Results 131 to 140 of about 19,143 (304)

Machine Learning-Assisted Multi-Property Prediction and Sintering Mechanism Exploration of Mullite–Corundum Ceramics

Mullite–corundum ceramics are pivotal in heat transfer pipelines and thermal energy storage systems due to their excellent mechanical properties, thermal stability, and chemical resistance.
Jian Pan, Xiaocheng Liang, Pengrui Wang, Weijin Zhang, Weibin Xu, Qingyue Chen, Hao Feng, Benjun Cheng   +7 more
core   +1 more source

Technical ceramics, properties and applications

, 2021
U ovom zavrsnom radu prikazana je povijest keramike od otkrica pa sve do dana danasnjeg, te kako je napredovala keramika od pocetka. Navedena je osnovna podjela keramike, zatim podjela tehnicke keramike, njezina grada i svojstva.
Ergotić, Ana
core  

Influence of Geometric Design on Mechanical Performance of Auxetic Metastructure

Advanced Engineering Materials, EarlyView.
Strategic geometric reinforcement transforms auxetic performance. This study evaluates 3D‐printed arrowhead metastructures, revealing that a modified design with local ring reinforcement suppresses premature failure to achieve superior energy absorption and structural efficiency.
Muhammad Gulzari, Supharoek Sornsomrit, Muhammad Ayaz Akbar, Stephan Rudykh   +3 more
wiley   +1 more source

Motifs and in Personal Interpretationns Used in Traditional İznik Ceramics and Decors

, 2010
Geleneksel İznik Çini Dekorlarında Kullanılan Motifler ve Kişisel Yorumlar isimli tez çalışmamda geleneksel sanatlardan biri olan çini sanatının motifleri, çizim aşamaları ve motiflerin gelişim aşamalarını içeren bilgiler ile ilgili genel bir ...
Yılmaz, Aslı
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A Dislocation Perspective on Strength and Toughness in Ceramics

Advanced Engineering Materials, EarlyView.
Dislocations in ceramics enjoy a long but yet under‐appreciated history. The three research waves for dislocations in ceramics highlight the topic evolution over the last 90 years. This review focuses on the impact of dislocation on strength and toughness in ceramics.
Xufei Fang
wiley   +1 more source

Tony HACKENS (Éd.), Ancient and Traditional Ceramics. Céramiques anciennes et traditionnelles.

, 2000
Greene Kevin, Hawthorne John. Tony HACKENS (Éd.), Ancient and Traditional Ceramics. Céramiques anciennes et traditionnelles.. In: L'antiquité classique, Tome 69, 2000. pp.
Greene, Kevin, Hawthorne, John
core   +1 more source

Compatibility of Methacrylate Based Resins Controls Interfacial Failure and Toughness in 3D‐Printed Multimaterial Composites

Advanced Engineering Materials, EarlyView.
This work shows that the mechanical performance of multimaterial digital light processing (DLP) printed thermoset composites is governed by resin compatibility and interfacial design rather than spatial patterning alone. Brittle and ductile resin combinations produced premature interfacial failure, while graded interfaces and mechanically compatible ...
Ahmed M. H. Ibrahim, Li Meng, Ahmad R. Najafi, Nicolas J. Alvarez   +3 more
wiley   +1 more source

Technological upgradation of small and medium enterprises (SMEs) in traditional ceramics

, 2002
In the changing global scenario, recent trends in technology for production of traditional ceramics in terms of raw materials, processing and equipment; forming processes, frits, glazes and colours; firing etc are discussed.
Maiti, Kedar Nath
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The Influence of Thickness on Light Transmission for Pre- and Fully Crystallized Chairside CAD/CAM Lithium Disilicate Ceramics

Aim: This in vitro study aimed to compare the light-transmission properties of two chairside CAD/CAM lithium disilicate (LD) ceramics (a novel fully crystallized and a traditional pre-crystallized) across varying thicknesses.
Salahaldeen Abuhammoud, Silvia Rojas-Rueda, Carlos A. Jurado, Chin-Chuan Fu, Amit Unnadkat, Nicholas G. Fischer, Franciele Floriani   +6 more
core   +1 more source

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

Advanced 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, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source