Pub Date : 2024-10-04DOI: 10.1016/j.ijfatigue.2024.108636
Lei Gan , Zhi-Ming Fan , Hao Wu , Zheng Zhong
A data-driven model is presented for accurate prediction of multiaxial fatigue life based upon the principle of transfer learning (TL). The Tradaboost framework is explored to adjust the weights of training data from different sources, actuating information transfer from domain knowledge to the data-driven modeling of multiaxial fatigue life. Subsequently, extensive experimental results tested under the proportional and non-proportional circle loadings are collected for model evaluation. The results demonstrate that the proposed model is more accurate than domain knowledge-based, conventional data-driven, and comparable TL-based models, with a low data requirement, showcasing good applicability for multiaxial fatigue life assessment.
{"title":"Prediction of multiaxial fatigue life with a data-driven knowledge transfer model","authors":"Lei Gan , Zhi-Ming Fan , Hao Wu , Zheng Zhong","doi":"10.1016/j.ijfatigue.2024.108636","DOIUrl":"10.1016/j.ijfatigue.2024.108636","url":null,"abstract":"<div><div>A data-driven model is presented for accurate prediction of multiaxial fatigue life based upon the principle of transfer learning (TL). The Tradaboost framework is explored to adjust the weights of training data from different sources, actuating information transfer from domain knowledge to the data-driven modeling of multiaxial fatigue life. Subsequently, extensive experimental results tested under the proportional and non-proportional circle loadings are collected for model evaluation. The results demonstrate that the proposed model is more accurate than domain knowledge-based, conventional data-driven, and comparable TL-based models, with a low data requirement, showcasing good applicability for multiaxial fatigue life assessment.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"190 ","pages":"Article 108636"},"PeriodicalIF":5.7,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-04DOI: 10.1016/j.ijfatigue.2024.108634
Jian-Xing Mao , Zhi-Fan Xian , Xin Wang , Dian-Yin Hu , Jin-Chao Pan , Rong-Qiao Wang , Shi-Kun Zou , Yang Gao
Cold expansion (CE) serves as a practical surface enhancement to improve the fatigue life of hole structures by improving surface integrity in both macro-scale and micro-scale. Due to the inaccessibility and high cost of experimental measurements, the physical relation between surface integrity and fatigue life are always implicit, serving as the major challenge for accurate life prediction. To address this issue, a novel method is proposed by introducing physical information to traditional data-driven method, where surface integrity enriched by multi-scale simulation is mapped to fatigue life via machine learning (ML) mechanism. As integrated to four typical ML algorithms, the proposed physics-enhanced data-driven method exhibit outstanding capability for accuracy improvement, decreasing the scatter band by amplitude between 27.3 % and 71.4 %. The proposed method offers a promising option for fatigue life prediction on surface treated structures with limited physical information.
冷膨胀（CE）是一种实用的表面强化技术，可通过改善宏观和微观尺度的表面完整性来提高孔结构的疲劳寿命。由于实验测量的不可得性和高成本，表面完整性与疲劳寿命之间的物理关系总是隐含的，这成为准确预测寿命的主要挑战。为解决这一问题，我们提出了一种新方法，即在传统的数据驱动方法中引入物理信息，通过机器学习（ML）机制将多尺度模拟丰富的表面完整性映射到疲劳寿命。与四种典型的 ML 算法相比，所提出的物理增强型数据驱动方法在提高精度方面表现突出，其散射带的幅度降低了 27.3% 到 71.4%。所提出的方法为物理信息有限的表面处理结构的疲劳寿命预测提供了一种有前途的选择。
{"title":"Fatigue life prediction of cold expansion hole using physics-enhanced data-driven method","authors":"Jian-Xing Mao , Zhi-Fan Xian , Xin Wang , Dian-Yin Hu , Jin-Chao Pan , Rong-Qiao Wang , Shi-Kun Zou , Yang Gao","doi":"10.1016/j.ijfatigue.2024.108634","DOIUrl":"10.1016/j.ijfatigue.2024.108634","url":null,"abstract":"<div><div>Cold expansion (CE) serves as a practical surface enhancement to improve the fatigue life of hole structures by improving surface integrity in both macro-scale and micro-scale. Due to the inaccessibility and high cost of experimental measurements, the physical relation between surface integrity and fatigue life are always implicit, serving as the major challenge for accurate life prediction. To address this issue, a novel method is proposed by introducing physical information to traditional data-driven method, where surface integrity enriched by multi-scale simulation is mapped to fatigue life via machine learning (ML) mechanism. As integrated to four typical ML algorithms, the proposed physics-enhanced data-driven method exhibit outstanding capability for accuracy improvement, decreasing the scatter band by amplitude between 27.3 % and 71.4 %. The proposed method offers a promising option for fatigue life prediction on surface treated structures with limited physical information.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"190 ","pages":"Article 108634"},"PeriodicalIF":5.7,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1016/j.ijfatigue.2024.108635
