Results 211 to 220 of about 102,036 (378)

Influence of Different Notch Insertion Methods on the Fatigue Behavior of Metastable Cr‐Ni‐Cu‐N and AISI 316L Austenitic Stainless Steels

open access: yesFatigue &Fracture of Engineering Materials &Structures, EarlyView.
ABSTRACT The fatigue behavior of a metastable austenitic Cr‐Ni‐Cu‐N steel and an austenitic AISI 316L steel was investigated with a focus on the effect of mechanically machined and formed notches, taking into account hardness and residual stress measurements.
Pia Nitzsche   +7 more
wiley   +1 more source

On the Identification of Dissipative Phenomena in Fatigue‐Loaded 2024 Aluminum by Means of Second Harmonic of Temperature Analysis

open access: yesFatigue &Fracture of Engineering Materials &Structures, EarlyView.
ABSTRACT This study explores the use of temperature harmonics to detect intrinsic dissipation during cyclic loading in aluminum alloys. Under sinusoidal loading, the temperature of a solid is modulated by thermomechanical heat sources. The primary source is the thermoelastic effect, which modulates the temperature at the load frequency and twice the ...
Riccardo Cappello   +3 more
wiley   +1 more source

Self-Diffusion of Ni in Austenite of Nickel Steels

open access: bronze, 1966
Naomichi Mori, Sin rsquo ichi Nagashima
openalex   +2 more sources

Very High Cycle Fatigue Performance of Ductile Cast Iron With Different Microstructures

open access: yesFatigue &Fracture of Engineering Materials &Structures, EarlyView.
ABSTRACTThe very high cycle fatigue performance of four ductile cast irons, a solid solution strengthened ferritic, ferritic‐pearlitic, and two austempered ductile cast irons, was investigated by ultrasonic fatigue testing under fully reversed loading conditions.
Max Ahlqvist   +3 more
wiley   +1 more source

Machine Learning Models to Predict the Static Failure of Double‐Lap Shear Bolted Connections

open access: yesFatigue &Fracture of Engineering Materials &Structures, EarlyView.
ABSTRACT This study investigates the potential of machine learning models to predict the failure load and mode of double‐lap shear bolted connections. Five algorithms were evaluated: adaptive boosting, artificial neural network, decision trees, support vector machines with radial basis function kernel, and k‐nearest neighbors.
H. Almuhanna, G. Torelli, L. Susmel
wiley   +1 more source

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