In this review article, the latest applications of machine learning (ML) in additive manufacturing (AM) field are reviewed. These applications, such as parameter optimization and anomaly detection, are classified into different types of ML tasks, including regression, classification, and clustering. The performance of various ML algorithms in these types of AM tasks are compared and evaluated. Finally, several future research directions are suggested.

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Thermal properties of La2Zr2O7 double-layer thermal barrier coatings La2Zr2O7 is a promising thermal barrier coating (TBC) material. In this work La2Zr2O7 and 8YSZ layered TBC systems were fabricated. Thermal properties such as thermal conductivity and coefficient of thermal expansion were investigated. Furnace heat treatment and jet engine thermal shock (JETS) tests were also conducted. The thermal conductivities of porous La2Zr2O7 single layer coatings are 0.50~0.66 W/m/ o C at the temperature range from 100 to 900 o C, which are 30~40% lower than the 8YSZ coatings. The coefficients of thermal expansion of La2Zr2O7 coatings are about 9~10×10-6 o C-1 at the temperature range from 200 o C to 1200 o C, which are close to those of 8YSZ at low temperature range and about 10% lower than 8YSZ at high temperature range. Double layer porous 8YSZ plus La2Zr2O7 coatings show a better performance in thermal cycling experiments. It is likely because porous 8YSZ serves as a buffer layer to release stress.