Results 151 to 160 of about 44,636 (300)
Microstructure Evolution of a VMnFeCoNi High‐Entropy Alloy After Synthesis, Swaging, and Annealing
Advanced Engineering Materials, EarlyView.The synthesis and processing (rotary swaging and annealing) of the novel VMnFeCoNi alloy is investigated, alongside the estimation of the grain size effect on hardness. Analysis of a wide grain size range of recrystallized microstructures (12–210 µm) reveals a low annealing twin density.
Aditya Srinivasan Tirunilai +6 more
wiley +1 more source
Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning
18031814One of the key obstacles in traditional deep learning is the reduction in model transparency caused by increasingly intricate model functions, which can lead to problems such as overfitting and excessive confidence in predictions. With the advent
Wendlinger, Maximilian +2 more
core +1 more source
Advanced Engineering Materials, EarlyView.The temperature dependence of fatigue behavior in nickel‐based superalloys is investigated through high‐resolution measurements of plastic localization. While increasing temperature reduces localization and enhances fatigue performance in René 88DT, Inconel 718 exhibits a sharp degradation at intermediate temperature due to intensified slip ...
M. Calvat +5 more
wiley +1 more source
Influence of Test Temperature and Test Frequency on Fatigue Life of Aluminum Alloy EN AW‐2618A
Advanced Engineering Materials, EarlyView.The influence of test temperature and test frequency on the fatigue life of EN AW‐2618A is investigated. High‐cycle fatigue tests are performed at different test temperatures and frequencies on the 1000 h/230°C overaged state. Both test parameters reduce fatigue life due to time‐dependent damage mechanisms.
Ying Han +5 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.Do not let thermal drift and instrument artifacts deceive high‐temperature nanoindentation results. We compare classical Oliver–Pharr and automatic image recognition analyses across steels and a Ni alloy to quantify these effects. Accounting for artifacts reveals systematic softening with temperature, while Cr and Ni additions boost resistance ...
Velislava Yonkova +2 more
wiley +1 more source
Structural and multibody dynamics - Uncertainty quantification and model reduction
, 2014 Ces travaux de recherche s'intéressent de manière générale à la quantification des incertitudes et à la réduction de modèle pour la modélisation numérique des systèmes dynamiques. Dans une première partie, on s'intéresse à la quantification des incertitudes pour les systèmes multicorps.
openaire +1 more source
Optimization of the Production of Rubber Compounds Using Mathematical Models
Advanced Engineering Materials, EarlyView.Rubber compounds were mixed in a batch internal mixer, and symbolic regression was used to derive mathematical models linking recipe and process parameters to ram path, torque, and mixing quality (incorporation, dispersion, distribution). Subsequent optimization with evolutionary algorithms identified operating conditions that reduce specific energy ...
Anke Bardehle +7 more
wiley +1 more source
Statistical Methodologies for Decision-Making and Uncertainty Reduction in Machine Learning
While advances in machine learning and the expansion of massive datasets have significantly improved predictive accuracy, the translation of these predictions into actionable decisions—alongside a robust understanding of associated risks—remains ...
Zhang, Haofeng
core +1 more source
Advanced Engineering Materials, EarlyView.Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari +5 more
wiley +1 more source