Pub Date : 2026-10-27DOI: 10.1016/j.ijplas.2026.104861
J. Ramesh Babu, Pranav M. Karve, Sankaran Mahadevan, Kishore Appunhi Nair, Tawqeer Nasir Tak, Somnath Ghosh
Bayesian calibration of crystal plasticity finite element model (CPFEM) parameters is performed using macroscopic volumetric stress–strain data. Laboratory test data from only a few experiments are generally available to calibrate this complex model, which accounts for many phenomena (anisotropic elasticity and plasticity, rate- and size-dependent behavior, isotropic and kinematic hardening). Deterministic parameter-fitting procedures, which do not explicitly quantify parameter uncertainty, are typically used for calibration. A Bayesian calibration methodology that quantifies uncertainty in the model parameters (via prior and posterior distributions) is desirable, so that the predictions from the calibrated model can reflect this uncertainty. However, using the high-fidelity CPFE model is computationally challenging in sampling-based posterior estimation methods (e.g., Markov Chain Monte Carlo, or MCMC simulations) because the model contains a large number of material and constitutive parameters, making simultaneous calibration computationally expensive and impractical. To tackle this challenge, a subset of key parameters is first identified for calibration based on physical intuition. A surrogate model is then trained to predict CPFEM outputs of interest (Young’s modulus, yield point, and hardening slope) corresponding to given parameter values (for fixed values of other inputs, e.g., loading). The surrogate model greatly reduces the computational burden of Markov Chain Monte Carlo (MCMC) simulation. The effect of incorporating model discrepancy and further reducing the number of model parameters (guided by global sensitivity analyses) on the posterior distributions of key CPFEM outputs is investigated. The proposed methodology is demonstrated by calibrating eleven parameters in a CPFEM for Ti6Al4V. It is found that including a model discrepancy term produces wider posterior distributions and alleviates overconfidence. For this CPFEM, calibrating seven parameters does not significantly change output posterior distributions relative to eleven parameters. Further reduction (four parameters) captures the primary trends, but results in slightly different posterior distributions due to neglected second-order interaction effects. The proposed surrogate-enabled Bayesian framework offers a reproducible template for uncertainty-quantified CPFEM calibration for engineering alloys using only macroscopic tensile test (stress–strain) data.
{"title":"Bayesian calibration of crystal plasticity finite element model parameters","authors":"J. Ramesh Babu, Pranav M. Karve, Sankaran Mahadevan, Kishore Appunhi Nair, Tawqeer Nasir Tak, Somnath Ghosh","doi":"10.1016/j.ijplas.2026.104861","DOIUrl":"https://doi.org/10.1016/j.ijplas.2026.104861","url":null,"abstract":"Bayesian calibration of crystal plasticity finite element model (CPFEM) parameters is performed using macroscopic volumetric stress–strain data. Laboratory test data from only a few experiments are generally available to calibrate this complex model, which accounts for many phenomena (anisotropic elasticity and plasticity, rate- and size-dependent behavior, isotropic and kinematic hardening). Deterministic parameter-fitting procedures, which do not explicitly quantify parameter uncertainty, are typically used for calibration. A Bayesian calibration methodology that quantifies uncertainty in the model parameters (via prior and posterior distributions) is desirable, so that the predictions from the calibrated model can reflect this uncertainty. However, using the high-fidelity CPFE model is computationally challenging in sampling-based posterior estimation methods (e.g., Markov Chain Monte Carlo, or MCMC simulations) because the model contains a large number of material and constitutive parameters, making simultaneous calibration computationally expensive and impractical. To tackle this challenge, a subset of key parameters is first identified for calibration based on physical intuition. A surrogate model is then trained to predict CPFEM outputs of interest (Young’s modulus, yield point, and hardening slope) corresponding to given parameter values (for fixed values of other inputs, e.g., loading). The surrogate model greatly reduces the computational burden of Markov Chain Monte Carlo (MCMC) simulation. The effect of incorporating model discrepancy and further reducing the number of model parameters (guided by global sensitivity analyses) on the posterior distributions of key CPFEM outputs is investigated. The proposed methodology is demonstrated by calibrating eleven parameters in a CPFEM for Ti<ce:glyph name=\"sbnd\"></ce:glyph>6Al<ce:glyph name=\"sbnd\"></ce:glyph>4V. It is found that including a model discrepancy term produces wider posterior distributions and alleviates overconfidence. For this CPFEM, calibrating seven parameters does not significantly change output posterior distributions relative to eleven parameters. Further reduction (four parameters) captures the primary trends, but results in slightly different posterior distributions due to neglected second-order