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+5 more
wiley +1 more source
Asymptotic Confidence Interval, Sample Size Formulas and Comparison Test for the Agreement Intra-Class Correlation Coefficient in Inter-Rater Reliability Studies. [PDF]
Bourredjem A, Cardot H, Devilliers H.
europepmc +1 more source
How to obtain the confidence interval from a P value
D. Altman, J. Bland
semanticscholar +1 more source
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+4 more
wiley +1 more source
Confidence Interval Estimation for Cutting Tool Wear Prediction in Turning Using Bootstrap-Based Artificial Neural Networks. [PDF]
Colantonio L+3 more
europepmc +1 more source
Enhanced Fog Water Harvesting on Superhydrophobic Steel Meshes
Fog harvesting using mesh designs offers a sustainable solution to water scarcity. This study highlights key considerations for fog harvesting research and develops a methodology for a standardized protocol reflecting fog characteristics and environmental conditions.
Pegah Sartipizadeh+3 more
wiley +1 more source
Construction of Confidence Interval for a Univariate Stock Price Signal Predicted Through Long Short Term Memory Network. [PDF]
De S, Dey AK, Gouda DK.
europepmc +1 more source
Computation of confidence intervals for Poisson processes [PDF]
J. A. Aguilar–Saavedra
openalex +1 more source
Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials
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+4 more
wiley +1 more source
3D‐Printed Architected Material for the Generation of Foam‐Based Protective Equipment
This study investigates 3D‐printed architected structures as alternatives to traditional foams in protective gear. It focuses on customizing impact strength and damping through design and manufacturing integration. Testing shows these structures outperform conventional foams, offering enhanced customizability, lower weight, and tunable performance ...
Ali Zolfagharian+5 more
wiley +1 more source