Pub Date : 2023-10-30DOI: 10.1088/1361-651x/ad041a
Prakash Kumar, Binay Kumar
Abstract This work aims to analyze the wear properties of the hybrid aluminum metal matrix composites (HAMMCs) using finite element analysis (FEA). A dry sliding linear reciprocating wear mechanism is analyzed using ANSYS 19.1. Aluminum 7075 alloy and HAMMC reinforced with ZrB 2 (1, 3, and 5 wt.%) and fly ash (2 wt.%) is taken as sample material. A steel ball (EN 52100) is utilized as a counterpart in the dry sliding wear properties study. The deformation of the steel ball during the wear process is assumed to be negligible. Under various circumstances, a 3D point-to-surface connection is built to analyze the dry sliding wear process. The wear depth, contact pressure, and wear volume are analyzed using FEA. The analytical results are compared with the experimental results with the help of ANSYS to analyze the process parameters. The ANOVA analysis is employed for optimization, which exhibits that the load had the most significant impact on the material’s wear rate, followed by the material’s composition and temperature. The wear depth, wear rate, and contact pressure at optimum input parameters for the HAMMCs are 0.47 μ m, 11.31 × 10 −6 mm 3 Nm −1 , and 0.33 MPa, respectively. The Simulated results support the experimental results, and the average error is 9.82%.
{"title":"Numerical Simulation of linear reciprocating wear mechanism of Hybrid aluminum metal matrix composite using finite element method","authors":"Prakash Kumar, Binay Kumar","doi":"10.1088/1361-651x/ad041a","DOIUrl":"https://doi.org/10.1088/1361-651x/ad041a","url":null,"abstract":"Abstract This work aims to analyze the wear properties of the hybrid aluminum metal matrix composites (HAMMCs) using finite element analysis (FEA). A dry sliding linear reciprocating wear mechanism is analyzed using ANSYS 19.1. Aluminum 7075 alloy and HAMMC reinforced with ZrB 2 (1, 3, and 5 wt.%) and fly ash (2 wt.%) is taken as sample material. A steel ball (EN 52100) is utilized as a counterpart in the dry sliding wear properties study. The deformation of the steel ball during the wear process is assumed to be negligible. Under various circumstances, a 3D point-to-surface connection is built to analyze the dry sliding wear process. The wear depth, contact pressure, and wear volume are analyzed using FEA. The analytical results are compared with the experimental results with the help of ANSYS to analyze the process parameters. The ANOVA analysis is employed for optimization, which exhibits that the load had the most significant impact on the material’s wear rate, followed by the material’s composition and temperature. The wear depth, wear rate, and contact pressure at optimum input parameters for the HAMMCs are 0.47 μ m, 11.31 × 10 −6 mm 3 Nm −1 , and 0.33 MPa, respectively. The Simulated results support the experimental results, and the average error is 9.82%.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"53 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136018992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Topologically interlocking architectures have demonstrated the potential to create durable ceramics
with desirable thermo-mechanical properties. However, designing such materials poses
challenges due to the intricate design space, rendering traditional modeling approaches ineffective
and impractical. This paper presents a novel approach to designing high-performance architectured
ceramics by integrating machine learning (ML) techniques and finite element analysis (FEA)
data. The design space of interlocked architectured ceramics encompasses tiles with varying angles
and sizes. The study considers three configurations 3 × 3, 5 × 5, and 7 × 7 arrays of tiles
with five sets of interlocking angles (5◦, 10◦, 15◦, 20◦, and 25◦). By training ML models, specifically
convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) using FEA simulation
data, we establish correlations between architectural parameters and thermo-mechanical characteristics.
A grid comprising all possible designs was generated to predict high-performance
architectured ceramics. This grid was then fed into the networks that were trained using results
from the FEA simulation. The predicted results for all possible interpolated designs are utilized
to determine the optimal structure among the configurations. The goal is to identify the optimal
interlocked ceramics that minimize the out-of-plane deformation for thermal shielding and maximize
heat absorption for heat sink applications. To validate the performance of the outcomes,
FEA simulations were conducted on the best predictions obtained from both the MLP and CNN
algorithms. Despite the limited amount of available simulation data, our networks demonstrate
effectiveness in predicting the transient thermo-mechanical responses of potential panel designs.
Notably, the optimal design predicted by CNN led to ≈30% improvement in edge temperature.
