Results 101 to 110 of about 186,399 (262)

Recycling of NiTi Shape Memory Alloys: Fundamental and Technological Aspects of a Vacuum Induction Melting Processing Route

Advanced Engineering Materials, EarlyView.
The present study investigates recycling of NiTi shape memory alloys via vacuum induction melting. An ingot was synthesized from elemental Ni and Ti and subjected to three subsequent remelting cycles. Remelting increases process durations and impurity levels and adversely affects microstructures and functional properties.
Sakia Sophia Noorzayee, Son Tung Mai, Sarath Menon, Braulio Gadiel Sánchez Ortega, Ralf Drautz, Alexander Kauffmann, Gunther Eggeler, Jan Frenzel   +7 more
wiley   +1 more source

Beyond multi-class – structured learning for machine translation

, 2010
In this thesis, we explore and present machine learning (ML) approaches to a particularly challenging research area – machine translation (MT). The study aims at replacing or developing each component in the MT system with an appropriate discriminative ...
Ni, Y, Ni, Yizhao
core  

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

Advanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara, Vincent Rat, Quentin Dubreuil, Patrice Valorge, Simon Goutier, Alain Denoirjean, Olivier Messe, Céline Ruelle, Jean‐Philippe Poli   +8 more
wiley   +1 more source

Abstraction for Bayesian Reinforcement Learning in Factored POMDPs

Publisher Copyright: © 2025, Transactions on Machine Learning Research. All rights reserved.Bayesian reinforcement learning provides an elegant solution to addressing the explo-ration–exploitation trade-off in Partially Observable Markov Decision ...
Starre, Rolf A.N., Katt, Sammie, Loog, Marco, Oliehoek, Frans A., Çelikok, Mustafa Mert   +4 more
core  

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, Andreas Limper, Ulrich Giese, Carlos Lucke, Mario Beiner, Christoph Gögelein, Lisa Leuchtenberger‐Engel, Christian Hopmann   +7 more
wiley   +1 more source

GPU Power Prediction via Ensemble Machine Learning for DVFS Space Exploration

, 2018
A software-based approach to achieve high performance within a power budget often involves dynamic voltage and frequency scaling (DVFS). Consequently, accurately predicting the power consumption of an application at different DVFS levels (or more ...
Feng, Wu-chun, Adhinarayanan, Vignesh, Dutta, Bishwajit   +2 more
core  

Machine Learning and Artificial Intelligence–accelerated Computational Approaches in Materials Science

Journal of Physics: Conference Series
Abstract Material discovery and design are being revolutionised by machine learning and artificial intelligence (AI). Traditional methods are powerful, but they are computationally costly and do not efficiently explore the vast chemical and structural design space.
Shafiq Sharhrah, Neetu Singh, Ganesh Maurya, Manbir Kaur, Chitransh Bose, Rashmi Priyadarshini   +5 more
openaire   +1 more source

Workflow for Design of Experiments‐Based Modeling of Species Transport and Growth Kinetics in GaN Hydride Vapor Phase Epitaxy

Advanced Engineering Materials, EarlyView.
A novel workflow for investigating hydride vapor phase epitaxy for GaN bulk crystal growth is proposed. It combines Design of experiments (DoE) with physical simulations of mass transport and crystal growth kinetics, serving as an intermediate step between DoE and experiments.
J. Tomkovič, G. Lukin, H. Torkashvand, A. Kerschbaum, M. Müller, S. Besendörfer, J. Heitmann, J. Friedrich   +7 more
wiley   +1 more source

Using unsupervised machine learning for fault identification in virtual machines

, 2015
Self-healing systems promise operating cost reductions in large-scale computing environments through the automated detection of, and recovery from, faults. However, at present there appears to be little known empirical evidence comparing the different
Schneider, Christopher
core  

Toward Knowledge‐Based Workflows: A Semantic Approach to Atomistic Simulations for Mechanical and Thermodynamic Properties

Advanced Engineering Materials, EarlyView.
Knowledge‐based atomistic workflows are presented for mechanical and thermodynamic properties. By coupling modular simulations with ontology‐aligned metadata and provenance, Fe case studies on elastic behavior, defects, thermal properties, and Hall–Petch strengthening reveal how FAIR, queryable, and reusable simulation data can be generated. Mechanical
Abril Azócar Guzmán, Hoang‐Thien Luu, Sarath Menon, Tilmann Hickel, Nina Merkert, Stefan Sandfeld   +5 more
wiley   +1 more source