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1. Effect of initial texture on fatigue properties of extruded ZK60 magnesium alloy

   https://doi.org/10.1111/ffe.12792  

   Xiong, Y. ; Gong, X. ; Jiang, Y. ( February 2018 , Fatigue & Fracture of Engineering Materials & Structures)

   The effect of initial texture on cyclic deformation behavior of extruded ZK60 magnesium (Mg) alloy was experimentally investigated under strain‐controlled loading with the strain amplitudes at 4%, 1%, and 0.35%. The testing specimens were taken from extrusion direction (ED), transverse direction (TD), and a material precompressed to 9.4% along the ED (ED_−9.4%). At a high strain amplitude of 4%, the cyclic deformation modes of ED and ED_−9.4%specimens are similar, and they experience twinning exhaustion → slip and detwinning exhaustion → slip during each loading cycle. At a medium strain amplitude of 1%, twinning‐detwinning is involved in the cyclic deformation, but different deformation mechanisms were observed in the 3 different specimens. Partial twinning‐complete detwinning mode dominates the cyclic deformation in the ED specimen, while partial detwinning‐retwinning mode occurs in the ED_−9.4%specimen. For the TD specimen, both basal slip andtension twinning occur during cyclic deformation. At a low strain amplitude of 0.35%, dislocation slips dominate the deformation for the ED specimen with a few observable tension twins. For the ED_−9.4%specimen, initially twined texture increases the ductility of the material and enhances fatigue life as compared with the other 2 specimens.

    

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2. Fatigue Behavior and Cyclic Deformation of Additive Manufactured NiTi

   https://doi.org/10.1016/j.jmatprotec.2017.10.006  

   Bagheri, A. ; Mahtabi, M.J. ; Shamsaei, N. ( October 2017 , Journal of materials processing technology)

   The aim of this study is to experimentally investigate the fatigue behavior of additively manufactured (AM) NiTi (i.e. Nitinol) specimens and compare the results to the wrought material. Additive manufacturing is a technique in which components are fabricated in a layer-by-layer additive process using a sliced CAD model based on the desired geometry. NiTi rods were fabricated in this study using Laser Engineered Net Shaping (LENS), a Direct Laser Deposition (DLD) AM technique. Due to the high plateau stress of the as-fabricated NiTi, all the AM specimens were heat-treated to reduce their plateau stress, close to the one for the wrought material. Two different heat treatment processes, resulting in different stress plateaus, were employed to be able to compare the results in stress- and strain-based fatigue analysis. Straincontrolled constant amplitude pulsating fatigue experiments were conducted on heat-treated AM NiTi specimens at room temperature (~24°C) to investigate their cyclic deformation and fatigue behavior. Fatigue lives of AM NiTi specimens were observed to be shorter than wrought material specifically in the high cycle fatigue regime. Fractography of the fracture surface of fatigue specimens using Scanning Electron Microscopy (SEM) revealed the presence of microstructural defects such as voids, resulting from entrapped gas or lack of fusion and serving as crack initiation sites, to be the main reason for the shorter fatigue lives of AM NiTi specimens. However, the maximum stress level found to be the most influential factor in the fatigue behavior of superelastic NiTi. 

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3. Artificial Neural Networks and Phenomenological Degradation Models for Fatigue Damage Tracking and Life Prediction in Laser Induced Graphene Interlayered Fiberglass Composites

   Nasser, J. ( June 2021 , Smart materials and structures)

   null (Ed.)

   The mechanical properties of fiber reinforced polymer matrix composites are known to gradually deteriorate as fatigue damage accumulates under cyclic loading conditions. While the steady degradation in elastic stiffness throughout fatigue life is a well-established and studied concept, it remains difficult to continuously monitor such structural changes during the service life of many dynamic engineering systems where composite materials are subjected to random and unexpected loading conditions. Recently, laser induced graphene (LIG) has been demonstrated to be a reliable, in-situ strain sensing and damage detection component in fiberglass composites under both quasi-static and dynamic loading conditions. This work investigates the potential of exploiting the piezoresistive properties of LIG interlayered fiberglass composites in order to formulate cumulative damage parameters and predict both damage progression and fatigue life using artificial neural networks (ANNs) and conventional phenomenological models. The LIG interlayered fiberglass composites are subjected to tension–tension fatigue loading, while changes in their elastic stiffness and electrical resistance are monitored through passive measurements. Damage parameters that are defined according to changes in electrical resistance are found to be capable of accurately describing damage progression in LIG interlayered fiberglass composites throughout fatigue life, as they display similar trends to those based on changes in elastic stiffness. These damage parameters are then exploited for predicting the fatigue life and future damage state of fiberglass composites using both trained ANNs and phenomenological degradation and accumulation models in both specimen-to-specimen and cycle-to-cycle schemes. When used in a specimen-to-specimen scheme, the predictions of a two-layer Bayesian regularized ANN with 40 neurons in each layer are found to be at least 60% more accurate than those of phenomenological degradation models, displaying R2 values greater than 0.98 and root mean square error (RMSE) values smaller than 10−3. A two-layer Bayesian regularized ANN with 25 neurons in each layer is also found to yield accurate predictions when used in a cycle-to-cycle scheme, displaying R2 values greater than 0.99 and RMSE values smaller than 2 × 10−4 once more than 30% of the initial measurements are used as inputs. The final results confirm that piezoresistive LIG interlayers are a promising tool for achieving accurate and continuous fatigue life predictions in multifunctional composite structures, specifically when coupled with machine learning algorithms such as ANNs. 