Qingtong Wang , Jingtai Yu , Bingbing Li , Yuguang Li , Kang Wang , Xu Chen
The continuous fatigue tests and creep-fatigue tests with the imposition of strain hold at the peak strain were conducted at 700 °C on Inconel 617 alloy. The strain amplitude of 0.25 %, 0.30 %, 0.35 %, 0.40 %, and hold time of 60 s, 600 s and 1800 s were used. The cyclic deformation behavior and dynamic strain aging (DSA) were discussed. The strain localization, dislocation substructure and precipitation behavior were carefully characterized, which provided physical information to understand the cyclic deformation behavior. The dominant damage mechanism and damage interaction, responsible for the cracking behavior were identified based on the fracture surface observation and secondary cracks morphology. The cyclic life saturation effect was comprehensively elucidated from the perspective of macroscopic mechanical response and microscopic deformation mechanism.
{"title":"Mechanisms of deformation, damage and life behavior of inconel 617 alloy during creep-fatigue interaction at 700 °C","authors":"Qingtong Wang , Jingtai Yu , Bingbing Li , Yuguang Li , Kang Wang , Xu Chen","doi":"10.1016/j.ijfatigue.2024.108635","DOIUrl":"10.1016/j.ijfatigue.2024.108635","url":null,"abstract":"<div><div>The continuous fatigue tests and creep-fatigue tests with the imposition of strain hold at the peak strain were conducted at 700 °C on Inconel 617 alloy. The strain amplitude of 0.25 %, 0.30 %, 0.35 %, 0.40 %, and hold time of 60 s, 600 s and 1800 s were used. The cyclic deformation behavior and dynamic strain aging (DSA) were discussed. The strain localization, dislocation substructure and precipitation behavior were carefully characterized, which provided physical information to understand the cyclic deformation behavior. The dominant damage mechanism and damage interaction, responsible for the cracking behavior were identified based on the fracture surface observation and secondary cracks morphology. The cyclic life saturation effect was comprehensively elucidated from the perspective of macroscopic mechanical response and microscopic deformation mechanism.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"190 ","pages":"Article 108635"},"PeriodicalIF":5.7,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1016/j.ijfatigue.2024.108637
Jianli Zhou , Yixu Zhang , Ni Wang , Wenjie Gao , Ling’en Liu , Liang Tang , Jin Wang , Junxia Lu , Yuefei Zhang , Ze Zhang
Nickel-based single crystal superalloys, as engine blade materials, are prone to fatigue damage due to repeated startups and shutdowns. Therefore, monitoring and quantitatively estimating fatigue cracks are essential for engineering structures to ensure safety. In this study, we proposed a method for fatigue crack segmentation and damage prediction based on deep learning and in-situ high-temperature scanning electron microscopy (SEM). Sequential SEM images describing the crack initiation and propagation under near-service conditions were obtained by conducting in-situ high-temperature fatigue experiments. A fatigue crack dataset with high-quality was thus constructed for further dynamic and real-time crack segmentation and damage assessment. Deep learning-based models were used to segment cracks and predict damage behavior (i.e., crack area, length, width, and stress intensity factors) at future based on prior damage information. The short-term and long-term damage prediction capability were validated by comparing model performance when predicting damage at different future time points. Additionally, we compared the model performance when predicting damage at specific time point based on varying lengths of input sequence. Results demonstrated that the model could segment cracks and scales with different sizes accurately. The model performed well in short-term damage prediction. The long-term predictive performance showed decrease than that of short-term, which could be improved by feeding long length of input sequence. The proposed approach demonstrates the feasibility and effectiveness of deep learning-based crack segmentation and damage prediction, which facilitates the move toward real-time analysis and rapid diagnosis of material damage in the future.