interaction effects. The proposed surrogate-enabled Bayesian framework offers a reproducible template for uncertainty-quantified CPFEM calibration for engineering alloys using only macroscopic tensile test (stress–strain) data.","PeriodicalId":340,"journal":{"name":"International Journal of Plasticity","volume":"21 1","pages":""},"PeriodicalIF":9.8,"publicationDate":"2026-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149291026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper investigates microstructure–property correlations in helium-assisted cold spray additive manufacturing (CSAM) of Al1050. Electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM) reveal a bimodal microstructure, with ultrafine-/nanoscale grains formed at particle–particle interfaces by severe plastic deformation during impact and coarse grains retained in particle interiors. To link local microstructure to macroscopic response, two crystal plasticity finite element (CPFE) parameter sets for coarse-grained (CG) and ultrafine-grained (UFG) constituents were inversely identified from nanoindentation load–depth curves. These parameters were assigned to an EBSD-derived representative volume element (RVE) and benchmarked against Voronoi-generated homogeneous RVEs, namely homogeneous coarse-grained (HM-CG) and homogeneous ultrafine-grained (HM-UFG) models, together with extracted constituent subsets. The calibrated CPFE model reproduces the experimental tensile stress–strain response up to 4.5% strain. Field analyses reveal CG/UFG stress partitioning, preferential strain localization within CG clusters, and heterogeneous slip activation governed by compatibility constraints. Dislocation-resolved outputs show two-stage partitioning of statistically stored dislocation (SSD) storage, with a crossover at approximately 1% strain for the baseline initial-density contrast; its occurrence and position depend on the prescribed initial SSD densities. In contrast, geometrically necessary dislocations (GND) localize mainly in interface- and boundary-dominated regions and correlate with strain-gradient intensity. Mechanism-ablation simulations predict a larger strengthening increment from GND-driven back stress than from GND-related forest hardening within the adopted formulation, with larger increments at smaller microstructural length scales. Strength–ductility benchmarking shows that helium-assisted CSAM Al1050 achieves high yield strength while retaining elongation. The results connect the CSAM-induced bimodal microstructure with heterogeneous plasticity and model-dependent dislocation strengthening.
{"title":"Microstructure-Driven Mechanical Behavior and Deformation Micromechanisms of Helium-Assisted Cold-Spray Additively Manufactured Al1050: A Dislocation-Density-Based Crystal Plasticity Study","authors":"Jiashuo QI, Yichen HU, Rija Nirina RAOELISON, Pierre-Emmanuel MAZERAN, Zhenming YUE, Daniel MARCEAU, Xiang WANG, Mousa JAVIDANI, Mohamed RACHIK","doi":"10.1016/j.ijplas.2026.104870","DOIUrl":"https://doi.org/10.1016/j.ijplas.2026.104870","url":null,"abstract":"This paper investigates microstructure–property correlations in helium-assisted cold spray additive manufacturing (CSAM) of Al1050. Electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM) reveal a bimodal microstructure, with ultrafine-/nanoscale grains formed at particle–particle interfaces by severe plastic deformation during impact and coarse grains retained in particle interiors. To link local microstructure to macroscopic response, two crystal plasticity finite element (CPFE) parameter sets for coarse-grained (CG) and ultrafine-grained (UFG) constituents were inversely identified from nanoindentation load–depth curves. These parameters were assigned to an EBSD-derived representative volume element (RVE) and benchmarked against Voronoi-generated homogeneous RVEs, namely homogeneous coarse-grained (HM-CG) and homogeneous ultrafine-grained (HM-UFG) models, together with extracted constituent subsets. The calibrated CPFE model reproduces the experimental tensile stress–strain response up to 4.5% strain. Field analyses reveal CG/UFG stress partitioning, preferential strain localization within CG clusters, and heterogeneous slip activation governed by compatibility constraints. Dislocation-resolved outputs show two-stage partitioning of statistically stored dislocation (SSD) storage, with a crossover at approximately 1% strain for the baseline initial-density contrast; its occurrence and position depend on the prescribed initial SSD densities. In contrast, geometrically necessary dislocations (GND) localize mainly in interface- and boundary-dominated regions and correlate with strain-gradient intensity. Mechanism-ablation simulations predict a larger strengthening increment from GND-driven back stress than from GND-related forest hardening within the adopted formulation, with larger increments at smaller microstructural length scales. Strength–ductility benchmarking shows that helium-assisted CSAM Al1050 achieves high yield strength while retaining elongation. The results connect the CSAM-induced bimodal microstructure with heterogeneous plasticity and model-dependent dislocation strengthening.","PeriodicalId":340,"journal":{"name":"International Journal of Plasticity","volume":"21 1","pages":""},"PeriodicalIF":9.8,"publicationDate":"2026-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149291208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-10-23DOI: 10.1016/j.ijplas.2026.104863
