The Experts below are selected from a list of 5685 Experts worldwide ranked by ideXlab platform
Z F Zhang - One of the best experts on this subject based on the ideXlab platform.
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improving Fatigue Property of metallic glass by tailoring the microstructure to suppress shear band formation
Materialia, 2019Co-Authors: R.t. Qu, H F Zhang, Songcui Wu, Z F ZhangAbstract:Abstract Metallic glass (MG) usually exhibits poor Fatigue Property, limiting its application as structural materials. Although Fatigue failure happens at stress level much lower than the yield strength of MGs, shear band has been found to be one of main origins of Fatigue crack initiation under high stress level. In this work, to enhance the Fatigue Property of MG, we proposed and verified a new strategy through tailoring the microstructure to suppress the shear band formation and then to impede the Fatigue crack initiation, in contrast to the previous way of proliferating shear bands. Heat treatment, which is controllable, nondestructive and easy to conduct in practice, was utilized to adjust the MG microstructure. In order to make shear band difficult to initiate, annealing before glass transition temperature was performed. To determine better microstructure of MG with enhanced Fatigue Property, we employed the results of simple mechanical tests including uniaxial tension, compression and notch tension as feedbacks. The treated MG materials with improved elastic limit but only slightly decreased notch toughness than the as-cast samples were expected to show enhanced Fatigue Property, which has been examined by subsequent experiments. The enhanced elastic limit implies the improved resistances for shear band formation and thus Fatigue crack initiation, while the still high notch toughness guarantees the large tolerance for Fatigue failure. The present strategy provides a promising way for enhancing the Fatigue endurance Property of MGs without the requirement of intrinsically large plasticity, which may thus be beneficial for the structural application of most MGs with high glass forming ability.
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improving the Fatigue Property of metallic glass by tailoring the microstructure to suppress shear band formation
Social Science Research Network, 2019Co-Authors: Xiaohan Wang, H F Zhang, Z F Zhang, Zhenyun ZhuAbstract:Metallic glass (MG) usually exhibits poor Fatigue Property, limiting its application as structural materials. Shear bands play a key role in Fatigue crack initiation and propagation of MG. In this work, to enhance the Fatigue Property of MG, we proposed and verified a new strategy of suppressing the shear band initiation and then impeding the Fatigue crack formation through relaxing the microstructure, in contrast to the previous one of proliferating shear bands through toughening MG. Moreover, simple quasi-static tests including uniaxial tension, compression and notch tension, are employed as feedback to estimate the best microstructure after relaxing and then the optimal Fatigue Property of MG. It is found that if the relaxed samples exhibit higher compressive elastic limit and tensile strength, but only slightly decreased notch toughness than the as-cast samples, the Fatigue Property of relaxed samples can be obviously enhanced. The present strategy provides a promising way for enhancing the Fatigue endurance Property of MGs without the requirement of intrinsically large plasticity, which may thus be beneficial for the structural application of most MGs with high glass forming ability.
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thermo mechanical Fatigue Property and life prediction of vermicular graphite iron
Materials Science and Engineering A-structural Materials Properties Microstructure and Processing, 2017Co-Authors: Mengxiao Zhang, J C Pang, Y Qiu, Mingna Wang, Z F ZhangAbstract:Abstract Thermo-mechanical Fatigue (TMF) failure is the major problem of the cylinder head subjected to combined variations in temperature and loading during operation. This study mainly focuses on the TMF Property and the life prediction of vermicular graphite iron (VGI). The ferrite clusters can be easily found from the microstructure and fractography images. Compared with the TMF experimental data testing at 125–400 ℃ and 125–500 ℃, significant cyclic hardening occurs in the former and slight hardening does in the latter. Depending on the difference in the damage mechanism between TMF and iso-thermal low-cycle Fatigue (LCF), based on the hysteresis energy, a life prediction method has been proposed at first. By the minimum amount of LCF and TMF tests, the present method can predict the TMF life rapidly, accurately and cheaply. And based on the difference in the Fatigue crack propagation thresholds between pearlite and ferrite, the fracture mechanism of TMF was also discussed.
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Fatigue fracture behaviour of spot welded b1500hs steel under tensile shear load
Fatigue & Fracture of Engineering Materials & Structures, 2015Co-Authors: Z F Zhang, Baoquan Wang, Q Q Duan, Ge Yao, J C Pang, L WangAbstract:The microstructural characterizations, micro-hardness measurements and Fatigue tests of B1500HS steel spot welded tensile-shear specimens were performed. The high hardness values of base material (470 HV) and nugget (515 HV) are closely related to the domi- nant formation of martensitic microstructures, while the occurrence of soft zone is the result of the formation of ferrite phases in inter-critical heat-affected zone (HAZ), as well as martensite tempering in sub-critical HAZ. The Fatigue failure modes involve the frac- ture along the circumference or along the direction of width. The Fatigue Property of spot welded B1500HS is found to be better than that of spot welded M190 because of the thicker sheet and suitable nugget size, which follows the standard rule of 5t 0.5 , where
Hua Li - One of the best experts on this subject based on the ideXlab platform.
