Results 81 to 90 of about 15,135,820 (245)

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

Advanced 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, Karthik Kumar Banku, Prasanth Bondi, Ivan Zhirnov, Yangyiwei Yang, Gordon Schmidt, Jürgen Christen, Georg Hasemann, Manja Krüger, Bai‐Xiang Xu, Bilal Gökce, Silja‐Katharina Rittinghaus   +11 more
wiley   +1 more source

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

Advanced 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, Y. Beideler, D. Chune, R. De Guio, T. Roland, H. Chibane   +5 more
wiley   +1 more source

Using Bandit Algorithms for Project Selection in Cross-Project Defect Prediction

, 2021
Background: defect prediction model is built using historical data from previous versions/releases of the same project. However, such historical data may not exist in case of newly developed projects.
Bennin, Kwabena Ebo, Tahir, Amjed, Nakasai, Keitaro, Asano, Takuya, Tsunoda, Masateru, Toda, Koji, Monden, Akito, Matsumoto, Kenichi   +7 more
core   +1 more source

ALTRA: Cross-Project Software Defect Prediction via Active Learning and Tradaboost

IEEE Access, 2020
Cross-project defect prediction (CPDP) methods can be used when the target project is a new project or lacks enough labeled program modules. In these new target projects, we can easily extract and then measure these modules with software measurement ...
Zhidan Yuan, Xiang Chen, Zhanqi Cui, Yanzhou Mu   +3 more
doaj   +1 more source

DigiChrom: A Domain Ontology for Semantic Representation of Trivalent Chromium Platings and Its Large Language Model‐Based Alignment With Multiple Mid‐Level Ontologies

Advanced Engineering Materials, EarlyView.
Digitalizing electroplating requires both domain knowledge and interoperability. This work introduces PlatOn, a domain ontology for trivalent chromium plating and coating characterization, and a hybrid pipeline that aligns it to a mid‐level reference ontology by combining eight similarity metrics with language model reasoning. Expert‐validated mappings
Janik Harter, Elena Garcia Trelles, Christoph Schweizer, Thomas Seifert, Christoph Baumer, Mahmoud Elkady, Katja Feige, Daniel Grimme, Edgar Hoffmann, Andreas Waibel, Anke Walter   +10 more
wiley   +1 more source

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

Advanced 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, Jörg Hohe, Martin Kördel, Michael Giese, Niklas Toffert, Christian Seidel, Olaf Rehme, Patrik Schneider, Oliver Focke, Elie Abi Aoun, Claas Falldorf, Beñat Gutierrez, Alexandra Kühn, Sven Torstrick-von der Lieth, Christoph Herden, Erik Johannsen, Jan‐Uwe R. Schmidt, Felix Winkelmann, Andreas Schott, Daniel Schott   +19 more
wiley   +1 more source

Integrated Approach to Software Defect Prediction

IEEE Access, 2017
Software defect prediction provides actionable outputs to software teams while contributing to industrial success. Empirical studies have been conducted on software defect prediction for both cross-project and within-project defect prediction.
Ebubeogu Amarachukwu Felix, Sai Peck Lee
doaj   +1 more source

Distinct Microstructural Characteristic Lengths Defining Notch Fatigue Crack Initiation and Propagation, and Defect Tolerance in Advanced Steels

Advanced Engineering Materials, EarlyView.
Schematic representation of modes of microcrack nucleation, growth and temporary arrestment at different boundaries, with the criteria for crack propagation in three scenarios: (1) large notch, (2) defect/small sharp notch, and (3) long crack. This work revisits and integrates results on the fatigue behavior of advanced bainitic steels (in particular ...
Lucia Morales‐Rivas
wiley   +1 more source

Cross-project defect prediction: a large scale experiment on data vs. domain vs. process [PDF]

, 2009
Prediction of software defects works well within projects as long as there is a sufficient amount of data available to train any models. However, this is rarely the case for new software projects and for many companies.
Zimmermann, Thomas, Harald Gall, Gall, Harald; https://orcid.org/, Emanuel Giger, Murphy, Brendan, Giger, Emanuel, Thomas Zimmermann, Brendan Murphy, Nachiappan Nagappan, Nagappan, Nachiappan   +9 more
core   +1 more source

Cross-Project Defect Prediction Based on Two-Phase Feature Importance Amplification. [PDF]

Comput Intell Neurosci, 2022
Xing Y, Lin W, Lin X, Yang B, Tan Z.
europepmc   +1 more source