Early Stage Fatigue Damage Characterization in Aluminum Alloys and Stainless Steels with Meandering Winding Magnetometer Technology

2008 ◽  
pp. 427-427-12
Author(s):  
V Weiss ◽  
N Goldfine ◽  
M Natishan
Keyword(s):  
Aluminum Alloys ◽  
Fatigue Damage ◽  
Stainless Steels ◽  
Early Stage ◽  
Damage Characterization

Plastic Slip and Early Stage of Fatigue Damage in Polycrystals: Comparison between Experiments and Crystal Plasticity Finite Elements Simulations

2014 ◽  
Vol 891-892 ◽  
pp. 1711-1716 ◽  
Author(s):  
Loic Signor ◽  
Emmanuel Lacoste ◽  
Patrick Villechaise ◽  
Thomas Ghidossi ◽  
Stephan Courtin
Keyword(s):  
Fatigue Crack ◽  
Finite Elements ◽  
Crack Initiation ◽  
Fatigue Damage ◽  
Crystal Plasticity ◽  
Early Stage ◽  
Low Cycle Fatigue ◽  
Fatigue Cracks ◽  
Fatigue Crack Initiation ◽  
Plastic Slip

For conventional materials with solid solution, fatigue damage is often related to microplasticity and is largely sensitive to microstructure at different scales concerning dislocations, grains and textures. The present study focuses on slip bands activity and fatigue crack initiation with special attention on the influence of the size, the morphology and the crystal orientation of grains and their neighbours. The local configurations which favour - or prevent - crack initiation are not completely identified. In this work, the identification and the analysis of several crack initiation sites are performed using Scanning Electron Microscopy and Electron Back-Scattered Diffraction. Crystal plasticity finite elements simulation is employed to evaluate local microplasticity at the scale of the grains. One of the originality of this work is the creation of 3D meshes of polycrystalline aggregates corresponding to zones where fatigue cracks have been observed. 3D data obtained by serial-sectioning are used to reconstruct actual microstructure. The role of the plastic slip activity as a driving force for fatigue crack initiation is discussed according to the comparison between experimental observations and simulations. The approach is applied to 316L type austenitic stainless steels under low-cycle fatigue loading.

Structural Alloys

1983 ◽  
pp. 371-411 ◽  
Author(s):  
H. I. McHenry
Keyword(s):  
Aluminum Alloys ◽  
Physical Properties ◽  
Stainless Steels ◽  
Material Selection ◽  
Selection Process ◽  
Austenitic Stainless Steels ◽  
General Information ◽  
Metals And Alloys ◽  
Mechanical And Physical Properties ◽  
Cryogenic Application

Abstract This chapter discusses the structural alloys being used for cryogenic applications in commercially significant quantities. It emphasizes the practical considerations involved in the material selection process and provides the information necessary to make preliminary selections of alloys most suitable for the intended cryogenic application. The chapter provides general information on a class or group of alloys, their representative mechanical and physical properties, and their fabrication characteristics. The materials covered are austenitic stainless steels, nickel steels, aluminum alloys, and other metals and alloys.

A comparative study of fatigue damage sensing in aluminum alloys using electrical impedance and laser ultrasonic methods

2009 ◽  
Author(s):  
Lindsey Channels ◽  
Debejyo Chakraborty ◽  
Brad Butrym ◽  
Narayan Kovvali ◽  
James Spicer ◽  
...  
Keyword(s):  
Aluminum Alloys ◽  
Comparative Study ◽  
Fatigue Damage ◽  
Electrical Impedance ◽  
Laser Ultrasonic ◽  
Ultrasonic Methods ◽  
Damage Sensing

A continuum damage and discrete crack-based approach for fatigue response and residual strength prediction of notched laminated composites

2017 ◽  
Vol 51 (15) ◽  
pp. 2203-2225 ◽  
Author(s):  
Eugene Fang ◽  
Xiaodong Cui ◽  
Jim Lua
Keyword(s):  
Fatigue Damage ◽  
Air Force ◽  
Residual Strength ◽  
Damage Mechanics ◽  
Finite Thickness ◽  
Strength Prediction ◽  
Continuum Damage ◽  
Failure Initiation ◽  
Damage Characterization ◽  
Discrete Crack

