The history of machine learning (ML) can be traced back to the 1950 s, and its application in
alloy design has recently begun to flourish and expand rapidly. The driving force behind this …

Abstract Machine learning is now applied in virtually every sphere of life for data analysis
and interpretation. The main strengths of the method lie in the relative ease of the …

A neural network model is developed to search vast compositional space of high entropy
alloys (HEAs). The model predicts the mechanical properties of HEAs better than several …

Rapid chemical functionalization of additives and efficient determination of their optimum
concentrations are important for designing high-performance lubricants, especially under …

Data-mining based machine learning (ML) method is emerging as a strategy to predict
aluminium (Al) alloy properties with the promise of less intensive experimental work …

Considering the complex relationship among mechanical properties of 7xxx aluminum alloy,
it is very crucial to optimize two or more target properties simultaneously in developing new …

This present work deals with designing a new lubricant with multiple friction modifiers (FM)
having better tribological properties using artificial neural network (ANN) and genetic …

This study reports on the modeling and multi objective optimization of age hardening
process parameters using artificial neural network (ANN) and adaptive neuro-fuzzy …

Biodegradable lubricant based on the blend of various vegetable oils with different nano
friction modifier in combination is designed using computational intelligence technique and …

This encyclopedia, written by authoritative experts under the guidance of an international
panel of key researchers from academia, national laboratories, and industry, is a …