{"title":"Designing architectured ceramics for transient thermal applications using finite element and deep learning","authors":"Elham Kiani, Hamidreza Yazdani Sarvestani, Hossein Ravanbakhsh, Razyeh Behbahani, Behnam Ashrafi, Meysam Rahmat, Mikko Karttunen","doi":"10.1088/1361-651x/ad073a","DOIUrl":"https://doi.org/10.1088/1361-651x/ad073a","url":null,"abstract":"Abstract Topologically interlocking architectures have demonstrated the potential to create durable ceramics
with desirable thermo-mechanical properties. However, designing such materials poses
challenges due to the intricate design space, rendering traditional modeling approaches ineffective
and impractical. This paper presents a novel approach to designing high-performance architectured
ceramics by integrating machine learning (ML) techniques and finite element analysis (FEA)
data. The design space of interlocked architectured ceramics encompasses tiles with varying angles
and sizes. The study considers three configurations 3 × 3, 5 × 5, and 7 × 7 arrays of tiles
with five sets of interlocking angles (5◦, 10◦, 15◦, 20◦, and 25◦). By training ML models, specifically
convolutional neural networks (CNNs) and multilayer perceptrons (MLPs) using FEA simulation
data, we establish correlations between architectural parameters and thermo-mechanical characteristics.
A grid comprising all possible designs was generated to predict high-performance
architectured ceramics. This grid was then fed into the networks that were trained using results
from the FEA simulation. The predicted results for all possible interpolated designs are utilized
to determine the optimal structure among the configurations. The goal is to identify the optimal
interlocked ceramics that minimize the out-of-plane deformation for thermal shielding and maximize
heat absorption for heat sink applications. To validate the performance of the outcomes,
FEA simulations were conducted on the best predictions obtained from both the MLP and CNN
algorithms. Despite the limited amount of available simulation data, our networks demonstrate
effectiveness in predicting the transient thermo-mechanical responses of potential panel designs.
Notably, the optimal design predicted by CNN led to ≈30% improvement in edge temperature.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134907849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-10-19DOI: 10.1088/1361-651x/ad04f4
Priyabrata Das, Pulak Mohan Pandey
Abstract Medium entropy alloys (MEAs) are a subset of compositionally complex alloys (CCAs) whose mixing entropy lies between R and 1.5R where R is the universal gas constant. The properties of MEAs largely depend on the phases present in the alloy such as solid solution (SS), solid solution + intermetallic (SS+IM) and amorphous (AM). Hence, the correct prediction of phases can enable the efficient selection of material compositions with anticipated properties. In this paper, three ML algorithms viz. k-nearest neighbors (KNN), artificial neural network (ANN), and random forest (RF) were employed for the ternary phase classification problem. An MEA dataset was constructed by utilizing all reported MEAs till February 2023 to the best of authors’ knowledge. The study implied that the use of only three features (mixing enthalpy, atomic size mismatch, and a strain energy related parameter) were sufficient for the phase prediction in MEAs. Among the three ML algorithms, ANN had the highest macro averaged F1 score (86.7%) and accuracy (87.3%) in predicting the phases in MEAs, while RF has the lowest macro F1 score (84.67%) and accuracy (84.8%). However, for phase prediction between single phase SS and multi-phase SS (binary classification), distance-based algorithm (KNN) was found to be suitable. The prediction performance of ML model over a completely unseen data was assessed in the case study section. The experimentally determined phase details of three new MEA compositions fabricated by powder metallurgy route was also included in the unseen dataset. The SS and AM phases were correctly labeled nine times out of eleven instances by using ANN model. However, the model prediction for SS+IM phase was found to be less reliable (three out of five correct) owing to its relatively poor F1 score.