{"title":"Prediction of fatigue crack damage using in-situ scanning electron microscopy and machine learning","authors":"Jianli Zhou , Yixu Zhang , Ni Wang , Wenjie Gao , Ling’en Liu , Liang Tang , Jin Wang , Junxia Lu , Yuefei Zhang , Ze Zhang","doi":"10.1016/j.ijfatigue.2024.108637","DOIUrl":"10.1016/j.ijfatigue.2024.108637","url":null,"abstract":"<div><div>Nickel-based single crystal superalloys, as engine blade materials, are prone to fatigue damage due to repeated startups and shutdowns. Therefore, monitoring and quantitatively estimating fatigue cracks are essential for engineering structures to ensure safety. In this study, we proposed a method for fatigue crack segmentation and damage prediction based on deep learning and in-situ high-temperature scanning electron microscopy (SEM). Sequential SEM images describing the crack initiation and propagation under near-service conditions were obtained by conducting in-situ high-temperature fatigue experiments. A fatigue crack dataset with high-quality was thus constructed for further dynamic and real-time crack segmentation and damage assessment. Deep learning-based models were used to segment cracks and predict damage behavior (i.e., crack area, length, width, and stress intensity factors) at future based on prior damage information. The short-term and long-term damage prediction capability were validated by comparing model performance when predicting damage at different future time points. Additionally, we compared the model performance when predicting damage at specific time point based on varying lengths of input sequence. Results demonstrated that the model could segment cracks and scales with different sizes accurately. The model performed well in short-term damage prediction. The long-term predictive performance showed decrease than that of short-term, which could be improved by feeding long length of input sequence. The proposed approach demonstrates the feasibility and effectiveness of deep learning-based crack segmentation and damage prediction, which facilitates the move toward real-time analysis and rapid diagnosis of material damage in the future.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"190 ","pages":"Article 108637"},"PeriodicalIF":5.7,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-03DOI: 10.1016/j.ijfatigue.2024.108629
Marcus C. Lam , Carla M.C. Cruz , Alexis Loustaunau , Anthony Koumpias , Amberlee S. Haselhuhn , Andrew Wessman , Sammy Tin
Fatigue resistance at elevated temperatures is crucial for certifying aerospace structures additively manufactured by the laser beam powder bed fusion (PBF-LB) method with IN718 superalloy. This study employed a multi-step, supersolvus heat treatment process with hot isostatic pressing (HIP), called RHSA, to minimize pores and brittle phases. Stress intensity factor (△K) calculations using data from X-ray computed tomography and shape factors referencing finite element analysis (FEA) studies confirmed the suppression of △K below the threshold of conventional IN718 (∼5 MPa√m), shifting fatigue behavior to grain-structure-dominated. Despite a very high twin boundary (TB) fraction (>70%), fatigue tests at 450°C and R = 0.1 demonstrated low scatter. Slip trace analysis and high-resolution electron backscatter diffraction (EBSD) revealed that TB-induced strain concentration became prominent only at high △K, causing cracking at 45⁰ to the loading direction. The randomly oriented TBs with higher angles (60⁰) compared to high-angle grain boundaries (HAGBs) (30–40⁰) likely enhanced slip resistance and provided a net strengthening effect, which can explain the lower-than-average TB% along fracture paths. These insights suggest that a high TB fraction is not detrimental if fatigue stress is not excessive, alleviating concerns about annealing twins during defect minimization in AM IN718, allowing novel processes to improve fatigue resistance in PBF-LB IN718.
