Classification of Microstructures of Al–Si Casting Alloy in Different Cooling Rates with Machine Learning Technique

Machine Learning-Assisted Design of Yttria-Stabilized Zirconia Thermal Barrier Coatings with High Bonding Strength

“…Machine learning is the core of artificial intelligence with the ability to reorganize the existing knowledge structure and figure out implicit relationships, and it has been applied in many areas such as medical treatment, finance, materials, and chemistry with significant progress. − In materials design, machine learning has occupied an important part in the development and design of alloys, polymers, perovskites, and other materials by virtue of the advantages of obtaining performance and trends from available data without knowing the underlying physical mechanism. − Yang et al used machine learning combined with high-throughput screening and pattern recognition back-projection technology to break the upper limit of the hardness of the existing high-entropy alloys and designed the hardness of Co 18 Cr 7 Fe 35 Ni 5 V 35 to be 1148 HV, which is 24.8% higher than the hardness of the alloy with the highest hardness in the original data set. Chen et al used a step-by-step screening method of the packaging algorithm to screen out a subset of features for ridge regression, XGBoost, and support vector regression (SVR) models and integrated the three models to design low-melting-point alloys.…”