters. Using AlCoCrFeNi MPEA as a testbed for laser metal deposition, we present a framework correlating material properties to processing variables coupling predictions from fundamental molecular simulations and meta-heuristic optimization approaches. A set of dimensionless objective functions are constructed to connect elemental diffusion and atomic radii to the macroscopic process parameters, viz., cooling rate, energy density and powder deposition density. Our results suggest that the diffusion coefficient varies exponentially with cooling rate, when the MPEA assumes a crystalline phase upon solidification due to the formation of crystal point defects and a high activation energy rate required for diffusion during rapid cooling. However, the absence of these defects in an amorphous phase of the alloy renders no definitive correlation for the elemental diffusion coefficients with varying cooling rates. Through a multi-objective cuckoo search optimization, we construct a Pareto front to identify optimal values for processing variables, which concur with the parameters adopted in the literature for laser cladding of complex alloys.