{"title":"Fatigue mechanisms at 450°C of a highly twined (>70%) and HIP-densified IN718 superalloy additively manufactured by laser beam powder bed fusion","authors":"Marcus C. Lam , Carla M.C. Cruz , Alexis Loustaunau , Anthony Koumpias , Amberlee S. Haselhuhn , Andrew Wessman , Sammy Tin","doi":"10.1016/j.ijfatigue.2024.108629","DOIUrl":"10.1016/j.ijfatigue.2024.108629","url":null,"abstract":"<div><div>Fatigue resistance at elevated temperatures is crucial for certifying aerospace structures additively manufactured by the laser beam powder bed fusion (PBF-LB) method with IN718 superalloy. This study employed a multi-step, supersolvus heat treatment process with hot isostatic pressing (HIP), called RHSA, to minimize pores and brittle phases. Stress intensity factor (△K) calculations using data from X-ray computed tomography and shape factors referencing finite element analysis (FEA) studies confirmed the suppression of △K below the threshold of conventional IN718 (∼5 MPa√m), shifting fatigue behavior to grain-structure-dominated. Despite a very high twin boundary (TB) fraction (>70%), fatigue tests at 450°C and R = 0.1 demonstrated low scatter. Slip trace analysis and high-resolution electron backscatter diffraction (EBSD) revealed that TB-induced strain concentration became prominent only at high △K, causing cracking at 45⁰ to the loading direction. The randomly oriented TBs with higher angles (60⁰) compared to high-angle grain boundaries (HAGBs) (30–40⁰) likely enhanced slip resistance and provided a net strengthening effect, which can explain the lower-than-average TB% along fracture paths. These insights suggest that a high TB fraction is not detrimental if fatigue stress is not excessive, alleviating concerns about annealing twins during defect minimization in AM IN718, allowing novel processes to improve fatigue resistance in PBF-LB IN718.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"190 ","pages":"Article 108629"},"PeriodicalIF":5.7,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-02DOI: 10.1016/j.ijfatigue.2024.108632
Dongxu Zhang , Yonghua Li , Zhenliang Fu , Yufeng Wang , Kangjun Xu
With the increasing service life of bogie frames, the risk of fatigue failure becomes significant, making fatigue reliability analysis crucial for ensuring operational safety. However, accurately analyzing fatigue reliability presents a significant challenge with uncertain factors such as load fluctuations, unstable material shaping, and dimensional manufacturing deviations. To address this, this paper establishes a comprehensive active learning reliability framework based on surrogate models, enabling high-fidelity modeling and precise fatigue reliability analysis of welded frames under parameter uncertainty. The material utilization method was developed using APDL for secondary development to efficiently evaluate frame fatigue failure indicators. The effectiveness of this method was validated by combining the improved Goodman-Smith fatigue limit diagram and test bench fatigue tests, which helped identify the locations on the frame most prone to fatigue fractures. An Atom Search Optimization-BP Neural Network surrogate model was established with the objective of maximum material utilization, and the fatigue reliability of the bogie frame was obtained by combining the active learning function and the Monte Carlo method. The results show that the uncertainty design parameters greatly impact the fatigue reliability of critical welded structures. The proposed method improves the accuracy and efficiency of the fatigue reliability analysis of the bogie frame.
{"title":"Fatigue reliability analysis of bogie frames considering parameter uncertainty","authors":"Dongxu Zhang , Yonghua Li , Zhenliang Fu , Yufeng Wang , Kangjun Xu","doi":"10.1016/j.ijfatigue.2024.108632","DOIUrl":"10.1016/j.ijfatigue.2024.108632","url":null,"abstract":"<div><div>With the increasing service life of bogie frames, the risk of fatigue failure becomes significant, making fatigue reliability analysis crucial for ensuring operational safety. However, accurately analyzing fatigue reliability presents a significant challenge with uncertain factors such as load fluctuations, unstable material shaping, and dimensional manufacturing deviations. To address this, this paper establishes a comprehensive active learning reliability framework based on surrogate models, enabling high-fidelity modeling and precise fatigue reliability analysis of welded frames under parameter uncertainty. The material utilization method was developed using APDL for secondary development to efficiently evaluate frame fatigue failure indicators. The effectiveness of this method was validated by combining the improved Goodman-Smith fatigue limit diagram and test bench fatigue tests, which helped identify the locations on the frame most prone to fatigue fractures. An Atom Search Optimization-BP Neural Network surrogate model was established with the objective of maximum material utilization, and the fatigue reliability of the bogie frame was obtained by combining the active learning function and the Monte Carlo method. The results show that the uncertainty design parameters greatly impact the fatigue reliability of critical welded structures. The proposed method improves the accuracy and efficiency of the fatigue reliability analysis of the bogie frame.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"190 ","pages":"Article 108632"},"PeriodicalIF":5.7,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-02DOI: 10.1016/j.ijfatigue.2024.108626
Yingxuan Dong , Xiaofa Yang , Dongdong Chang , Qun Li
To address the limitations of traditional methods (such as the S-N curves and Paris’s law) in evaluating the fatigue life of multi-defect materials, this study developed a fracture mechanics-based physics-informed neural network (PINN) to predict the lifetime of multi-defect materials using cyclic loading (Δσ) and the equivalent damage area (AD). Influences of multiple defects were unified characterized through the equivalent damage area, which was calculated based on the M−integral fatigue model. This model reflects the energy evolution of multi-defect fatigue damage. By embedding the prior knowledge of fracture mechanics derived from the M−integral fatigue model into the loss function of PINN, crucial physical information was captured during the training progress, enhancing the interpretability of the neural network. By integrating the advantage of the M−integral fatigue model in characterizing the fatigue performance of multi-defect materials and the nonlinear fitting ability of neural networks, the proposed approach effectively improves the generalization ability and predictive accuracy of limited fatigue data. The presented PINN models accurately forecast the fatigue life of multi-defect materials, with a squared correlation coefficient (R2) exceeding 0.9. The presented methodological framework addresses the existing gap in methods for evaluating the fatigue performance of multi-defect materials and reliance on fatigue testing.
