Results 211 to 220 of about 102,036 (378)
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
Martensitic Transformation Mechanism In Situ Observation for the Simulated Coarse-Grained Heat-Affected Zone of DP1180 Steel. [PDF]
Li W+6 more
europepmc +1 more source
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
Naomichi Mori, Sin rsquo ichi Nagashima
openalex +2 more sources
Deformation Behavior of S32750 Duplex Stainless Steel Based on In Situ EBSD Technology. [PDF]
Bao S, Feng H, Song Z, He J, Wu X, Gu Y.
europepmc +1 more source
Very High Cycle Fatigue Performance of Ductile Cast Iron With Different Microstructures
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
Evolution of dislocations during the rapid solidification in additive manufacturing. [PDF]
Gao L+5 more
europepmc +1 more source
Machine Learning Models to Predict the Static Failure of Double‐Lap Shear Bolted Connections
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