Results 161 to 170 of about 8,772,795 (303)

Cryogenic Deformation Behavior of TiAl and Insights Into Embrittlement After High‐Temperature Exposure Combined With Secondary Ion Mass Spectroscopy of Near Surface Chemistry

Advanced Engineering Materials, EarlyView.
Near‐surface deuterium enrichment profiles for different microstructural types of TiAl after exposure at 700 °C in a heavy water‐containing environment. The deuterium levels are significantly higher than expected from natural occurrence, indicating that the heavy water dissociated during the exposure treatment and entered the specimens.
Jonathan D. H. Paul, Alexander Welle, Dirk Matthiessen, Michael Oehring, Fritz Appel, Florian Pyczak   +5 more
wiley   +1 more source

Parameter calibration of the simulation model for coated rice seeds in UAV seeding based on discrete element methodology. [PDF]

PLoS One
Qingxu Y, Liu M, Gong Y, Chen X, Hu J.
europepmc   +1 more source

Elastic–Plastic Modeling and Experimental Characterization of Granular Jamming Actuators

Advanced Engineering Materials, EarlyView.
Granular jamming enables rapid switching between soft and rigid mechanical states. Here, a reduced‐order elastic‐perfectly‐plastic beam framework captures pressure‐dependent loading, unloading, residual deformation, and hysteresis in jammed beams, while matching three‐point bending experiments across materials and geometries. The framework links design
Saheli Patel, Prashant K. Purohit, Jordan R. Raney   +2 more
wiley   +1 more source

Ford 1600 Diesel, test 1228

, 1976
The Ford 1600 Diesel is being driven on the tractor test track followed by the test car and an Oliver ...
Nebraska Tractor Test Laboratory
core  

How Accurate Are Low-Cost Air Quality Sensors? A 10-Month Study of Three-Tier Comparison with Three Calibration Methods and Cross-Site Transferability. [PDF]

Sensors (Basel)
Mercado-Velazquez AA, Escamilla-Ambrosio PJ, Bodanese E, Islam M, Toosy F, Mosler G, Poslad S.   +6 more
europepmc   +1 more source

Thermal Cycle–Microstructure–Property Relationships in Rail Steel Heat‐Affected Zone Simulated by Gleeble

Advanced Engineering Materials, EarlyView.
Microstructural evolution of premium rail steel during flash butt welding (FBW), comparing physically simulated heat‐affected zones with actual weld regions. Thermal cycles induce phase transformations and microstructural heterogeneity that influence weld quality and mechanical performance. These findings provide insights into optimizing FBW parameters
Emmanoelle Cintra da Cunha Araújo, João Marcos da Silva Nunes, Marcelly Cristiny Nunes de Carvalho, Alex Rojas, Cristie Luis Kugelmeier, Cristhian Ricardo Loayza Loayza, João Carlos Ferreira, Maria Veronica Goncalves Rodrigues, Rute Sayuri Kano, Isaac Araújo Furtado, Jacyara Dalylla da Silva Nunes, Jhonatan Peres de Sousa, Marcos Natan da Silva Lima, Eden Santos Silva, Gedeon Silva Reis, Samuel Filgueiras Rodrigues   +15 more
wiley   +1 more source

test

, 2000
test
core  

Development and internal validation of machine learning models for predicting prediabetes in China. [PDF]

PLoS One
Jiang Z, Qian W, Yang H, Luo Y, Peng W, Wu Q, Wei X.   +6 more
europepmc   +1 more source

Microstructural, Mechanical, and Textural Evolution of Dissimilar Combination of Laser‐Welded Maraging Steel and 4340 Steel

Advanced Engineering Materials, EarlyView.
The present work reports the microstructural, mechanical, and texture evolution of a dissimilar combination laser‐welded maraging steel with 4340 steel. This is important for aerospace applications. The demand for dissimilar welding is growing rapidly with the requirement of efficient and durable joints.
Bharat Behl, Yu Dong, Alokesh Pramanik, Tapas Kumar Bandyopadhyay   +3 more
wiley   +1 more source

Investigation of Resonance Characteristics of More Than 10 000 Metal‐Coated Silicon MEMS Cantilevers Using Machine Learning

Advanced Engineering Materials, EarlyView.
More than 10 000 metal‐coated silicon MEMS cantilevers are fabricated and characterized to build a large‐scale experimental dataset spanning multiple vibrational modes. Machine learning algorithms are benchmarked to predict resonance frequency and quality factor, with ensemble based models achieving accuracy approaching experimental uncertainty.
Sina Zare Pakzad, Saurabh Thorat, Andre Loch Gesing, Dominik Huber, Christopher Zellner, Patrick Egger, Georg Pfusterschmied, Ulrich Schmid, Michael Schneider   +8 more
wiley   +1 more source