{"title":"Predicting fatigue life of multi-defect materials using the fracture mechanics-based physics-informed neural network framework","authors":"Yingxuan Dong , Xiaofa Yang , Dongdong Chang , Qun Li","doi":"10.1016/j.ijfatigue.2024.108626","DOIUrl":"10.1016/j.ijfatigue.2024.108626","url":null,"abstract":"<div><div>To address the limitations of traditional methods (such as the S-N curves and Paris’s law) in evaluating the fatigue life of multi-defect materials, this study developed a fracture mechanics-based physics-informed neural network (PINN) to predict the lifetime of multi-defect materials using cyclic loading (Δσ) and the equivalent damage area (<em>A<sub>D</sub></em>). Influences of multiple defects were unified characterized through the equivalent damage area, which was calculated based on the <em>M</em>−integral fatigue model. This model reflects the energy evolution of multi-defect fatigue damage. By embedding the prior knowledge of fracture mechanics derived from the <em>M</em>−integral fatigue model into the loss function of PINN, crucial physical information was captured during the training progress, enhancing the interpretability of the neural network. By integrating the advantage of the <em>M</em>−integral fatigue model in characterizing the fatigue performance of multi-defect materials and the nonlinear fitting ability of neural networks, the proposed approach effectively improves the generalization ability and predictive accuracy of limited fatigue data. The presented PINN models accurately forecast the fatigue life of multi-defect materials, with a squared correlation coefficient (<em>R</em><sup>2</sup>) exceeding 0.9. The presented methodological framework addresses the existing gap in methods for evaluating the fatigue performance of multi-defect materials and reliance on fatigue testing.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"190 ","pages":"Article 108626"},"PeriodicalIF":5.7,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-02DOI: 10.1016/j.ijfatigue.2024.108631
Qianyu Xia , Chenhao Ji , Zhixin Zhan , Xiaojia Wang , Zhi Bian , Weiping Hu , Qingchun Meng
The bronze/steel diffusion welded (BSDW) bimetallic material is often applied in the rotors of piston pumps to withstand complex alternating loads under high-speed operating conditions. Although diffusion welding is a type of solid-phase welding method to achieve high-quality material connections, the fatigue problems still deserve our attention, especially the very high cycle fatigue (VHCF) and high cycle fatigue (HCF) problems. However, due to the high cost of obtaining data, it is necessary to find an efficient and high-precision fatigue life prediction method for diffusion welded materials with a small sample size. In this study, a novel method continuum damage mechanics − transfer learning method (CDM-TLM) for fatigue life prediction of BSDW material is proposed based on the transfer learning (TL) and continuum damage mechanics − finite element method (CDM-FEM). In comparison with the test results, the predicted values of BSDW material fatigue life all fall within the twice error band of the median values of the test life. The influence of frozen layers during TL and training samples in source and target models on the prediction performance is further discussed. CDM-TLM is an effective life prediction method for high-precision life prediction of BSDW material with a small sample size.
