Results 81 to 90 of about 691,972 (277)

Some New Algebraic Method Developments in the Characterization of Matrix Equalities

open access: yesAxioms
Algebraic expressions and equalities can be constructed arbitrarily in a given algebraic framework according to the operational rules provided, and thus it is a prominent and necessary task in mathematics and applications to construct, classify, and ...
Yongge Tian
doaj   +1 more source

Generalized Inverse Classification [PDF]

open access: yes, 2017
Accepted to SDM 2017.
Lash, Michael T.   +4 more
openaire   +2 more sources

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

open access: yesAdvanced 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   +7 more
wiley   +1 more source

The m-CCE Inverse in Minkowski Space and Its Applications

open access: yesAxioms
In this paper, we introduce a new generalized inverse called m-CCE inverse which presents a generalization of the CCE inverse in Minkowski space by using the m-core-EP decomposition and the Minkowski inverse.
Xin Tan, Xiaoji Liu
doaj   +1 more source

Additive Manufacturing of Continuous Fibre Reinforced Composites: Process, Characterisation, Modelling, and Sustainability

open access: yesAdvanced Engineering Materials, EarlyView.
Additive manufacturing provides precise control over the placement of continuous fibres within polymer matrices, enabling customised mechanical performance in composite components. This article explores processing strategies, mechanical testing, and modelling approaches for additive manufactured continuous fibre‐reinforced composites.
Cherian Thomas, Amir Hosein Sakhaei
wiley   +1 more source

Phase Field Failure Modeling: Brittle‐Ductile Dual‐Phase Microstructures under Compressive Loading

open access: yesAdvanced Engineering Materials, EarlyView.
The approach by Amor and the approach by Miehe and Zhang for asymmetric damage behavior in the phase field method for fracture are compared regarding their fitness for microcrack‐based failure modeling. The comparison is performed for the case of a dual‐phase microstructure with a brittle and a ductile constituent.
Jakob Huber, Jan Torgersen, Ewald Werner
wiley   +1 more source

In Situ Micromechanical Study of Bimodal γ′–γ″ Precipitate Assemblies in Ni–Cr–Al–Nb Superalloy

open access: yesAdvanced Engineering Materials, EarlyView.
A Ni–Cr–Al–Nb superalloy with a bimodal γ′–γ″ precipitate distribution is developed. Composite precipitate assemblies form through heterogeneous nucleation, effectively impeding dislocation motion. Micropillar compression reveals high strength at room and elevated temperatures, governed by precipitate shearing, with coupled faulting mechanisms ...
Ujjval Bansal   +4 more
wiley   +1 more source

Magnetic Control of Chiral Hybridized Phonon Magnetic Moments in Ferrimagnets Fe2‐xZnxMo3O8

open access: yesAdvanced Functional Materials, EarlyView.
Helicity‐resolved magneto‐Raman spectroscopy reveals magnetic control of chiral phonon magnetic moments in polar ferrimagnet (ZnxFe2−xMo3O₈). Large spontaneous zero‐field phonon splittings, selective phonon–magnon coupling, and asymmetric Zeeman responses demonstrate that phonon chirality is governed by magnon‐phonon coupling and magnetization.
Youngsu Choi   +8 more
wiley   +1 more source

Through a generalized inverse

open access: yesDemonstratio Mathematica, 2008
AbstractTraditionally, the existence of a generalized inverse of a ...
openaire   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

open access: yesAdvanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore   +7 more
wiley   +1 more source

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