Materials machine learning (ML) is revolutionizing various areas in a fast speed, aiming to
efficiently design novel materials with superior performance. Here we reviewed the recent …

Neural network (NN) models have significantly impacted fatigue‐related engineering
communities and are expected to increase rapidly due to the recent advancements in …

The defects created in metallurgical and manufacturing processes generally play a decisive
role in very high cycle fatigue life of engineering structures. By taking stress level, defect size …

An increment learning scheme based on fully-connected neural network is proposed to
predict the fatigue–crack growth in middle tension, M (T), specimens of 7B04 T6 aluminum …

The 2xxx series aerospace aluminum alloys, vital for aircraft structures due to their excellent
mechanical strength and corrosion resistance, face challenges with corrosion fatigue failure …

There was modeled the stress-strain diagram of 6061-T651 aluminum alloy by machine
learning methods. In this study, methods of k-nearest neighbors and random forest were …

Materials selection for aluminum alloys with desired fatigue properties and other mechanical
properties is very difficult. Usually, when fatigue properties are maximized, other mechanical …

The thermal conductivity coefficient of epoxy composites for aircraft, which are reinforced
with glass fiber and filled with aerosil, γ-aminopropylaerosil, aluminum oxide, chromium …

INTRODUCTION luminum-silicon alloys have been extensively utilized in internal
combustion (IC) engines as a substitute for cast iron and steel components to decrease the …

Various input variables, including corrosion time, fretting force, stress, lubrication, heat-
treating, and nano-particles, were evaluated by modeling of stress-controlled fatigue …