{"title":"Machine learning based phase prediction and powder metallurgy assisted experimental validation of medium entropy compositionally complex alloys","authors":"Priyabrata Das, Pulak Mohan Pandey","doi":"10.1088/1361-651x/ad04f4","DOIUrl":"https://doi.org/10.1088/1361-651x/ad04f4","url":null,"abstract":"Abstract Medium entropy alloys (MEAs) are a subset of compositionally complex alloys (CCAs) whose mixing entropy lies between R and 1.5R where R is the universal gas constant. The properties of MEAs largely depend on the phases present in the alloy such as solid solution (SS), solid solution + intermetallic (SS+IM) and amorphous (AM). Hence, the correct prediction of phases can enable the efficient selection of material compositions with anticipated properties. In this paper, three ML algorithms viz. k-nearest neighbors (KNN), artificial neural network (ANN), and random forest (RF) were employed for the ternary phase classification problem. An MEA dataset was constructed by utilizing all reported MEAs till February 2023 to the best of authors’ knowledge. The study implied that the use of only three features (mixing enthalpy, atomic size mismatch, and a strain energy related parameter) were sufficient for the phase prediction in MEAs. Among the three ML algorithms, ANN had the highest macro averaged F1 score (86.7%) and accuracy (87.3%) in predicting the phases in MEAs, while RF has the lowest macro F1 score (84.67%) and accuracy (84.8%). However, for phase prediction between single phase SS and multi-phase SS (binary classification), distance-based algorithm (KNN) was found to be suitable. The prediction performance of ML model over a completely unseen data was assessed in the case study section. The experimentally determined phase details of three new MEA compositions fabricated by powder metallurgy route was also included in the unseen dataset. The SS and AM phases were correctly labeled nine times out of eleven instances by using ANN model. However, the model prediction for SS+IM phase was found to be less reliable (three out of five correct) owing to its relatively poor F1 score.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135729351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-10-19DOI: 10.1088/1361-651x/ad01cc
Meijun Liu, Liwei Cheng, Jiazhong Xu
Abstract In this study, a combination of block-centered grid modeling and an enhanced genetic algorithm (GA) is introduced with the aim of optimizing the random permeability field within the Vacuum Assisted Resin Transfer Molding (VARTM) infusion model to enhance the accuracy of predicted resin flow distribution. Within the established 2D-VARTM model, random permeability values in the x and y directions are assigned to each grid. The model is then solved using the central difference method in conjunction with the upstream weighting method to predict the resin flow distribution. Subsequently, an improved GA based on heuristic mutation strategies was designed and validated. This algorithm employs the discrepancy between model predictions and actual sampling results as its fitness function and integrates heuristic strategies for iterative optimization. Simulation results revealed a significant improvement in the predictive accuracy of the model, with a jump from an initial 87.49%–97.19%. In practical applications, the predictive accuracy of the model reached 95.25%. This research offers an effective optimization approach for VARTM models and underscores the potential applicability of the enhanced GA in related fields.
{"title":"Improved Genetic Algorithm for 2D Resin Flow Model Optimization in VARTM Process","authors":"Meijun Liu, Liwei Cheng, Jiazhong Xu","doi":"10.1088/1361-651x/ad01cc","DOIUrl":"https://doi.org/10.1088/1361-651x/ad01cc","url":null,"abstract":"Abstract In this study, a combination of block-centered grid modeling and an enhanced genetic algorithm (GA) is introduced with the aim of optimizing the random permeability field within the Vacuum Assisted Resin Transfer Molding (VARTM) infusion model to enhance the accuracy of predicted resin flow distribution. Within the established 2D-VARTM model, random permeability values in the x and y directions are assigned to each grid. The model is then solved using the central difference method in conjunction with the upstream weighting method to predict the resin flow distribution. Subsequently, an improved GA based on heuristic mutation strategies was designed and validated. This algorithm employs the discrepancy between model predictions and actual sampling results as its fitness function and integrates heuristic strategies for iterative optimization. Simulation results revealed a significant improvement in the predictive accuracy of the model, with a jump from an initial 87.49%–97.19%. In practical applications, the predictive accuracy of the model reached 95.25%. This research offers an effective optimization approach for VARTM models and underscores the potential applicability of the enhanced GA in related fields.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135667459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract High-entropy alloys (HEAs), composed of multiple constituent elements with concentrations ranging from 5% to 35%, have been considered ideal solid solution of multi-principal elements. However, recent experimental and computational studies have demonstrated that complex enthalpic interactions among constituents lead to a wide variety of local chemical ordering (LCO) at lower temperatures. HEAs containing Cu typically decompose by forming of Cu-rich phases during annealing, thus affecting mechanical properties. In this study, CuNiCoFe HEA was chosen as a model with a tendency for Cu segregation at low temperatures. The formation of LCO and its impact on the deformation behaviors in the single-crystalline CuNiCoFe HEA were studied via molecular dynamics simulations. Our results demonstrate that CuNiCoFe HEA decomposes by Cu clustering, in agreement with prior experimental and computational studies, owing to insufficient configuration entropy to compete against the mixing enthalpy at lower temperatures. A softening in ultimate stress in the LCO models was observed compared to the random solid solution models. The softening is due to the lower unstable stacking fault energy, which determines the nucleation event of dislocations, thereby rationalizing the dislocation nucleation in the Cu-rich regions and the softening of the overall ultimate strength in the LCO models. Additionally, the inhomogeneous FCC-BCC transformation is closely associated with concentration inhomogeneity. CuNiCoFe HEA with LCO can be regarded as composites, consisting of clusters with different properties. Consequently, concentration inhomogeneity induced by LCO profoundly impacts the mechanical properties and deformation behaviors of the HEA. This study provides insights into the effect of LCO on the mechanical properties of CuNiCoFe HEAs, which is crucial for developing HEAs with tailored properties for specific applications.
