Results 101 to 110 of about 8,528,873 (308)

R&D of the EM Calorimeter Energy Calibration with Machine Learning based on the low-level features of the Cluster [PDF]

open access: yesEPJ Web of Conferences
We have developed an energy calibration method using machine learning for the ILC electromagnetic (EM) calorimeter (ECAL), a sampling calorimeter consisting of Silicon-Tungsten layers.
Morimasa Suzuna   +8 more
doaj   +1 more source

Inverse Identification of Energy‐Dependent Laser Absorptivity in NiTi Laser Powder‐Bed Fusion via Calibrated Melt Pool Simulation

open access: yesAdvanced Engineering Materials, EarlyView.
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi   +3 more
wiley   +1 more source

Jet Energy Calibration in the CMS experiment

open access: yesEPJ Web of Conferences, 2013
Jet reconstruction and calibration in the CMS experiment are complicated by the nonlinear response of the calorimeters and high pileup conditions. These difficulties are mitigated at CMS by utilising the particle flow approach. The jet energy calibration
Rathjens Denis   +3 more
doaj   +1 more source

Energy gain scale calibration of the XRISM Resolve microcalorimeter spectrometer: ground calibration results and on-orbit comparison

open access: yesAstronomical Telescopes + Instrumentation
. The Resolve instrument aboard the X-ray Imaging and Spectroscopy Mission (XRISM) is a 36-pixel microcalorimeter spectrometer that provides nondispersive spectroscopy with ∼5  eV spectral resolution in the soft X-ray waveband.
M. Eckart   +31 more
semanticscholar   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

open access: yesAdvanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer   +4 more
wiley   +1 more source

Energy Calibration for the GERDA Experiment

open access: yes, 2020
GERDA (GERmanium Detector Array) is an experiment conducted at the underground laboratory of LNGS (Laboratori Nazionali del Gran Sasso) to search for neutrinoless double beta decay in Ge76. It uses enriched high-purity germanium diodes both as the sources of the decay and as the detectors.
Ransom, Chloe, Junting Huang
openaire   +1 more source

Influence of Scan Strategies in Electron Beam Powder Bed Fusion on Solidification, Microstructure, and High‐Temperature Compressive Properties of γ′‐Strengthened Inconel 738LC

open access: yesAdvanced Engineering Materials, EarlyView.
Experiments and thermophysical simulations were conducted to investigate the electron beam powder bed fusion electron beam (PBF‐EB/M) process for the γ′‐strengthened nickel‐based superalloy Inconel 738LC. The results demonstrate the impact of process‐induced microstructural variations on high‐temperature mechanical behavior, providing a basis for ...
Jan Niklas Petenati   +11 more
wiley   +1 more source

New AI‐Assisted Approach for Expanding the Solution Space: Application to Lattice Structure Design

open access: yesAdvanced Engineering Materials, EarlyView.
This work introduces an innovative framework for designing structured materials by ex panding the design space through reparameterization of qualitative variables into continuous structural descriptors. Combined with machine‐learning‐based prediction and multi‐objective optimization, the approach enables the discovery of novel lattice architectures ...
G. H. Gahimbare   +5 more
wiley   +1 more source

OntOMat: Toward Ontology‐Based Product and Process Design Engineering and Optimization Solutions Fueling Circular Value Chains

open access: yesAdvanced Engineering Materials, EarlyView.
The OntOMat ontology establishes a structured framework for polymer matrix fiber reinforced composite materials, integrating manufacturing processes, characterization methods, and multiscale design through the VDI/VDE 3682 formalized process description standard.
Nicolas Christ   +19 more
wiley   +1 more source

Semantic Modeling in Materials Science and Engineering With Platform MaterialDigital Core Ontology 3.0

open access: yesAdvanced Engineering Materials, EarlyView.
The community‐driven Platform MaterialDigital Core Ontology (PMDco) 3.0 is introduced as a Basic Formal Ontology‐aligned semantic backbone for the processing–structure–properties paradigm in Materials Science and Engineering. Modular engineering, automated releases, and validation workflows are highlighted and key semantic patterns for materials ...
Markus Schilling   +15 more
wiley   +1 more source

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