Researchers are exploring the use of machine learning models to predict various properties of additively manufactured materials, including hardness, tensile strength, and wear behavior. The models are being applied to a range of materials, such as acrylonitrile butadiene styrene, aluminum alloys, and high entropy alloys. Different approaches, including neural networks and surrogate modeling, are being investigated to improve the accuracy of these predictions.