{"title":"Impact of local chemical ordering on deformation mechanisms in single-crystalline CuNiCoFe high-entropy alloys: A molecular dynamics study","authors":"Siyao Shuang, Yanxiang Liang, Xie Zhang, Fuping Yuan, Guozheng Kang, Xu Zhang","doi":"10.1088/1361-651x/ad04f3","DOIUrl":"https://doi.org/10.1088/1361-651x/ad04f3","url":null,"abstract":"Abstract High-entropy alloys (HEAs), composed of multiple constituent elements with concentrations ranging from 5% to 35%, have been considered ideal solid solution of multi-principal elements. However, recent experimental and computational studies have demonstrated that complex enthalpic interactions among constituents lead to a wide variety of local chemical ordering (LCO) at lower temperatures. HEAs containing Cu typically decompose by forming of Cu-rich phases during annealing, thus affecting mechanical properties. In this study, CuNiCoFe HEA was chosen as a model with a tendency for Cu segregation at low temperatures. The formation of LCO and its impact on the deformation behaviors in the single-crystalline CuNiCoFe HEA were studied via molecular dynamics simulations. Our results demonstrate that CuNiCoFe HEA decomposes by Cu clustering, in agreement with prior experimental and computational studies, owing to insufficient configuration entropy to compete against the mixing enthalpy at lower temperatures. A softening in ultimate stress in the LCO models was observed compared to the random solid solution models. The softening is due to the lower unstable stacking fault energy, which determines the nucleation event of dislocations, thereby rationalizing the dislocation nucleation in the Cu-rich regions and the softening of the overall ultimate strength in the LCO models. Additionally, the inhomogeneous FCC-BCC transformation is closely associated with concentration inhomogeneity. CuNiCoFe HEA with LCO can be regarded as composites, consisting of clusters with different properties. Consequently, concentration inhomogeneity induced by LCO profoundly impacts the mechanical properties and deformation behaviors of the HEA. This study provides insights into the effect of LCO on the mechanical properties of CuNiCoFe HEAs, which is crucial for developing HEAs with tailored properties for specific applications.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135729334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-10-19DOI: 10.1088/1361-651x/ad04f2
Jiyun Kong, Qihong Fang, Jia Li
Abstract In recent years, FeCrNi medium entropy alloy, a new material with high hardness, high strength, good ductility and wear resistance, has been widely studied. In this work, the effect of precipitation volume fraction on the friction behavior of FeCrNi is studied by molecular dynamics simulation. With the increase of precipitation volume fraction, the average friction coefficient shows an upward trend. When the volume fraction of precipitation is between 2.33% and 3.10%, the wear resistance of FeCrNi would be enhanced after the nanoscratching. When the volume fraction of precipitation is between 2.33% and 3.10%, the normal force is larger, which means that a certain precipitation volume fraction will strengthen FeCrNi. Low precipitation volume fraction can effectively reduce the wear volume and wear rate during scratching, thus effectively reducing frictional force and friction coefficient. The interaction between dislocation and precipitation is an important factor that hinders dislocation propagation, leading to sample strengthening and the increase of wear volume, which is manifested as the increase of normal force and frictional force. The results guide the study of the effect of multiple precipitation on frictional properties and precipitation-dislocation interaction in FeCrNi.
