Results 111 to 120 of about 1,994,533 (253)

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

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

An Integrated Physics-Based and Data-Driven Framework for Defect Prediction in Advanced Nanoimprint Lithography Toward Inorganic Semiconductor Patterning

Micromachines
Advanced nanoimprint lithography (NIL) is promising for inorganic semiconductor patterning because it enables high-resolution replication with a relatively simple process flow; however, yield loss increasingly originates from spatially distributed ...
Jean Chien, Eric Lee
doaj   +1 more source

Defect structure in nanomaterials /

, 2012
Nanomaterials exhibit unique mechanical and physical properties compared to their coarse-grained counterparts, and are consequently a major focus of current scientific research.
Gubicza, Jeno,author.
core  

Unsupervised defect prediction review protocol

, 2019
This file is to describe our unsupervised defect prediction review ...
Martin Shepperd (620654), Ning Li (5491316), Yuchen Guo (6237404)   +2 more
core   +1 more source

Thermodynamic Pathways of Nonequilibrium Solidification in Wire‐Arc Additive Manufacturing Fe‐Based Multicomponent Alloy Structures

Advanced Engineering Materials, EarlyView.
Geometry‐driven thermal behavior in wire‐arc additive manufacturing (WAAM) influences microstructural evolution during nonequilibrium solidification of a chemically complex Fe–Cr–Nb–W–Mo–C nanocomposite system. By comparing different deposits configurations, distinct entropy–cooling rate correlations, segregation, and carbide evolution are revealed ...
Blanca Palacios, Anthony Gonzalez, Aleksander Aleman, Tyler Dolmetsch, Tanaji Paul, Arvind Agarwal   +5 more
wiley   +1 more source

A Hybrid DDMRP-OUTL Inventory Policy with Defect Prediction for Resilient Supply Chains

Jurnal Optimasi Sistem Industri
The high variability of consumer demand makes the development of inventory strategies crucial, especially regarding operational inventory resilience. Combining defect prediction with inventory strategies is crucial amidst uncertainty related to quality.
Erly Ekayanti Rosyida, Ilyas Mas'udin, Nisa Isrofi, Sulthan Rafif   +3 more
doaj   +1 more source

Towards Defect Phase Diagrams: From Research Data Management to Automated Workflows

Advanced Engineering Materials, EarlyView.
A research data management infrastructure is presented for the systematic integration of heterogeneous experimental and simulation data required for defect phase diagrams. The approach combines openBIS with a companion application for large‐object storage, automated metadata extraction, provenance tracking and federated data access, thereby supporting ...
Khalil Rejiba, Sang‐Hyeok Lee, Christina Gasper, Martina Freund, Sandra Korte‐Kerzel, Ulrich Kerzel   +5 more
wiley   +1 more source

Explainable AI-Driven Metrics for Transparent Software Quality Prediction

IEEE Access
Accurate software quality prediction is critical for early defect identification and effective allocation of testing resources. Although machine learning (ML) and deep learning (DL) models have significantly improved defect prediction performance, their ...
Abdulaziz Attaallah, Khalil Al Sulbi
doaj   +1 more source

Graph-based machine learning improves just-in-time defect prediction. [PDF]

PLoS One, 2023
Bryan J, Moriano P.
europepmc   +1 more source