Results 51 to 60 of about 606,186 (241)

Harnessing Fungal Biowelding for Constructing Mycelium‐Engineered Materials

Advanced Engineering Materials, EarlyView.
Mycelium‐bound composites (MBCs) offer low‐carbon alternatives for construction, yet interfacial bonding remains a critical challenge. This review examines fungal biowelding as a biocompatible adhesive, elucidating mycelium‐mediated interfacial mechanisms and their role in material assembly. Strategies to optimize biowelding are discussed, highlighting
Xue Brenda Bai, Xinrui Sarah Niu, Zhao Qin   +2 more
wiley   +1 more source

A Thermodynamic 3D Model for the Simulation of Diffusion‐Controlled Alloying Processes in Heterogeneous Material Structures

Advanced Engineering Materials, EarlyView.
A numerical model resulting from irreversible thermodynamics for describing transport processes is introduced, focusing on thermodynamic activity gradients as the actual driving force for diffusion. Implemented in CUDA C++ and using CalPhaD methods for determining the necessary activity data, the model accurately simulates interdiffusion in aluminum ...
Ulrich Holländer, Leonhard Holländer, André Langohr, Hans Jürgen Maier   +3 more
wiley   +1 more source

A novel machine learning-based model for predicting the transition fatigue lifetime in piston aluminum alloys

International Journal of Lightweight Materials and Manufacture
The estimation of transition fatigue lifetimes for piston aluminum alloys was carried out using unsupervised machine learning (ML) with the K-means algorithm.
Mahmood Matin, Mohammad Azadi
doaj   +1 more source

Estimating the square root of probability density function on Riemannian manifold [PDF]

, 2018
We propose that the square root of a probability density function can be represented as a linear combination of Gaussian kernels. It is shown that if the Gaussian kernel centres and kernel width are known, then the maximum likelihood parameter estimator ...
Absil, Boumal, Chen, Chen, Chen, Chen, Dempster, Duda, Girolami, Hager, Harandi, Haykin, Hong, Hong, Lui, McLachlan, Mishra, Parzen, Pinheiro, Rutkowski, Silverman, Vapnik, Weston, Yin   +23 more
core   +1 more source

Microstructural Evolution and Vacancy Defect Formation in Mn–Mo–Ni RPV Steel Under Low Cycle Fatigue: Insights From EBSD and PALS

Advanced Engineering Materials, EarlyView.
Low‐cycle fatigue damage in Mn–Mo–Ni reactor pressure vessel steel is examined using a combined electron backscatter diffraction and positron annihilation lifetime spectroscopy approach. The study correlates texture evolution, dislocation substructure development, and vacancy‐type defect formation across uniform, necked, and fracture regions, providing
Apu Sarkar, Dhanadeep Dutta, G. Bharat Reddy   +2 more
wiley   +1 more source

EBSD Study of Creep‐Induced Lattice Misorientation in MgO‐Particle‐Reinforced Austenitic Steel Composites

Advanced Engineering Materials, EarlyView.
Creep experiments at 900°C on coarse‐grained steel‐ceramic composites containing recycled magnesia reveal that higher ceramic volume fractions significantly enhance the creep resistance. Detailed EBSD investigations identify subgrain formation in the steel matrix as the dominant deformation mechanism.
Moritz Müller, Amaya Compart, Mahnaz Mehdizadehlima, Serhii Yaroshevskyi, Christos G. Aneziris, Horst Biermann, Anja Weidner   +6 more
wiley   +1 more source

Group-based local adaptive deep multiple kernel learning with lp norm

PLoS ONE, 2020
The deep multiple kernel Learning (DMKL) method has attracted wide attention due to its better classification performance than shallow multiple kernel learning.
Shengbing Ren, Fa Liu, Weijia Zhou, Xian Feng, Chaudry Naeem Siddique, Robertas Damasevicius   +5 more
doaj   +2 more sources

Consensus Multiple Kernel K-Means Clustering With Late Fusion Alignment and Matrix-Induced Regularization

IEEE Access, 2019
Multiple kernel clustering (MKC) attracts considerable attention due to its competitive performance in unsupervised learning. However, we observe that most of the existing MKC approaches do not sufficiently consider the correlation between different ...
Jingtao Hu, Miaomiao Li, En Zhu, Siwei Wang, Xinwang Liu, Yongping Zhai   +5 more
doaj   +1 more source

Additive Gaussian Process Regression for Predictive Design of High‐Performance, Printable Silicones

Advanced Engineering Materials, EarlyView.
A chemistry‐aware design framework for tuning printable polydimethylsiloxane (PDMS) for vat photopolymerization (VPP) is developed using additive Gaussian process (GP) modeling. Polymer network mechanics informs variable groupings, feasible formulation constraints, and interaction variables.
Roxana Carbonell, Martha Leach, Hongtao Song, Carolyn Seepersad   +3 more
wiley   +1 more source

Productivity‐Driven Optimization of Laser Powder Bed Fusion Parameters for IN718 Superalloy: Process Control, Microstructure, and Mechanical Properties

Advanced Engineering Materials, EarlyView.
This study demonstrates how optimizing laser power, scanning speed, and hatching distance in laser powder bed fusion can boost the productivity of Inconel 718 manufacturing by up to 29% while maintaining mechanical integrity. The work delivers a validated process window and cost–time analysis, offering industry‐ready guidelines for efficient additive ...
Amir Behjat, Guido Barale, John Stavridis, Ehsan Norouzi, Jin‐Yoo Suh, Dong‐Ik Kim, Luca Iuliano, Abdollah Saboori   +7 more
wiley   +1 more source