{"title":"Damage mechanics coupled with a transfer learning approach for the fatigue life prediction of bronze/steel diffusion welded bimetallic material","authors":"Qianyu Xia , Chenhao Ji , Zhixin Zhan , Xiaojia Wang , Zhi Bian , Weiping Hu , Qingchun Meng","doi":"10.1016/j.ijfatigue.2024.108631","DOIUrl":"10.1016/j.ijfatigue.2024.108631","url":null,"abstract":"<div><div>The bronze/steel diffusion welded (BSDW) bimetallic material is often applied in the rotors of piston pumps to withstand complex alternating loads under high-speed operating conditions. Although diffusion welding is a type of solid-phase welding method to achieve high-quality material connections, the fatigue problems still deserve our attention, especially the very high cycle fatigue (VHCF) and high cycle fatigue (HCF) problems. However, due to the high cost of obtaining data, it is necessary to find an efficient and high-precision fatigue life prediction method for diffusion welded materials with a small sample size. In this study, a novel method continuum damage mechanics − transfer learning method (CDM-TLM) for fatigue life prediction of BSDW material is proposed based on the transfer learning (TL) and continuum damage mechanics − finite element method (CDM-FEM). In comparison with the test results, the predicted values of BSDW material fatigue life all fall within the twice error band of the median values of the test life. The influence of frozen layers during TL and training samples in source and target models on the prediction performance is further discussed. CDM-TLM is an effective life prediction method for high-precision life prediction of BSDW material with a small sample size.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"190 ","pages":"Article 108631"},"PeriodicalIF":5.7,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01DOI: 10.1016/j.ijfatigue.2024.108630
Kenneth M. Peterson , Michelle Harr , Adam Pilchak , S. Lee Semiatin , Nathan Levkulich , Jacob Ruff , Darren C. Pagan
Grain-scale stress redistribution events are characterized within the Ti-6Al-4V (Ti64) hexagonal close-packed phase using far-field high-energy X-ray diffraction microscopy. Specimens were deformed in monotonic uniaxial tension and under cyclic tensile loading with approximately 7000 grains probed in each specimen. Analyses focused on the evolution of the resolved shear stresses applied to the basal , prismatic , and pyramidal slip systems, as well as normal stresses applied to basal planes, within individual grains. Slip system softening is observed in the basal and prismatic slip systems across the ensemble, while hardening is observed for the pyramidal slip systems during both monotonic and cyclic loading. In addition, discrete stress redistribution events in which increases of normal stresses in grains not favorably oriented for slip that may lead to crack initiation are analyzed. It is observed that these increases in normal stresses are correlated to crystallographic slip in multiple neighboring grains favorably oriented for slip.
利用远场高能 X 射线衍射显微镜研究了 Ti-6Al-4V (Ti64) 六方紧密堆积 α 相中晶粒尺度应力再分布事件的特征。试样在单调单轴拉伸和循环拉伸载荷下变形，每个试样中探测了约 7000 个晶粒。分析的重点是单个晶粒内施加于基面〈a〉、棱柱形〈a〉和金字塔形〈c+a〉滑移系统的分辨剪应力以及施加于基面的法向应力的演变。在整个组合中，基面〈a〉和棱柱形〈a〉滑移系统都出现了软化现象，而在单调和循环加载过程中，金字塔形〈c+a〉滑移系统都出现了硬化现象。此外，还分析了离散应力再分布事件，在这些事件中，不利于滑移的晶粒法向应力增加，可能导致裂纹萌生。据观察，这些法向应力的增加与多个相邻晶粒的结晶滑移相关，而这些晶粒的滑移方向有利于滑移。
{"title":"3D in situ observations of stress redistribution in Ti-6Al-4V within rogue grain neighborhoods during monotonic and cyclic loading","authors":"Kenneth M. Peterson , Michelle Harr , Adam Pilchak , S. Lee Semiatin , Nathan Levkulich , Jacob Ruff , Darren C. Pagan","doi":"10.1016/j.ijfatigue.2024.108630","DOIUrl":"10.1016/j.ijfatigue.2024.108630","url":null,"abstract":"<div><div>Grain-scale stress redistribution events are characterized within the Ti-6Al-4V (Ti64) hexagonal close-packed <span><math><mi>α</mi></math></span> phase using far-field high-energy X-ray diffraction microscopy. Specimens were deformed in monotonic uniaxial tension and under cyclic tensile loading with approximately 7000 grains probed in each specimen. Analyses focused on the evolution of the resolved shear stresses applied to the basal <span><math><mrow><mo>〈</mo><mi>a</mi><mo>〉</mo></mrow></math></span>, prismatic <span><math><mrow><mo>〈</mo><mi>a</mi><mo>〉</mo></mrow></math></span>, and pyramidal <span><math><mrow><mo>〈</mo><mi>c</mi><mo>+</mo><mi>a</mi><mo>〉</mo></mrow></math></span> slip systems, as well as normal stresses applied to basal