Results 81 to 90 of about 731,480 (301)

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Prediction of Surface Topography Parameters in Direct Laser Interference Patterning of Stainless Steel Using Infrared Monitoring and Convolutional Neural Networks

Advanced Engineering Materials, EarlyView.
This study presents an infrared monitoring approach for direct laser interference patterning (DLIP) combined with a convolutional neural network (CNN). Thermal emission data captured during structuring are used to predict surface topography parameters.
Lukas Olawsky, Marcelo Sallese, Leander Kläber, Clemens Kuhn, Keming Du, Andrés Fabián Lasagni   +5 more
wiley   +1 more source

Gaussian Processes for Regression [PDF]

, 1995
The Bayesian analysis of neural networks is difficult because a sim ple prior over weights implies a complex prior distribution over functions. In this paper we investigate the use of Gaussian process priors over functions, which permit the predictive Bayesian anal ysis for fixed values of hyperparameters to be carried out exactly using matrix ...
Williams, Christopher, Rasmussen, Carl
openaire   +2 more sources

Redshift Evolution of the X-Ray and Ultraviolet Luminosity Relation of Quasars: Calibrated Results from SNe Ia

The Astrophysical Journal
Quasars could serve as standard candles if the relation between their ultraviolet (UV) and X-ray luminosities can be accurately calibrated. Previously, we developed a model-independent method to calibrate quasar standard candles using the distance ...
Xiaolei Li, Ryan E. Keeley, Arman Shafieloo   +2 more
doaj   +1 more source

Characterization of Nitinol Produced by Laser Powder Bed Fusion for Mechanical Metamaterial Applications

Advanced Engineering Materials, EarlyView.
Stabilization of L‐PBF Ni50.7Ti49.3 under low‐cycle loading was investigated. Recoverable strain after cycling was dependent on the amount of applied load. Recovery ratio was 53.4% and 35.1% at intermediate and high load, respectively. The maximum total strain reached 10.3% at a high load of 1200 MPa.
Ondřej Červinek, Jakub Hurník, Miroslav Šmíd, Ondřej Zobač, Melanie Todt, Daniel Koutný   +5 more
wiley   +1 more source

Universal Stochastic Process in Blazar Gamma-Ray Variability Revealed by Bayesian Kernel Comparison

The Astrophysical Journal
Although Gaussian processes have been widely applied in modeling the high-energy light curves of blazars, a systematic comparison of the performances of different kernel functions within Gaussian processes has remained lacking.
Jiachao Liu, Nan Ding, Qiusheng Gu, Keli Peng, Yunyong Tang, Yongyun Chen, Dingrong Xiong   +6 more
doaj   +1 more source

Quality-Related Fault Detection Based on Improved Independent Component Regression for Non-Gaussian Processes

IEEE Access, 2019
Partial least squares (PLS) and linear regression methods have been widely utilized for quality-related fault detection in industrial processes recently.
Majed Aljunaid, Hongbo Shi, Yang Tao
doaj   +1 more source

Influence of an Argon/Silane Atmosphere on the Temperature of a Thermal Plasma

Advanced Engineering Materials, EarlyView.
The influence of a silane‐doped argon atmosphere on the chemical composition and temperature of a thermal nontransferring argon plasma is investigated using optical emission spectroscopy. As a result of the high amount of free electrons resulting from the stepwise ionization and dissociation of the silane molecule, even a silane addition of 0.01 vol ...
Lena Kreie, David Kramer, Thomas Scheithauer, Hans Jürgen Maier, Thomas Hassel   +4 more
wiley   +1 more source

Scalable Gaussian Process Regression Networks [PDF]

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020
Gaussian process regression networks (GPRN) are powerful Bayesian models for multi-output regression, but their inference is intractable. To address this issue, existing methods use a fully factorized structure (or a mixture of such structures) over all the outputs and latent functions for posterior approximation, which, however, can miss the strong ...
Shibo Li, Wei Xing, Robert M. Kirby, Shandian Zhe   +3 more
openaire   +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