Peng Zhang, Keke Tang
Ductile fracture in metals is history-dependent, with plastic localization typically preceding and guiding crack growth. This work proposes an incremental graph Kolmogorov–Arnold network deep energy method (GKAN–DEM) for quasi-static ductile phase-field fracture. The method employs a reduced J2 incremental potential for elastoplastic response, strictly separates trial and committed states, and combines a neural displacement solve with a mesh-native phase-field subproblem to ensure consistent updates of internal variables. The graph network operates directly on the finite element mesh and predicts displacement increments, while material history variables retain their element-level evolution. Representative benchmarks show that the method accurately reproduces load response, plastic localization and crack paths, with close agreement with finite element solutions. Architecture studies further show that the adopted ChebyKAN parameterization reaches comparable accuracy with fewer staggered displacement solves than the tested fixed-activation controls. The resulting framework offers a general route for extending deep energy methods to path-dependent nonlinear problems in computational solid mechanics.
{"title":"An incremental graph KAN deep energy method for ductile phase-field fracture","authors":"Peng Zhang, Keke Tang","doi":"10.1016/j.ijplas.2026.104863","DOIUrl":"https://doi.org/10.1016/j.ijplas.2026.104863","url":null,"abstract":"Ductile fracture in metals is history-dependent, with plastic localization typically preceding and guiding crack growth. This work proposes an incremental graph Kolmogorov–Arnold network deep energy method (GKAN–DEM) for quasi-static ductile phase-field fracture. The method employs a reduced J2 incremental potential for elastoplastic response, strictly separates trial and committed states, and combines a neural displacement solve with a mesh-native phase-field subproblem to ensure consistent updates of internal variables. The graph network operates directly on the finite element mesh and predicts displacement increments, while material history variables retain their element-level evolution. Representative benchmarks show that the method accurately reproduces load response, plastic localization and crack paths, with close agreement with finite element solutions. Architecture studies further show that the adopted ChebyKAN parameterization reaches comparable accuracy with fewer staggered displacement solves than the tested fixed-activation controls. The resulting framework offers a general route for extending deep energy methods to path-dependent nonlinear problems in computational solid mechanics.","PeriodicalId":340,"journal":{"name":"International Journal of Plasticity","volume":"15 1","pages":""},"PeriodicalIF":9.8,"publicationDate":"2026-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149291027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-10-22DOI: 10.1016/j.ijplas.2026.104868
Kazuki Okuno, Kenichi Takai
{"title":"Mechanistic differences between hydrogen-related intergranular and transgranular fractures in tempered martensitic steel revealed by frozen-in hydrogen distribution","authors":"Kazuki Okuno, Kenichi Takai","doi":"10.1016/j.ijplas.2026.104868","DOIUrl":"https://doi.org/10.1016/j.ijplas.2026.104868","url":null,"abstract":"","PeriodicalId":340,"journal":{"name":"International Journal of Plasticity","volume":"90 1","pages":""},"PeriodicalIF":9.8,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149231960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-10-01DOI: 10.1016/j.ijplas.2026.104864
Dan Xiao,Chuandong Wu,Weiming Huang,Shuai Shen,Qi Xu,Ailin Zhou,Yijing Zhou,Jingjing Wang,Shuang Yang,Zhanghua Gan,Jing Liu
{"title":"Coherent/Semi-coherent Precipitation Behavior and Fracture Mechanisms of CoCrNi Multi-principal Element Alloys with High Strength over a Wide Temperature Range","authors":"Dan Xiao,Chuandong Wu,Weiming Huang,Shuai Shen,Qi Xu,Ailin Zhou,Yijing Zhou,Jingjing Wang,Shuang Yang,Zhanghua Gan,Jing Liu","doi":"10.1016/j.ijplas.2026.104864","DOIUrl":"https://doi.org/10.1016/j.ijplas.2026.104864","url":null,"abstract":"","PeriodicalId":340,"journal":{"name":"International Journal of Plasticity","volume":"1 1","pages":"104864"},"PeriodicalIF":9.8,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149208681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Creep resistance is critical for the reliability of engineering structures at high temperatures. In this study, in situ scanning electron microscope (SEM) creep experiments show that laser powder bed fusion fabricated Ti-6Al-4V (LPBF Ti-6Al-4V) exhibits a creep lifetime about three to five times longer than that of forged Ti-6Al-4V. Distinct creep failure mechanisms were identified, with grain boundary sliding dominating in the forged Ti-6Al-4V, while void-induced grain boundary separation controlled the LPBF Ti-6Al-4V. By integrating experiments with a multiphysics coupled microscale creep model that simultaneously captures diffusion creep, dislocation glide and climb, grain boundary sliding, and void evolution, the results suggest that the elongated grain morphology and lower dislocation density in LPBF Ti-6Al-4V contribute to its enhanced creep performance. A physics-informed neural network (PINN)-driven multiscale creep framework is developed to bridge the gap between the mechanistic microscale creep model and macroscale creep life prediction. This work provides new insights into the creep resistance of additively manufactured titanium alloys and presents a promising approach for multiscale creep life assessment.