{"title":"Optimizing the friction behavior of medium entropy alloy via controllable coherent nanoprecipitation","authors":"Jiyun Kong, Qihong Fang, Jia Li","doi":"10.1088/1361-651x/ad04f2","DOIUrl":"https://doi.org/10.1088/1361-651x/ad04f2","url":null,"abstract":"Abstract In recent years, FeCrNi medium entropy alloy, a new material with high hardness, high strength, good ductility and wear resistance, has been widely studied. In this work, the effect of precipitation volume fraction on the friction behavior of FeCrNi is studied by molecular dynamics simulation. With the increase of precipitation volume fraction, the average friction coefficient shows an upward trend. When the volume fraction of precipitation is between 2.33% and 3.10%, the wear resistance of FeCrNi would be enhanced after the nanoscratching. When the volume fraction of precipitation is between 2.33% and 3.10%, the normal force is larger, which means that a certain precipitation volume fraction will strengthen FeCrNi. Low precipitation volume fraction can effectively reduce the wear volume and wear rate during scratching, thus effectively reducing frictional force and friction coefficient. The interaction between dislocation and precipitation is an important factor that hinders dislocation propagation, leading to sample strengthening and the increase of wear volume, which is manifested as the increase of normal force and frictional force. The results guide the study of the effect of multiple precipitation on frictional properties and precipitation-dislocation interaction in FeCrNi.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135729087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract MD simulation is carried out to study diffusion in sodium silicate glasses (NS1, NS2, NS3, NS4) at temperatures of 973, 1173 and 1373 K. The result shows that the structure consists of network region where more than 83% of total Si and O are present, and Na-polyhedron region in which most Na-polyhedrons possess several non-bridging oxygens. The Na-polyhedron region changes slightly with temperature, and significantly with SiO2 concentration. During 150 ps the Si and O atoms vibrate around fixed points, while Na atoms move from one Na-polyhedron to another. The network region is static, while the Na-polyhedron region is seen dynamically. The glasses exhibit the dynamics heterogeneity. The simulation shows that Na atoms reside in a small part of Na-polyhedron region and move frequently through pathways consisting of polyhedrons with high local sodium density. Moreover, they move between polyhedrons often by small displacements and rarely by large jumps. We establish the expression for diffusion constant DNa via average resident time in polyhedron tRP and mean square displacement of Na per polyhedron . The dependence of DNa on and lnDNa on tRP is found to be linear.
{"title":"Study of sodium diffusion in silicate glasses. Molecular dynamics simulation","authors":"Nguyen Thi Thảo, Kien Pham, N.V. Yen, Pham Khac Hung, Noritake Fumiya","doi":"10.1088/1361-651x/ad0419","DOIUrl":"https://doi.org/10.1088/1361-651x/ad0419","url":null,"abstract":"Abstract MD simulation is carried out to study diffusion in sodium silicate glasses (NS1, NS2, NS3, NS4) at temperatures of 973, 1173 and 1373 K. The result shows that the structure consists of network region where more than 83% of total Si and O are present, and Na-polyhedron region in which most Na-polyhedrons possess several non-bridging oxygens. The Na-polyhedron region changes slightly with temperature, and significantly with SiO2 concentration. During 150 ps the Si and O atoms vibrate around fixed points, while Na atoms move from one Na-polyhedron to another. The network region is static, while the Na-polyhedron region is seen dynamically. The glasses exhibit the dynamics heterogeneity. The simulation shows that Na atoms reside in a small part of Na-polyhedron region and move frequently through pathways consisting of polyhedrons with high local sodium density. Moreover, they move between polyhedrons often by small displacements and rarely by large jumps. We establish the expression for diffusion constant DNa via average resident time in polyhedron tRP and mean square displacement of Na per polyhedron . The dependence of DNa on and lnDNa on tRP is found to be linear.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136034306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-10-13DOI: 10.1088/1361-651x/acfe27
Victor Grand, Baptiste Flipon, Alexis Gaillac, Marc Bernacki
Abstract A recently developed full field level-set model of continuous dynamic recrystallization is applied to simulate zircaloy-4 recrystallization during hot compression and subsequent heat treatment. The influence of strain rate, final strain and initial microstructure is investigated, by experimental and simulation tools. The recrystallization heterogeneity is quantified. This enables to confirm that quenched microstructures display a higher extent of heterogeneity. The simulation results replicate satisfactorily experimental observations. The simulation framework is especially able to capture such recrystallization heterogeneity induced by a different initial microstructure. Finally, the role of intragranular dislocation density heterogeneities over the preferential growth of recrystallized grains is pointed out thanks to additional simulations with different numerical formulations.
