04241nam 2200985z- 450 991055771770332120220111(CKB)5400000000046148(oapen)https://directory.doabooks.org/handle/20.500.12854/76859(oapen)doab76859(EXLCZ)99540000000004614820202201d2021 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierEmpowering Materials Processing and Performance from Data and AIBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20211 online resource (156 p.)3-0365-1899-1 3-0365-1898-3 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 materials and products. Additionally, this gives rise to new paradigms in bridging raw material data and processing to the induced properties and performance. This present topical issue is a compilation of contributions on novel ideas and concepts, addressing several key challenges using data and artificial intelligence, such as:- proposing new techniques for data generation and data mining;- proposing new techniques for visualizing, classifying, modeling, extracting knowledge, explaining and certifying data and data-driven models;- processing data to create data-driven models from scratch when other models are absent, too complex or too poor for making valuable predictions;- processing data to enhance existing physic-based models to improve the quality of the prediction capabilities and, at the same time, to enable data to be smarter; and- processing data to create data-driven enrichment of existing models when physics-based models exhibit limits within a hybrid paradigm.Technology: general issuesbicsscadditive manufacturinganalytical modelartificial neural networksCode2Vectcomputational modelingconstitutive modelingdata drivendata miningdata-drivendata-driven mechanicseffective propertieselasto-visco-plasticityFE-beam modelfeature engineeringfinite element modelGaussian processGaussian process regressionGENERIChardnesshigh-throughput experimentationhyperelasticitylaser shock peeningmachine learningmanifold learningmechanical propertiesmicrocompressionmicrostructuresmodel calibrationmodel correctionmultiscalen/ananoindentationnanoporous metalsneural networksnonlinearnonlinear regressionopen-pore foamsphysics basedplasticityprincipal component analysisresidual stressessensitivity analysissoft living tissuesspherical indentationstatistical analysisstochasticsstructure-property relationshipTDATi-Mn alloystopological data analysisTechnology: general issuesChinesta Franciscoedt720584Cueto ElíasedtKlusemann BenjaminedtChinesta FranciscoothCueto ElíasothKlusemann BenjaminothBOOK9910557717703321Empowering Materials Processing and Performance from Data and AI3035953UNINA