International Journal of Material Forming最新文献

{"title":"Optimization of the microstructure and mechanical properties of SAPH440 dual-phase steel for automotive parts","authors":"B. Zibaei,&nbsp;A. Kamyabi-Gol,&nbsp;M. Mazinani,&nbsp;B. Beidokhti","doi":"10.1007/s12289-026-02078-8","DOIUrl":"10.1007/s12289-026-02078-8","url":null,"abstract":"<div><p>The effect of various variables, including intercritical annealing temperature, holding time, initial condition, and quenching media, on the mechanical properties of an industrial-grade SAPH440 low-carbon steel sheet was investigated. The main objective was to improve the overall mechanical properties of the steel by simultaneously increasing the ultimate tensile-to-yield strength ratio (T/Y), ductility (in terms of reduction in area), and work hardening rate. The results showed that the initial condition of the steel was the most effective parameter (~ 55%), and holding time at an intercritical temperature was the least effective parameter (~ 0%). The effect of intercritical annealing time on the microstructure and mechanical properties was evaluated during uniaxial tensile loading. After annealing, five steel specimens were heated to an intercritical temperature of 730 °C for 5, 10, 15, 20, and 30 min., and subsequently water-quenched. It was found that the yield point phenomenon in specimens with a holding time of 5–15 min. was similar to that of the fully annealed specimen due to insufficient volume fraction of martensite (&lt; 5%). Cementite spheroidization in these three specimens was found to be responsible for the observed increase in the post-neck region with holding time.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148927041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of the edge magnetic shield on transverse flux induction heating of endless-rolling strip","authors":"FangZhou Shi,&nbsp;MengYe Wang,&nbsp;Xinmiao Shao,&nbsp;WenShuai Wang,&nbsp;Meng Yan,&nbsp;Ce Ji,&nbsp;Huagui Huang","doi":"10.1007/s12289-026-02074-y","DOIUrl":"10.1007/s12289-026-02074-y","url":null,"abstract":"<div><p>Endless rolling represents a key development direction in short-process steel manufacturing, driven by its significant advantages: high dimensional accuracy, superior uniformity of microstructure and properties, high yield, and low process energy consumption. To satisfy the stringent temperature control requirements across the entire strip cross-section, these production lines are equipped with induction heating modules. However, the intrinsic magnetic field distribution of Transverse Flux Induction Heating (TFIH), combined with the skin effect, leads to severe overheating at the strip edges, which compromises temperature control accuracy across the strip’s width. This study, therefore, investigates the magnetic shield as a core control element to elucidate how its geometric parameters and spatial positioning regulate transverse temperature uniformity. For this purpose, an experimental platform for moving TFIH was constructed. The results indicate that an increase in the horizontal extension of the shield beyond the strip edge (<i>L</i>₁) elevated the temperature rise in the edge region from 766.0 °C to 794.0 °C. Conversely, increasing the coverage width over the strip (<i>L</i>₂) shifted the thermal peak further from the strip edge, causing the standard deviation of the transverse temperature to first decrease and then increase. Furthermore, increasing the vertical shield-to-strip distance (<i>H</i>₁) from 7.5 mm to 20 mm significantly reduced the transverse temperature standard deviation from 4.2 to 2.4, whereas increasing the shield thickness (H) across the same range yielded only a modest reduction to 3.65. The accuracy of the finite element analysis was validated through a comparison of simulation and experimental results. The findings of this study establish a robust theoretical foundation for the design and optimization of high-performance TFIH systems.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148926868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable failure-load prediction of externally bonded FRP-strengthened reinforced concrete beams using mechanics-based machine learning and consensus sensitivity analysis","authors":"Amit Thoriya,&nbsp;Husain Rangwala,&nbsp;Tarak Vora,&nbsp;Bharat Shah","doi":"10.1007/s12289-026-02075-x","DOIUrl":"10.1007/s12289-026-02075-x","url":null,"abstract":"<div><p>Accurate prediction of the failure load of reinforced concrete (RC) beams strengthened with externally bonded Fiber-Reinforced Polymer (FRP) composites remains challenging because four competing failure modes—concrete crushing, FRP rupture, intermediate crack debonding, and plate-end debonding—are governed by different but interacting flexural, bond, and anchorage mechanisms. Existing machine learning (ML) studies on this application rely exclusively on single-method interpretability and underutilise mechanics-based feature engineering. This study develops an interpretable ML framework