{"title":"Modeling CDRX and MDRX during hot forming of zircaloy-4","authors":"Victor Grand, Baptiste Flipon, Alexis Gaillac, Marc Bernacki","doi":"10.1088/1361-651x/acfe27","DOIUrl":"https://doi.org/10.1088/1361-651x/acfe27","url":null,"abstract":"Abstract A recently developed full field level-set model of continuous dynamic recrystallization is applied to simulate zircaloy-4 recrystallization during hot compression and subsequent heat treatment. The influence of strain rate, final strain and initial microstructure is investigated, by experimental and simulation tools. The recrystallization heterogeneity is quantified. This enables to confirm that quenched microstructures display a higher extent of heterogeneity. The simulation results replicate satisfactorily experimental observations. The simulation framework is especially able to capture such recrystallization heterogeneity induced by a different initial microstructure. Finally, the role of intragranular dislocation density heterogeneities over the preferential growth of recrystallized grains is pointed out thanks to additional simulations with different numerical formulations.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135805991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This work investigates the machining mechanism and deformation behavior of NiFeCo under conventional nanoscale cutting and ultrasonic elliptical vibration-assisted cutting (UEVC) through Molecular Dynamics (MD) simulation. The material removal process is considered in various vibration frequencies, amplitude ratios, and phase angles. In both cases, the highest shear strain, local stress, and temperature atoms mostly locate in the cutting area and chip volume, but the magnitudes are more significant under UEVC. The distribution analysis results of stacking fault and dislocation also show that grain boundaries strongly influence the deformation behavior and the local stress in the material. Moreover, in the cases of UEVC, the rise of vibration frequency and the decrease in amplitude ratio positively impact improving the material removal rate (MRR) and reducing the average cutting force. Meanwhile, the change in phase angles affects only the timing of the peak in force value and has no significant effect on the resultant force and the cutting efficiency.
{"title":"Machining mechanism of polycrystalline Nickel-based alloy under ultrasonic elliptical vibration-assisted cutting","authors":"Duy-Khanh Nguyen, Te-Hua Fang, Yue-Ru Cai, Ching-Chien Huang","doi":"10.1088/1361-651x/ad0316","DOIUrl":"https://doi.org/10.1088/1361-651x/ad0316","url":null,"abstract":"Abstract This work investigates the machining mechanism and deformation behavior of NiFeCo under conventional nanoscale cutting and ultrasonic elliptical vibration-assisted cutting (UEVC) through Molecular Dynamics (MD) simulation. The material removal process is considered in various vibration frequencies, amplitude ratios, and phase angles. In both cases, the highest shear strain, local stress, and temperature atoms mostly locate in the cutting area and chip volume, but the magnitudes are more significant under UEVC. The distribution analysis results of stacking fault and dislocation also show that grain boundaries strongly influence the deformation behavior and the local stress in the material. Moreover, in the cases of UEVC, the rise of vibration frequency and the decrease in amplitude ratio positively impact improving the material removal rate (MRR) and reducing the average cutting force. Meanwhile, the change in phase angles affects only the timing of the peak in force value and has no significant effect on the resultant force and the cutting efficiency.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135853441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 : 2023-10-13DOI: 10.1088/1361-651x/acff7c
Siddharth Singh, He Liu, Rajat Arora, Robert M Suter, Amit Acharya
Abstract A rigorous methodology is developed for computing elastic fields generated by experimentally observed defect structures within grains in a polycrystal that has undergone tensile extension. An example application is made using a near-field high energy x-ray diffraction microscope measurement of a zirconium sample that underwent 13.6% tensile extension from an initially well-annealed state. (Sub)grain boundary features are identified with apparent disclination line defects in them. The elastic fields of these features identified from the experiment are calculated.
{"title":"Modeling of experimentally observed topological defects inside bulk polycrystals","authors":"Siddharth Singh, He Liu, Rajat Arora, Robert M Suter, Amit Acharya","doi":"10.1088/1361-651x/acff7c","DOIUrl":"https://doi.org/10.1088/1361-651x/acff7c","url":null,"abstract":"Abstract A rigorous methodology is developed for computing elastic fields generated by experimentally observed defect structures within grains in a polycrystal that has undergone tensile extension. An example application is made using a near-field high energy x-ray diffraction microscope measurement of a zirconium sample that underwent <?CDATA $13.6%$?> <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" overflow=\"scroll\"> <mml:mn>13.6</mml:mn> <mml:mi mathvariant=\"normal\">%</mml:mi> </mml:math> tensile extension from an initially well-annealed state. (Sub)grain boundary features are identified with apparent disclination line defects in them. The elastic fields of these features identified from the experiment are calculated.","PeriodicalId":18648,"journal":{"name":"Modelling and Simulation in Materials Science and Engineering","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135804905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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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