{"title":"Microstructural origins of enhanced creep resistance in laser printed Ti-6Al-4V","authors":"Zhun Liang , Mingyang Zhang , Zheng Guo , Zongchang Guo , Yinan Cui","doi":"10.1016/j.ijplas.2026.104637","DOIUrl":"10.1016/j.ijplas.2026.104637","url":null,"abstract":"<div><div>Creep resistance is critical for the reliability of engineering structures at high temperatures. In this study, <em>in situ</em> scanning electron microscope (SEM) creep experiments show that laser powder bed fusion fabricated Ti-6Al-4V (LPBF Ti-6Al-4V) exhibits a creep lifetime about three to five times longer than that of forged Ti-6Al-4V. Distinct creep failure mechanisms were identified, with grain boundary sliding dominating in the forged Ti-6Al-4V, while void-induced grain boundary separation controlled the LPBF Ti-6Al-4V. By integrating experiments with a multiphysics coupled microscale creep model that simultaneously captures diffusion creep, dislocation glide and climb, grain boundary sliding, and void evolution, the results suggest that the elongated grain morphology and lower dislocation density in LPBF Ti-6Al-4V contribute to its enhanced creep performance. A physics-informed neural network (PINN)-driven multiscale creep framework is developed to bridge the gap between the mechanistic microscale creep model and macroscale creep life prediction. This work provides new insights into the creep resistance of additively manufactured titanium alloys and presents a promising approach for multiscale creep life assessment.</div></div>","PeriodicalId":340,"journal":{"name":"International Journal of Plasticity","volume":"199 ","pages":"Article 104637"},"PeriodicalIF":12.8,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146116217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-03-01Epub Date: 2026-02-03DOI: 10.1016/j.ijplas.2026.104632
Xian-Chen Kuang , Wu-Gui Jiang , Long-Hui Mao , Zhi-Kai Wu , Fen-Cheng Liu , Xiang Zhou , Peng-Hang Ling , Yang-Cheng Zhang , Min Yi
The fatigue performance of additively manufactured (AM) Inconel 718 is intrinsically governed by its grain morphology, necessitating a predictive understanding of the underlying plasticity-dominated mechanisms. To address this challenge, this study applies an integrated multilevel computational framework that explicitly bridges the process–structure–property–performance chain by coupling finite-element and cellular-automata (FE–CA) simulations of grain growth during laser powder bed fusion (LPBF), a deep neural network (DNN) for efficient material parameter calibration, and a strain-gradient crystal plasticity finite element (CPFE) model for fatigue life prediction. This unified framework enables, for the first time, a rigorous like-for-like comparison of three characteristic AM microstructures—equiaxed, columnar, and mixed grains—under a consistent computational and experimental calibration protocol, and thereby reveals new micromechanical insights into potential fatigue damage initiation from the plasticity perspective. Our simulations indicate that fatigue resistance is predominantly controlled by grain morphology and further modulated by morphology-induced anisotropy. Among them, equiaxed grains exhibit superior fatigue resistance to columnar and mixed grain morphologies, which is attributed to the activation of multiple slip systems and the resulting homogeneous deformation. In contrast, the strong texture in columnar grains gives rise to a pronounced “channeling effect”, leading to highly localized slip and a mismatch between regions of elevated plastic strain and actual damage accumulation. In terms of loading direction, the fatigue resistance under loading along the building direction (BD) is higher than that under loading along the transverse direction (TD). Crack initiation is predominantly predicted at high-angle grain boundaries and triple junctions, with the specific patterns highly sensitive to both grain morphology and loading direction. A key finding is the identification of a critical fatigue indicator parameter (FIP) threshold, beyond which fatigue life scatter intensifies significantly. While the CPFE model provides accurate predictions at intermediate strain amplitudes, its efficacy diminishes at higher strains due to the activation of alternative failure mechanisms. Overall, by integrating established computational methods, this work provides microstructure-sensitive insights and a practical framework for fatigue life prediction of AM materials, offering a potential pathway for AM process and microstructure optimization to achieve superior fatigue performance.
