Results 141 to 150 of about 1,157,336 (324)

Static and Dynamic Behavior of Novel Y‐Shaped Sandwich Beams Subjected to Compressive Loadings: Integration of Supervised Learning and Experimentation

Advanced Engineering Materials, EarlyView.
In this study, the mechanical response of Y‐shaped core sandwich beams under compressive loading is investigated, using deep feed‐forward neural networks (DFNNs) for predictive modeling. The DFNN model accurately captures stress–strain behavior, influenced by design parameters and loading rates.
Ali Khalvandi, Saeed Kamarian, Mahdi Bodaghi, Saeed Saber‐Samandari, Jung‐il Song   +4 more
wiley   +1 more source

Learning with prolonged delay of reinforcement [PDF]

, 1966
John García, Frank R. Ervin, R. A. Koelling   +2 more
openalex   +1 more source

Nanoparticle‐Coated X2CrNiMo17‐12‐2 Powder for Additive Manufacturing—Part II: Processability by Powder Bed Fusion of Metals Using a Laser Beam

Advanced Engineering Materials, EarlyView.
In this manuscript, the processability of X2CrNiMo17‐12‐2 powder coated with silicon carbide, silicon, and silicon nitride nanoparticles is investigated. The amount of nanoparticles varies from 0.25 to 1 vol%. By coating the powder feedstock material with nanoparticles, an enlargement of the process window and an increase in the build rate are achieved.
Nick Hantke, Tim R. Brocksieper, Arne Lüddecke, Tobias Grimm, Arno Kwade, Jan T. Sehrt   +5 more
wiley   +1 more source

On the selective reinforcement of spaced responses.

, 1953
Maurice P. Wilson, Fred S. Keller
openalex   +2 more sources

Nanoparticle‐Coated X2CrNiMo17‐12‐2 Powder for Additive Manufacturing – Part I: Surface, Flowability, and Optical Properties of SiC, Si, and Si3N4 Coated Metal Powders

Advanced Engineering Materials, EarlyView.
Herein, silicon‐based nanoparticle coatings on X2CrNiMo17‐12‐2 metal powder are presented. The coating process scale, process parameters, nanoparticle size (65–200 nm) as well as the coating amount are discussed regarding powder properties. The surface roughness affects the flowability, while reflectance depends on the coating material and surface ...
Arne Lüddecke, Nick Hantke, Harald Zetzener, Jan T. Sehrt, Arno Kwade   +4 more
wiley   +1 more source

Conjugate reinforcement of operant responding in infants [PDF]

, 1966
Lewis P. Lipsitt, Linda Johnson Pederson, Clement A. DeLucia   +2 more
openalex   +1 more source

Deformation Behavior of La2O3‐Doped Copper during Equal Channel Angular Pressing

Advanced Engineering Materials, EarlyView.
By additions of strengthening elements and/or structure optimization, the mechanical properties of copper can be increased while keeping favorable electric conductivity. By combining addition of La2O3 and processing by equal channel angular pressing, substructure development is achieved, leading to increase in microhardness to more than double the ...
Lenka Kunčická, Josef Walek, Radim Kocich   +2 more
wiley   +1 more source

Control and Real-Time Monitoring of Autonomous Underwater Vehicle Through Underwater Wireless Optical Communication

Applied Sciences
Real-time command and data transfer are essential for autonomous underwater vehicle (AUV) motion control in underwater missions. Due to the limitations of underwater acoustic communication, which has a low data rate, this paper introduces a new control ...
Dongwook Jung, Rouchen Zhang, Hyunjoon Cho, Daehyeong Ji, Seunghyen Kim, Hyeungsik Choi   +5 more
doaj   +1 more source

Control of responding by a percentage reinforcement schedule [PDF]

, 1967
Shin‐Ho Chung, Allen Neuringer
openalex   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

Advanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani, Surabhi Nadigotti, Erfan Fatehi, Derek Aranguren van Egmond, Behnam Ashrafi   +4 more
wiley   +1 more source