Results 71 to 80 of about 17,083 (305)

Fatigue Strength of Weathering Steel

Medžiagotyra, 2012
Fatigue behaviour of Atmofix 52 steel (comparable to COR-TENâ steel) exposed to atmospheric corrosion for 20 years was investigated. S-N curves for load symmetrical cycling and cycling with stress ratio R = 0 were determined on specimens detracted from a
Ludvík KUNZ, Petr LUKÁŠ, Jan KLUSÁK
doaj   +1 more source

Corrosion Fatigue Degradation Characteristics of Galvanized and Galfan High-Strength Steel Wire. [PDF]

Materials (Basel), 2023
Zhao Y, Su B, Fan X, Yuan Y, Zhu Y.
europepmc   +1 more source

Roadmap on Artificial Intelligence‐Augmented Additive Manufacturing

Advanced Intelligent Systems, EarlyView.
This Roadmap outlines the transformative role of artificial intelligence‐augmented additive manufacturing, highlighting advances in design, monitoring, and product development. By integrating tools such as generative design, computer vision, digital twins, and closed‐loop control, it presents pathways toward smart, scalable, and autonomous additive ...
Ali Zolfagharian, Liuchao Jin, Qi Ge, Wei‐Hsin Liao, Andrés Díaz Lantada, Francisco Franco Martínez, Tianyu Zhang, Tao Liu, Charlie C. L. Wang, Mohammad Hossein Mosallanejad, Reza Ghanavati, Abdollah Saboori, Alejandro De Blas De Miguel, William Solórzano‐Requejo, Yi Cai, Xiangyang Dong, Huangyi Qu, Najmeh Samadiani, Guangyan Huang, Austin Downey, Yanzhou Fu, Lang Yuan, Tsz‐Kwan (Glory) Lee, Arbind Agrahari Baniya, Eisha Waseem, Abdul Rahman Sani, Abbas Z. Kouzani, Yijia Wu, Markus P. Nemitz, Masoud Shirzad, Dageon Oh, Seung Yun Nam, Amedeo Franco Bonatti, Irene Chiesa, Gabriele Maria Fortunato, Giovanni Vozzi, Carmelo De Maria, Mahdi Bodaghi   +37 more
wiley   +1 more source

The corrosion fatigue behavior of magnesium alloy influenced by corrosion inhibitors with the differentiated inhibition mechanisms

Journal of Magnesium and Alloys
The effects of three corrosion inhibitors on Mg-Zn-Y-Nd alloy corrosion fatigue were investigated. Salicylic acid (SA) induces uniform but rapid corrosion, limiting fatigue life improvement.
Weizheng Cui, Di Mei, Yaqian Li, Shuya Mao, Shuijing Yu, Shijie Zhu, Liguo Wang, Shaokang Guan   +7 more
doaj   +1 more source

Experimental Research on Fatigue Performance of Reinforced Concrete T-Shaped Beams under Corrosion-Fatigue Coupling Action. [PDF]

Materials (Basel), 2023
Zhang T, Zhang X, Li P, Li H, Li X, Zou Y.   +5 more
europepmc   +1 more source

Enabling Metal‐Based Soft Robotics Through Investment Casting

Advanced Intelligent Systems, EarlyView.
Vacuum investment casting enables manufacturing of compliant soft robotic structures out of AA7075 high‐strength aluminum alloy. Additively manufactured patterns are converted into metal soft robotic structures addressing long lasting challenges like durability and nonlinearity of elastomer‐based soft robotics.
Felix Pancheri, Tim C. Lueth, Yilun Sun
wiley   +1 more source

Evaluation of Flexible Central Buckles on Short Suspenders' Corrosion Fatigue Degradation on a Suspension Bridge under Traffic Load. [PDF]

Materials (Basel), 2022
Zhao Y, Guo X, Su B, Sun Y, Li X.
europepmc   +1 more source

Corrosion‐fatigue behaviour of Cr–Mo steel under biaxial tension

, 2020
Vidit Gaur, V. Doquet, Emmanuel Persent, Eléonore Roguet   +3 more
openalex   +2 more sources

Errata: “Corrosion Fatique Crack Growth in Reactor Pressure Vessel Steels in PWR Primary Water” (Journal of Pressure Vessel Technology, 1983, 105, pp. 245–254) and “Development of Engineering Codes of Practice for Corrosion Fatigue” (Journal of Pressure Vessel Technology, 1983, 105, pp. 255–262) [PDF]

, 1983
P.M. Scott, A.E. Truswell, B. Tomkins, A.J.E. Foreman   +3 more
openalex   +1 more source

Deep Learning Approaches for Classifying Crack States With Overload and Predicting Fatigue Parameters in a Titanium Alloy

Advanced Intelligent Systems, EarlyView.
This study proposes a deep learning approach to evaluate the fatigue crack behavior in metals under overload conditions. Using digital image correlation to capture the strain near crack tips, convolutional neural networks classify crack states as normal, overload, or recovery, and accurately predict fatigue parameters.
Seon Du Choi, Suman Timilsina, Youn Woo Shin, Yong Nam Kwon, Kee‐Sun Sohn, Ji Sik Kim   +5 more
wiley   +1 more source