{"title":"Microstructure-induced fatigue scatter of additively manufactured inconel 718: Insight from multilevel simulations and dislocation-based strain gradient crystal plasticity","authors":"Xian-Chen Kuang , Wu-Gui Jiang , Long-Hui Mao , Zhi-Kai Wu , Fen-Cheng Liu , Xiang Zhou , Peng-Hang Ling , Yang-Cheng Zhang , Min Yi","doi":"10.1016/j.ijplas.2026.104632","DOIUrl":"10.1016/j.ijplas.2026.104632","url":null,"abstract":"<div><div>The fatigue performance of additively manufactured (AM) Inconel 718 is intrinsically governed by its grain morphology, necessitating a predictive understanding of the underlying plasticity-dominated mechanisms. To address this challenge, this study applies an integrated multilevel computational framework that explicitly bridges the process–structure–property–performance chain by coupling finite-element and cellular-automata (FE–CA) simulations of grain growth during laser powder bed fusion (LPBF), a deep neural network (DNN) for efficient material parameter calibration, and a strain-gradient crystal plasticity finite element (CPFE) model for fatigue life prediction. This unified framework enables, for the first time, a rigorous like-for-like comparison of three characteristic AM microstructures—equiaxed, columnar, and mixed grains—under a consistent computational and experimental calibration protocol, and thereby reveals new micromechanical insights into potential fatigue damage initiation from the plasticity perspective. Our simulations indicate that fatigue resistance is predominantly controlled by grain morphology and further modulated by morphology-induced anisotropy. Among them, equiaxed grains exhibit superior fatigue resistance to columnar and mixed grain morphologies, which is attributed to the activation of multiple slip systems and the resulting homogeneous deformation. In contrast, the strong texture in columnar grains gives rise to a pronounced “channeling effect”, leading to highly localized slip and a mismatch between regions of elevated plastic strain and actual damage accumulation. In terms of loading direction, the fatigue resistance under loading along the building direction (BD) is higher than that under loading along the transverse direction (TD). Crack initiation is predominantly predicted at high-angle grain boundaries and triple junctions, with the specific patterns highly sensitive to both grain morphology and loading direction. A key finding is the identification of a critical fatigue indicator parameter (FIP) threshold, beyond which fatigue life scatter intensifies significantly. While the CPFE model provides accurate predictions at intermediate strain amplitudes, its efficacy diminishes at higher strains due to the activation of alternative failure mechanisms. Overall, by integrating established computational methods, this work provides microstructure-sensitive insights and a practical framework for fatigue life prediction of AM materials, offering a potential pathway for AM process and microstructure optimization to achieve superior fatigue performance.</div></div>","PeriodicalId":340,"journal":{"name":"International Journal of Plasticity","volume":"198 ","pages":"Article 104632"},"PeriodicalIF":12.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146101602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-03-01Epub Date: 2026-01-29DOI: 10.1016/j.ijplas.2026.104627
Farhan Ashraf , Nicolò Grilli , Chen Liu , Michael Salvini , Catrin M. Davies , Christopher E. Truman , Mahmoud Mostafavi , David Knowles
Accurate modelling of plastic and creep deformation, along with the associated damage mechanisms in 316H stainless steel under high-temperature and complex loading conditions, is essential for ensuring the long-term structural integrity of power plant components. Robust physics-based models contribute to more accurate life assessment procedures, thereby improving safety and extending component service life under creep conditions. However, current approaches often lack accurate microstructure-sensitive models that can correlate experimentally observed local creep damage with key microstructural features such as grain orientation and morphology in creep damage prediction.