planes, within individual grains. Slip system softening is observed in the basal <span><math><mrow><mo>〈</mo><mi>a</mi><mo>〉</mo></mrow></math></span> and prismatic <span><math><mrow><mo>〈</mo><mi>a</mi><mo>〉</mo></mrow></math></span> slip systems across the ensemble, while hardening is observed for the pyramidal <span><math><mrow><mo>〈</mo><mi>c</mi><mo>+</mo><mi>a</mi><mo>〉</mo></mrow></math></span> slip systems during both monotonic and cyclic loading. In addition, discrete stress redistribution events in which increases of normal stresses in grains not favorably oriented for slip that may lead to crack initiation are analyzed. It is observed that these increases in normal stresses are correlated to crystallographic slip in multiple neighboring grains favorably oriented for slip.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"190 ","pages":"Article 108630"},"PeriodicalIF":5.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates nanoindentation and fatigue crack growth rates of pure aluminium and aluminium hollow glass microsphere metal matrix composite (Al + HGM MMCs) cast plates. Using the stir casting method, pure aluminium, and Al + HGM MMCs were fabricated with hollow glass microsphere (HGM) additions ranging from 5 % to 30 % by weight. Nanoindentation techniques were utilized to assess fundamental mechanical properties such as hardness and elastic modulus. This study primarily focuses on analyzing fatigue crack growth behaviour within the linear region of da/dN vs. ΔK graphs for these stir-cast plates. Chevron-notch CT specimens were prepared following ASTM E-647 standards, and the constant amplitude increasing ΔK method was employed to generate Paris curves. Furthermore, the research investigated the influence of stress ratios (R=0.1, 0.2, and 0.3) on the fatigue crack growth rate in both Pure Al and Al + HGM MMCs. The study also determined the threshold and critical stress intensity factor ranges (ΔKth and ΔKc) for these plates. Additionally, Paris constants (C, m) were calculated to characterize the fatigue behaviour of the cast plates. X-ray diffraction analysis was conducted to reveal dislocation densities, crystalline sizes, and micro-strain responses of the fatigue-fractured specimens. Moreover, SEM fractography analysis provided insights into the fracture behaviour and crack branching observed in both pure aluminium and Al + HGM MMCs plates.
{"title":"Fatigue crack growth rate behaviour of aluminium matrix composites reinforced with hollow glass microsphere","authors":"Karthick Ganesan , Ganesan Somasundaram Marimuthu , Shekhar Hansda , Vasantha Kumar Ramesh , Satheesh Mani , Balaji Thangapandi","doi":"10.1016/j.ijfatigue.2024.108628","DOIUrl":"10.1016/j.ijfatigue.2024.108628","url":null,"abstract":"<div><div>This study investigates nanoindentation and fatigue crack growth rates of pure aluminium and aluminium hollow glass microsphere metal matrix composite (Al + HGM MMCs) cast plates. Using the stir casting method, pure aluminium, and Al + HGM MMCs were fabricated with hollow glass microsphere (HGM) additions ranging from 5 % to 30 % by weight. Nanoindentation techniques were utilized to assess fundamental mechanical properties such as hardness and elastic modulus. This study primarily focuses on analyzing fatigue crack growth behaviour within the linear region of da/dN vs. ΔK graphs for these stir-cast plates. Chevron-notch CT specimens were prepared following ASTM E-647 standards, and the constant amplitude increasing ΔK method was employed to generate Paris curves. Furthermore, the research investigated the influence of stress ratios (R=0.1, 0.2, and 0.3) on the fatigue crack growth rate in both Pure Al and Al + HGM MMCs. The study also determined the threshold and critical stress intensity factor ranges (ΔK<sub>th</sub> and ΔK<sub>c</sub>) for these plates. Additionally, Paris constants (C, m) were calculated to characterize the fatigue behaviour of the cast plates. X-ray diffraction analysis was conducted to reveal dislocation densities, crystalline sizes, and micro-strain responses of the fatigue-fractured specimens. Moreover, SEM fractography analysis provided insights into the fracture behaviour and crack branching observed in both pure aluminium and Al + HGM MMCs plates.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":"190 ","pages":"Article 108628"},"PeriodicalIF":5.7,"publicationDate":"2024-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142424219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号