This paper presents a combined continuum damage and discrete crack (CDDC) modelling approach for fatigue damage characterization and post-fatigue residual strength prediction of laminated composite components with a hole. In order to capture both the fatigue cycle-driven material degradation and discrete damage-induced stress concentration and redistribution, an overlapped element approach is developed based on a combined user-defined material (UMAT) and user-defined element (UEL). An Abaqus element coupled with UMAT for fatigue damage characterization is used to detect the location of failure initiation, while the discrete crack network-based (DCN) UEL is applied to insert a crack without remeshing. The intensified stress field induced by the newly inserted matrix crack is used for the evaluation of failure initiation and stiffness degradation. The UMAT for the fatigue analysis has incorporated the stress-cycle ( S-N) curves for the damage evolution characterization associated with matrix and fiber based on the tested S-N curves for plies at their different orientations. A continuum damage mechanics (CDM) approach is used for the fatigue-driven delamination initiation and propagation by insertion of a finite thickness interface layer at each ply interface. Both the blind and recalibrated predictions are performed for specimens of three different layups under the Air Force Tech Scout 1 program. The predicted fatigue failure progression and the stiffness against cycle curves are compared with the test data provided by the Air Force Research Lab (AFRL). In addition, post-fatigue residual strength predictions are performed for these notched specimens under tension and compression.

scholarly journals A Data-Driven Framework for Early-Stage Fatigue Damage Detection in Aluminum Alloys Using Ultrasonic Sensors

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 211
Author(s):  
Susheel Dharmadhikari ◽  
Chandrachur Bhattacharya ◽  
Asok Ray ◽  
Amrita Basak
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Damage Detection ◽  
Fatigue Damage ◽  
Early Stage ◽  
Confocal Microscope ◽  
Recognition Algorithm ◽  
Pattern Recognition Algorithm ◽  
Notched Specimens ◽  
Digital Microscope

The paper presents a coupled machine learning and pattern recognition algorithm to enable early-stage fatigue damage detection in aerospace-grade aluminum alloys. U- and V-notched Al7075-T6 specimens are instrumented with a pair of ultrasonic sensors and, thereafter, tested on an MTS apparatus integrated with a confocal microscope and a digital microscope. The confocal microscope is focused on the notch root of the specimens, whereas the digital microscope is focused on the side of the notch. Two features, viz., the crack opening displacement (COD) and the crack length, are extracted during the tests in addition to the ultrasonic signal data. These signal data are analyzed using a machine learning framework that is built upon a symbolic time-series algorithm. This framework is interrogated for crack detection in the crack coalescence (CC) regime defined by COD of ~3 μm and detected through the confocal microscope. Additionally, the framework is probed in the crack propagation (CP) regime characterized by a crack length of ~0.2 mm and detected via the digital microscope. For the CC regime, training accuracies of 79.82% and 81.94% are achieved, whereas testing accuracies of 68.18% and 74.12% are observed for the U- and V-notched specimens, respectively. For the CP regime, overall training accuracies of 88.3% and 91.85% are observed, and accordingly, testing accuracies of 81.94% and 85.62% are obtained for the U- and V-notched specimens, respectively. The results show that a combined machine learning and pattern recognition algorithm enables robust and reliable fatigue damage detection in aerospace structural components.

scholarly journals Multifeature Extraction of Three-Dimensional Topography of Carbon Steel Specimen during Fatigue Process

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Tao Liu ◽  
Jingxiong Wu ◽  
Jingfa Lei ◽  
Xue Wang ◽  
Bingqi Zhang ◽  
...  
Keyword(s):  
Surface Topography ◽  
Metal Surface ◽  
Fatigue Damage ◽  
Feature Fusion ◽  
Early Stage ◽  
Three Dimensional ◽  
Mean Deviation ◽  
Height Distribution ◽  
Fusion Method ◽  
Fatigue Process

In order to investigate the variation of three-dimensional metal surface topography during fatigue process, a three-dimensional (3D) topography acquisition platform was built with an in situ tensile tester and a three-dimensional profilometer. Q235 steel specimens were chosen as research objects, and the three-dimensional surface topography information at various stages of fatigue damage was obtained. Through the characterization of three-dimensional roughness, combined with surface height distribution and multifractal analysis, the variations of metal surface topography in the fatigue process were described. Results show that the arithmetic mean deviation of the surface (Sa), the width of the multifractal spectrum (Δα), and the mean value of surface height distribution (μ) and its standard deviation (δ) increase nonlinearly with the increase of fatigue cycles. The rate of fatigue damage is slow in the early stage and high in the middle and late stages. The surface height distribution amplitude (A) decreases with the increase of fatigue cycles, which indicates that the height data concentration decreases, and the metal surface becomes uneven. The Bayesian data fusion method was applied to establish a nonlinear mapping between the topography features and the damage, with the above five characteristic parameters (Sa, Δα, A, μ, and δ) as the data layer. Finally, a surface topography feature fusion method is proposed, and a case study is conducted to verify its applicability. The research results can provide reference for fatigue damage assessment.

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