To address this knowledge gap, a combined modelling and experimental approach is employed to investigate creep damage initiation in 316H stainless steel at 550 °C. A crystal plasticity finite element (CPFE) model is developed to simulate the primary and secondary stages of creep deformation. To accurately predict local deformation under realistic boundary conditions, a new modelling strategy is introduced, embedding crystal plasticity domains within larger-scale geometries. Furthermore, a novel methodology is introduced to define damage initiation criterion by employing a classification algorithm to correlate experimentally observed creep damage with internal variables from the CPFE model. This data-driven approach enables the development of a predictive equation for identifying damaged grain boundaries. This equation represents a significant advancement over phenomenological approaches, such as the stress-modified ductility exhaustion (SMDE) model. The proposed model predicts approximately 67% of observed creep cavities at grain boundaries in the analysed regions, demonstrating the strong potential of a data-driven modelling framework for microstructure-sensitive damage prediction.
{"title":"Investigating creep damage initiation at the mesoscale using high-resolution electron microscopy, crystal plasticity modelling, and a classification algorithm","authors":"Farhan Ashraf , Nicolò Grilli , Chen Liu , Michael Salvini , Catrin M. Davies , Christopher E. Truman , Mahmoud Mostafavi , David Knowles","doi":"10.1016/j.ijplas.2026.104627","DOIUrl":"10.1016/j.ijplas.2026.104627","url":null,"abstract":"<div><div>Accurate modelling of plastic and creep deformation, along with the associated damage mechanisms in 316H stainless steel under high-temperature and complex loading conditions, is essential for ensuring the long-term structural integrity of power plant components. Robust physics-based models contribute to more accurate life assessment procedures, thereby improving safety and extending component service life under creep conditions. However, current approaches often lack accurate microstructure-sensitive models that can correlate experimentally observed local creep damage with key microstructural features such as grain orientation and morphology in creep damage prediction.</div><div>To address this knowledge gap, a combined modelling and experimental approach is employed to investigate creep damage initiation in 316H stainless steel at 550 °C. A crystal plasticity finite element (CPFE) model is developed to simulate the primary and secondary stages of creep deformation. To accurately predict local deformation under realistic boundary conditions, a new modelling strategy is introduced, embedding crystal plasticity domains within larger-scale geometries. Furthermore, a novel methodology is introduced to define damage initiation criterion by employing a classification algorithm to correlate experimentally observed creep damage with internal variables from the CPFE model. This data-driven approach enables the development of a predictive equation for identifying damaged grain boundaries. This equation represents a significant advancement over phenomenological approaches, such as the stress-modified ductility exhaustion (SMDE) model. The proposed model predicts approximately 67% of observed creep cavities at grain boundaries in the analysed regions, demonstrating the strong potential of a data-driven modelling framework for microstructure-sensitive damage prediction.</div></div>","PeriodicalId":340,"journal":{"name":"International Journal of Plasticity","volume":"198 ","pages":"Article 104627"},"PeriodicalIF":12.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146072352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2026-03-01Epub Date: 2026-01-15DOI: 10.1016/j.ijplas.2026.104613
Lin Guo , Jiaxing Wu , Ji Gu , Dechuang Zhang , Cheng Ma , Yilong Dai , Jianguo Lin , Ian Baker , Min Song
Twinning and martensitic transformations are well-understood under monotonic loading, yet how stress reversal and the associated kinematic reversibility, inherent to cyclic deformation, affects these mechanisms remains insufficiently understood. Here, we systematically investigate the microstructural evolution of a metastable high entropy alloy Fe60Mn12Cr12Ni8Si8 under cyclic tension-compression (CTC) loading. Multi-scale characterizations reveal that the cyclic stress reversal fundamentally alters the transformation pathway compared to the monotonic tension. The initial, undeformed material consists of a face-centered cubic γ phase. Monotonic tension primarily activates deformation-induced martensitic transformation, whereas CTC produces markedly different microstructural pathways depending on strain amplitude. At a low strain amplitude (0.5%), short-range glide of Shockley partial dislocations promotes extensive formation of HCP ε-martensite (a fraction of ∼68.3%). In contrast, high-strain-amplitude CTC loading (2.0%) activates an abnormal transformation-mediated twinning mechanism. This process, driven by the reversible motion of Shockley partial dislocation within confined ε-martensite, leads to a refined γ/γtwin/ε nano-laminate structure with a spacing of ∼2.6 nm. Furthermore, we identify unconventional polymorphic transformation pathways accommodating the high local stress concentrations: (i) nucleation of body-centered cubic α′-martensite at a specific interface where the two γ phases maintain an 86° angle between their respective planes, and (ii) a direct γ to body-centered tetragonal α-martensite transition via continuous lattice shearing along . These mechanisms are attributed to the unique stress accommodation requirements in the highly confined nano-laminates. The resulting hierarchical microstructure not only relieves local stress concentrations but also contributes to the good cyclic durability. Overall, these findings establish an atomistic mechanistic link between cyclic reversibility and transformation/twinning pathway selection, and suggest a processing-enabled route to engineer heterogeneous γ/γtwin/ε nano-laminate structure in bulk metastable alloys at room temperature.
