Abstract Analysis and design, as the most critical components in material science, require a
highly rigorous approach to assure long-term success. Due to a recent increase in the …

The use of artificial neural network models to enrich the analytical and predictive capabilities
of decision support systems in manufacturing has increased. The growing complexity and …

Industry 4.0 promotes the transformation of manufacturing industry to intelligence, which
demands advances in materials, devices, and systems of the construction industry …

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 …

Ultrathin niobium strips with different thicknesses are prepared by an accumulative rolling
process. The tensile test of the ultrathin niobium strips is carried out, and the microstructure …

Increased demands for high-performance materials have led to advanced composite
materials with complex hierarchical designs. However, designing a tailored material …

Abstract Design and process optimization are key aspects of manufacturing engineering.
This contribution details a machine learning (ML) methodology capable of learning from …

Industrial big data technology has become one of the important driving forces to intelligent
manufacturing in the steel industry. In this study, the characteristics of data in steel …

The accurate prediction of biodiesel fuel properties and determination of its optimal fatty acid
(FA) profiles is a non-trivial process. To this aim, machine learning (ML) based predictive …

One of the fundamental challenges in material science and engineering is the design of
multiple performance materials by considering various microstructural features and their …