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4. Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites

   https://doi.org/https://doi.org/10.1088/1361-665X/ac093d  

   Nasser, Jalal ; Groo, Lori Anne ; Sodano, Henry ( June 2021 , Smart materials and structures)

   null (Ed.)

   The mechanical properties of fiber reinforced polymer matrix composites are known to gradually deteriorate as fatigue damage accumulates under cyclic loading conditions. While the steady degradation in elastic stiffness throughout fatigue life is a well-established and studied concept, it remains difficult to continuously monitor such structural changes during the service life of many dynamic engineering systems where composite materials are subjected to random and unexpected loading conditions. Recently, laser induced graphene (LIG) has been demonstrated to be a reliable, in-situ strain sensing and damage detection component in fiberglass composites under both quasi-static and dynamic loading conditions. This work investigates the potential of exploiting the piezoresistive properties of LIG interlayered fiberglass composites in order to formulate cumulative damage parameters and predict both damage progression and fatigue life using artificial neural networks (ANNs) and conventional phenomenological models. The LIG interlayered fiberglass composites are subjected to tension–tension fatigue loading, while changes in their elastic stiffness and electrical resistance are monitored through passive measurements. Damage parameters that are defined according to changes in electrical resistance are found to be capable of accurately describing damage progression in LIG interlayered fiberglass composites throughout fatigue life, as they display similar trends to those based on changes in elastic stiffness. These damage parameters are then exploited for predicting the fatigue life and future damage state of fiberglass composites using both trained ANNs and phenomenological degradation and accumulation models in both specimen-to-specimen and cycle-to-cycle schemes. When used in a specimen-to-specimen scheme, the predictions of a two-layer Bayesian regularized ANN with 40 neurons in each layer are found to be at least 60% more accurate than those of phenomenological degradation models, displaying R2 values greater than 0.98 and root mean square error (RMSE) values smaller than 10−3. A two-layer Bayesian regularized ANN with 25 neurons in each layer is also found to yield accurate predictions when used in a cycle-to-cycle scheme, displaying R2 values greater than 0.99 and RMSE values smaller than 2 × 10−4 once more than 30% of the initial measurements are used as inputs. The final results confirm that piezoresistive LIG interlayers are a promising tool for achieving accurate and continuous fatigue life predictions in multifunctional composite structures, specifically when coupled with machine learning algorithms such as ANNs. 

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5. Micromechanical origins of remarkable elongation-to-fracture in AHSS TRIP steels via continuous bending under tension

   https://doi.org/141876  

   Sharma, Rishabh ; Poulin, Camille M. ; Knezevic, Marko ; Miles, Michael P. ; Fullwood, David T. ( August 2021 , Materials science engineering)

   null (Ed.)

   Continuous bending under tension (CBT) is known to achieve elongation-to-failure well above that achieved under a conventional uniaxial simple tension (ST) strain path. However, the detailed mechanism for supplying this increased ductility has not been fully understood. It is clear that the necking that occurs in a typical ST specimen is avoided by constantly moving the region of plastic deformation during the CBT process. The volume of material in which the flow stress is greatest is limited to a moving line where the rollers contact the sheet and superimpose bending stress on the applied tensile load. Hence the condition of a large volume of material experiencing stress greater than the material flow stress, leading to strain localization during ST, is avoided. However, the magnitude of the contribution of this phenomenon to the overall increase in elongation is unclear. In the current set of experiments, an elongation to fracture (ETF) of 4.56x and 3.7x higher than ST was achieved by fine-tuning CBT forming parameters for Q&P 1180 and TBF 1180, respectively. A comparison of maximum local strains near the final point of fracture in ST and CBT sheets via digital image correlation revealed that avoidance of localization of plastic strain during CBT accounts for less than half of the increased elongation in the CBT specimens for two steels containing different amounts of retained austenite (RA). Geometrically necessary dislocation evolution is monitored using high-resolution EBSD (HREBSD) for both strain paths, indicating a lower hardening rate in the CBT samples in the bulk of the sheet, potentially relating to the cyclical nature of the stress in the outer layers of the sheet. Interestingly, the GND evolution in the center of the sheet, which does not experience the same amplitude of cyclic stress, follows the ST behavior more closely than the sheet edges. This appears to contribute to a precipitous drop in residual ductility for the specimens that are pulled in ST after partial CBT processing. The rate of transformation of RA is also tracked in the steels, with a significantly lower rate of transformation during CBT, compared to ST. This suggests that a slower transformation rate achieved under CBT also contributed to higher strain-to-failure levels. 

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