{"title":"Abnormal twinning mechanisms and martensitic transformation in Fe60Mn12Cr12Ni8Si8 high entropy alloy under cyclic tension-compression loading","authors":"Lin Guo , Jiaxing Wu , Ji Gu , Dechuang Zhang , Cheng Ma , Yilong Dai , Jianguo Lin , Ian Baker , Min Song","doi":"10.1016/j.ijplas.2026.104613","DOIUrl":"10.1016/j.ijplas.2026.104613","url":null,"abstract":"<div><div>Twinning and martensitic transformations are well-understood under monotonic loading, yet how stress reversal and the associated kinematic reversibility, inherent to cyclic deformation, affects these mechanisms remains insufficiently understood. Here, we systematically investigate the microstructural evolution of a metastable high entropy alloy Fe<sub>60</sub>Mn<sub>12</sub>Cr<sub>12</sub>Ni<sub>8</sub>Si<sub>8</sub> under cyclic tension-compression (CTC) loading. Multi-scale characterizations reveal that the cyclic stress reversal fundamentally alters the transformation pathway compared to the monotonic tension. The initial, undeformed material consists of a face-centered cubic γ phase. Monotonic tension primarily activates deformation-induced martensitic transformation, whereas CTC produces markedly different microstructural pathways depending on strain amplitude. At a low strain amplitude (0.5%), short-range glide of Shockley partial dislocations promotes extensive formation of HCP ε-martensite (a fraction of ∼68.3%). In contrast, high-strain-amplitude CTC loading (2.0%) activates an abnormal transformation-mediated twinning mechanism. This process, driven by the reversible motion of Shockley partial dislocation within confined ε-martensite, leads to a refined γ/γ<sub>twin</sub>/ε nano-laminate structure with a spacing of ∼2.6 nm. Furthermore, we identify unconventional polymorphic transformation pathways accommodating the high local stress concentrations: (i) nucleation of body-centered cubic α′-martensite at a specific interface where the two γ phases maintain an 86° angle between their respective <span><math><msub><mrow><mo>(</mo><mrow><mn>11</mn><mover><mn>1</mn><mo>¯</mo></mover></mrow><mo>)</mo></mrow><mi>γ</mi></msub></math></span> planes, and (ii) a direct γ to body-centered tetragonal α-martensite transition via continuous lattice shearing along <span><math><mrow><mrow><mo>(</mo><mn>111</mn><mo>)</mo></mrow><msub><mrow><mo>[</mo><mrow><mn>11</mn><mover><mn>2</mn><mo>¯</mo></mover></mrow><mo>]</mo></mrow><mi>γ</mi></msub></mrow></math></span>. These mechanisms are attributed to the unique stress accommodation requirements in the highly confined nano-laminates. The resulting hierarchical microstructure not only relieves local stress concentrations but also contributes to the good cyclic durability. Overall, these findings establish an atomistic mechanistic link between cyclic reversibility and transformation/twinning pathway selection, and suggest a processing-enabled route to engineer heterogeneous γ/γ<sub>twin</sub>/ε nano-laminate structure in bulk metastable alloys at room temperature.</div></div>","PeriodicalId":340,"journal":{"name":"International Journal of Plasticity","volume":"198 ","pages":"Article 104613"},"PeriodicalIF":12.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145993424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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