Empowering Materials Processing and Performance from Data and AI

• Eará dahkkit: Chinesta, Francisco (Doaimmaheaddji), Cueto, Elías (Doaimmaheaddji), Klusemann, Benjamin (Doaimmaheaddji)
• Materiálatiipa: Elektrovnnalaš Girjji oassi
• Giella: eaŋgalasgiella
• Almmustuhtton: Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021
• Fáttát: Technology: general issues; plasticity; machine learning; constitutive modeling; manifold learning; topological data analysis; GENERIC; soft living tissues; hyperelasticity; computational modeling; data-driven mechanics; TDA; Code2Vect; nonlinear regression; effective properties; microstructures; model calibration; sensitivity analysis; elasto-visco-plasticity; Gaussian process; high-throughput experimentation; additive manufacturing; Ti-Mn alloys; spherical indentation; statistical analysis; Gaussian process regression; nanoporous metals; open-pore foams; FE-beam model; data mining; mechanical properties; hardness; principal component analysis; structure-property relationship; microcompression; nanoindentation; analytical model; finite element model; artificial neural networks; model correction; feature engineering; physics based; data driven; laser shock peening; residual stresses; data-driven; multiscale; nonlinear; stochastics; neural networks; n/a
• Liŋkkat: DOAB: download the publication; DOAB: description of the publication

Third millennium engineering address new challenges in materials sciences and engineering. In particular, the advances in materials engineering combined with the advances in data acquisition, processing and mining as well as artificial intelligence allow for new ways of thinking in designing new mat...