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Predictive models for Fatigue Property of laser powder bed fusion stainless steel 316L
Materials and Design, 2018Co-Authors: Meng Zhang, David Hardacre, Xiang Zhang, Hua LiAbstract:The selection of appropriate processing parameters is crucial for producing parts with target properties via the laser powder bed fusion (L-PBF) process. In this work, the Fatigue properties of L-PBF stainless steel 316L under controlled changes in laser power and scan speed were studied by employing the statistical response surface method. Processing regions corresponding to different Fatigue failure mechanisms were identified. The optimum Fatigue properties are associated with crack initiation from microstructure defect, which, by acting as the weakest link, creates enhanced porosity-tolerance at applied stress approaching the Fatigue limit. Deviations from the optimum processing condition lead to strength degradation and porosity-driven cracking. Based on the observed relations between microstructural features and failure behaviour, a processing-independent Fatigue prediction model was proposed. The microstructure-driven failure was modelled by a reference S-N curve where the intrinsic effect of microstructure inhomogeneity was accounted for by applying a reduction factor on Fatigue life. For the porosity-driven failure, high cycle Fatigue life follows an inverse-square-root relation with porosity fraction. This relation was incorporated into the Basquin equation for predicting the Fatigue strength parameters.
Meng Zhang - One of the best experts on this subject based on the ideXlab platform.
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application of data science approach to Fatigue Property assessment of laser powder bed fusion stainless steel 316l
2019Co-Authors: Meng Zhang, Xiang Zhang, C N Sun, P C Goh, J Wei, David HardacreAbstract:The adaptive neuro-fuzzy inference system (ANFIS) was applied for Fatigue life prediction of laser powder bed fusion (L-PBF) stainless steel 316L. The model was evaluated using a dataset containing 111 Fatigue data derived from 14 independent S-N curves. By using porosity fraction, tensile strength and cyclic stress as the inputs, the fuzzy rules defining the relations between these parameters and Fatigue life were obtained for a Sugeno-type ANFIS model. The computationally derived fuzzy sets agree well with understanding of the Fatigue failure mechanism, and the model demonstrates good prediction accuracy for both the training and test data. For parts made by the emerging L-PBF process where sufficient knowledge of the material behavior is still lacking, the ANFIS approach offers clear advantage over classical neural network, as the use of fuzzy logics allows more physically meaningful system design and result validation.
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Predictive models for Fatigue Property of laser powder bed fusion stainless steel 316L
Materials and Design, 2018Co-Authors: Meng Zhang, David Hardacre, Xiang Zhang, Hua LiAbstract:The selection of appropriate processing parameters is crucial for producing parts with target properties via the laser powder bed fusion (L-PBF) process. In this work, the Fatigue properties of L-PBF stainless steel 316L under controlled changes in laser power and scan speed were studied by employing the statistical response surface method. Processing regions corresponding to different Fatigue failure mechanisms were identified. The optimum Fatigue properties are associated with crack initiation from microstructure defect, which, by acting as the weakest link, creates enhanced porosity-tolerance at applied stress approaching the Fatigue limit. Deviations from the optimum processing condition lead to strength degradation and porosity-driven cracking. Based on the observed relations between microstructural features and failure behaviour, a processing-independent Fatigue prediction model was proposed. The microstructure-driven failure was modelled by a reference S-N curve where the intrinsic effect of microstructure inhomogeneity was accounted for by applying a reduction factor on Fatigue life. For the porosity-driven failure, high cycle Fatigue life follows an inverse-square-root relation with porosity fraction. This relation was incorporated into the Basquin equation for predicting the Fatigue strength parameters.
Jonathan C Knowles - One of the best experts on this subject based on the ideXlab platform.
