Results 131 to 140 of about 71,408 (254)

Continuous Stiffness Graded Metal–Ceramic Femoral Stems: UMAT‐Based Design and Finite Element Assessment

Advanced Engineering Materials, EarlyView.
Functionally graded metal–ceramic femoral stems are engineered through continuous UMAT‐based stiffness tailoring, eliminating discrete material interfaces while enhancing biomechanical compatibility. Low‐index power‐law gradation optimizes load transfer, reduces stress shielding, and controls implant–bone micromotion, highlighting a materials‐design ...
Rihem Nouira, Sameh Elleuch, Hanen Jrad
wiley   +1 more source

Bayesian-optimized GRU network modeling for thermal error compensation in gantry guideway grinder spindle. [PDF]

Sci Rep
Zhu S, Deng J, Chen H, Nan P, Ye Y, Shi Q.   +5 more
europepmc   +1 more source

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

Advanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source

A Cohesive-Zone-Modified Point-Stress Criterion for Notched Polymer Composites: Derivation, Calibration, and Validation. [PDF]

Polymers (Basel)
Abdellah MY, Irfan OM, Omar HM.
europepmc   +1 more source

Extrusion‐Based Additive Manufacturing of Advanced Ceramics: Strengthening Mechanisms and Process Optimization Review

Advanced Engineering Materials, EarlyView.
This review comprehensively evaluates extrusion‐based additive manufacturing for advanced ceramics, detailing feedstock options and key process parameters. By critically addressing defect mechanisms like porosity and cracking, the work highlights optimization strategies through machine learning and advanced postprocessing.
Meisam Bakhtiari, Mohammad Hadi Sheikh‐Ansari, Avinash Borgaonkar, Mehran Dadkhah, Abdollah Saboori   +4 more
wiley   +1 more source

Prediction of Hole Expansion Ratio in Advanced High-Strength Steels Using Physics-Informed Machine Learning. [PDF]

Materials (Basel)
Tiwari S, Dash K, Heo S, Park N, Subba Reddy NG.   +4 more
europepmc   +1 more source

Semantic Modeling in Materials Science and Engineering With Platform MaterialDigital Core Ontology 3.0

Advanced Engineering Materials, EarlyView.
The community‐driven Platform MaterialDigital Core Ontology (PMDco) 3.0 is introduced as a Basic Formal Ontology‐aligned semantic backbone for the processing–structure–properties paradigm in Materials Science and Engineering. Modular engineering, automated releases, and validation workflows are highlighted and key semantic patterns for materials ...
Markus Schilling, Philipp von Hartrott, Jörg Waitelonis, Thomas Hanke, Henk Birkholz, Hossein Beygi Nasrabadi, Khashayar Razghandi, Kamilla Zaripova, Felix Thonagel, Fabian Neuhaus, Martin Glauer, Lars Vogt, Harald Sack, Lutz Mädler, Bernd Bayerlein, Chris Eberl   +15 more
wiley   +1 more source

Accelerating the Measurement of Fatigue Crack Growth with Incremental Information-Based Machine Learning Approach. [PDF]

Materials (Basel)
Wen C, Lu H, Wang Y, Wang M, Tian Y, Wu D, Diao Y, Hu J, Zhang Z.   +8 more
europepmc   +1 more source

On the Effect of Creep on Local Lattice Orientation in Single‐Crystal Ni‐Based Superalloy Microstructures

Advanced Engineering Materials, EarlyView.
During creep of a single‐crystal Ni‐based superalloy, the overall crystal orientation is observed to remain constant while the microstructure evolves. Despite the lack of macroscopic rotation, small (<1°) rotations are observed on the submicron size scale and are accommodated by counteracting rotations over the scale of several micrometers.
E. J. Payton, E. B. Dursun, G. Eggeler, L. Agudo Jácome   +3 more
wiley   +1 more source

Foundational Machine‐Learning Interatomic Potential for Simulating Chemically Complex Ni‐Based Superalloys

Advanced Engineering Materials, EarlyView.
We apply a foundational machine‐learning interatomic potential based on the graph atomic cluster expansion (GRACE) to simulate the commercial Ni‐based single‐crystal superalloy CMSX‐4. Hybrid Monte‐Carlo/molecular dynamics sampling resolves short‐range order in the γ phase and L12 sublattice occupancies in the γ’ phase and connects them to stacking ...
Aditya Vishwakarma, Sarath Menon, Fritz Körmann, Thomas Hammerschmidt, Ralf Drautz   +4 more
wiley   +1 more source