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the biaxial flexural strength and Fatigue Property of lava y tzp dental ceramic
Dental Materials, 2007Co-Authors: Piyapanna Pittayachawan, Ailbhe Mcdonald, Aviva Petrie, Jonathan C KnowlesAbstract:Abstract Objectives The development of yttrium oxide partially stabilized zirconia (Y-TZP) has allowed the use of ceramic in load-bearing sites. The aim of this study was to evaluate and compare the biaxial flexural strength, hardness and Fatigue life of colored and uncolored zirconia in the LAVA™ system. Materials and methods Eight groups (n = 30) of standardized disc specimens (15 mm × 1.3 mm) were used to examine the biaxial flexural strength (ISO 6872 standard) using a Dartec HC10 Fatigue Tester (Zwick Ltd., UK) and Vickers hardness was also measured. The uncolored, FS4, FS7 groups were also submitted to dynamic Fatigue testing to produce stress–number curves. The strength reliability was analyzed using Weibull distribution. Results All groups had a mean biaxial flexural strength, hardness and Weibull modulus (m) of approximately 1100 MPa, 1300 HV and 9.8–12.9, respectively. One-way analysis of variance (ANOVA) showed no significant difference in biaxial flexural strength among the eight groups (p > 0.05). Two-way ANOVA showed no significant differences in hardness values among groups except FS1 and FS5 which had significantly higher hardness values than FS4 and FS7 (p Conclusion There was no difference in flexural strength of uncolored and colored Y-TZP ceramic. The Fatigue limit of uncolored, FS4 and FS7 zirconia may be defined as lying between 60 and 65% of the stress to failure.
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the biaxial flexural strength and Fatigue Property of lava y tzp dental ceramic
Dental Materials, 2007Co-Authors: Piyapanna Pittayachawan, Ailbhe Mcdonald, Aviva Petrie, Jonathan C KnowlesAbstract:Abstract Objectives The development of yttrium oxide partially stabilized zirconia (Y-TZP) has allowed the use of ceramic in load-bearing sites. The aim of this study was to evaluate and compare the biaxial flexural strength, hardness and Fatigue life of colored and uncolored zirconia in the LAVA™ system. Materials and methods Eight groups (n = 30) of standardized disc specimens (15 mm × 1.3 mm) were used to examine the biaxial flexural strength (ISO 6872 standard) using a Dartec HC10 Fatigue Tester (Zwick Ltd., UK) and Vickers hardness was also measured. The uncolored, FS4, FS7 groups were also submitted to dynamic Fatigue testing to produce stress–number curves. The strength reliability was analyzed using Weibull distribution. Results All groups had a mean biaxial flexural strength, hardness and Weibull modulus (m) of approximately 1100 MPa, 1300 HV and 9.8–12.9, respectively. One-way analysis of variance (ANOVA) showed no significant difference in biaxial flexural strength among the eight groups (p > 0.05). Two-way ANOVA showed no significant differences in hardness values among groups except FS1 and FS5 which had significantly higher hardness values than FS4 and FS7 (p Conclusion There was no difference in flexural strength of uncolored and colored Y-TZP ceramic. The Fatigue limit of uncolored, FS4 and FS7 zirconia may be defined as lying between 60 and 65% of the stress to failure.
David Hardacre - One of the best experts on this subject based on the ideXlab platform.
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application of data science approach to Fatigue Property assessment of laser powder bed fusion stainless steel 316l
2019Co-Authors: Meng Zhang, Xiang Zhang, C N Sun, P C Goh, J Wei, David HardacreAbstract:The adaptive neuro-fuzzy inference system (ANFIS) was applied for Fatigue life prediction of laser powder bed fusion (L-PBF) stainless steel 316L. The model was evaluated using a dataset containing 111 Fatigue data derived from 14 independent S-N curves. By using porosity fraction, tensile strength and cyclic stress as the inputs, the fuzzy rules defining the relations between these parameters and Fatigue life were obtained for a Sugeno-type ANFIS model. The computationally derived fuzzy sets agree well with understanding of the Fatigue failure mechanism, and the model demonstrates good prediction accuracy for both the training and test data. For parts made by the emerging L-PBF process where sufficient knowledge of the material behavior is still lacking, the ANFIS approach offers clear advantage over classical neural network, as the use of fuzzy logics allows more physically meaningful system design and result validation.
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Predictive models for Fatigue Property of laser powder bed fusion stainless steel 316L
Materials and Design, 2018Co-Authors: Meng Zhang, David Hardacre, Xiang Zhang, Hua LiAbstract:The selection of appropriate processing parameters is crucial for producing parts with target properties via the laser powder bed fusion (L-PBF) process. In this work, the Fatigue properties of L-PBF stainless steel 316L under controlled changes in laser power and scan speed were studied by employing the statistical response surface method. Processing regions corresponding to different Fatigue failure mechanisms were identified. The optimum Fatigue properties are associated with crack initiation from microstructure defect, which, by acting as the weakest link, creates enhanced porosity-tolerance at applied stress approaching the Fatigue limit. Deviations from the optimum processing condition lead to strength degradation and porosity-driven cracking. Based on the observed relations between microstructural features and failure behaviour, a processing-independent Fatigue prediction model was proposed. The microstructure-driven failure was modelled by a reference S-N curve where the intrinsic effect of microstructure inhomogeneity was accounted for by applying a reduction factor on Fatigue life. For the porosity-driven failure, high cycle Fatigue life follows an inverse-square-root relation with porosity fraction. This relation was incorporated into the Basquin equation for predicting the Fatigue strength parameters.