using 624 externally bonded FRP-strengthened RC beam specimens. Sixteen mechanics-based engineered features encoding force equilibrium, stiffness compatibility, and geometric ratios are engineered, forming a 39-feature input space. Four deterministic ensemble models (Random Forest, XGBoost, LightGBM, CatBoost) and the probabilistic NGBoost are trained with Optuna hyperparameter optimization. XGBoost achieves the highest R² of 0.9743 (RMSE = 15.73 kN), while CatBoost yields the lowest MAE of 9.21 kN and the most uniform failure-mode-wise accuracy. A four-method consensus sensitivity analysis combining SHAP, permutation importance, Morris screening, and Sobol indices consistently ranks the total tensile force capacity T_total = A_sf_y + A_ff_fu as the most influential mechanics-guided predictive descriptor within the assembled database. Five of the top ten consensus-ranked features are mechanics-based engineered quantities. LIME analysis of three representative specimens provides local examples that are broadly consistent with the global sensitivity trends. Separately, NGBoost with cross-conformal calibration provides 95% prediction intervals with 97.6% empirical coverage and a mean width of 70.9 kN, the first reported coverage-guaranteed uncertainty quantification for this domain. All models substantially outperform ACI 440.2R-23, demonstrating 44–49% improvement in R² and 45–64% reduction in MAE.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148926858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Composition-holdout validation of gaussian-process surrogates for reliability-aware multi-property prediction in AA6061 hybrid composites","authors":"Obinna Onyebuchi Barah,&nbsp;Abdulrazak Jinadu Otaru,&nbsp;Ige Bori,&nbsp;Sami F. Khalil","doi":"10.1007/s12289-026-02081-z","DOIUrl":"10.1007/s12289-026-02081-z","url":null,"abstract":"<div><p>Machine-learning studies of composite materials can report excellent accuracy even when repeated specimens from the same formulation are distributed across training and test sets, because the design variable itself is not truly withheld. This study quantifies that risk using an experimentally generated AA6061 dataset containing 12 independently prepared specimens at 0, 5, 10, and 15 wt% total hybrid reinforcements. The reinforcement comprised plantain stem fibre ash, eucalyptus wood ash, and periwinkle shell particulate at a fixed 1:1:1 mass ratio. Ultimate tensile strength, Brinell hardness, and Charpy impact energy were modelled using linear regression, quadratic ridge regression, and Gaussian-process regression (GPR) with Matérn-5/2 and radial-basis kernels. Two validation tasks were deliberately separated. Replicate-holdout validation tested new specimens at known compositions, whereas leave-one-composition-out validation tested an unseen reinforcement loading. Matérn GPR achieved R² values of 0.992, 0.994, and 0.994 for tensile strength, hardness, and impact energy under replicate holdout, but the corresponding composition-holdout R² values fell to -0.070, -0.699, and − 0.874. Nominal 95% GPR interval coverage also decreased to 50%, 75%, and 75%, respectively. After fitting all available data, the conservative multi-property screening score was highest near the experimentally observed H10 region. Because the model did not recover the H10 peak when that composition was withheld, this result is interpreted only as a model-smoothed description of the measured response and not as evidence of an optimized formulation. Within this sparse experimental dataset, the findings demonstrate the practical importance of aligning the validation unit with the intended composite-design task and identify 2.5, 7.5, and 12.5 wt% as informative next compositions for expanding the experimentally supported design space.</p><h3>Graphical Abstract</h3><div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148926859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven intelligent optimization of cold-rolled aluminum strip thickness control: a hybrid TCN-LSTM-AM and JITL-XGBoost approach","authors":"Xianfeng Gong,&nbsp;Zhiliang Lyu,&nbsp;Xiaomin Zhou,&nbsp;Hechuan Song,&nbsp;Rui Yuan,&nbsp;Han Xiao,&nbsp;Tianyang Wu","doi":"10.1007/s12289-026-02070-2","DOIUrl":"10.1007/s12289-026-02070-2","url":null,"abstract":"<div><p>Thickness control of cold-rolled aluminum strips is a complex process influenced by several coupled parameters. The acceleration stage refers to the transient period after strip threading, during which the mill speed gradually increases from the initial low-speed rolling condition to the steady rolling speed. During this stage, process variables such as rolling force, strip tension, roll gap, and main speed fluctuate significantly, weakening the control capability of the thickness control system. In this paper, a new roll gap optimization strategy was proposed, which integrates the TCN-LSTM-AM (temporal convolutional network-long short-term memory-attention mechanism) thickness prediction model and the JITL-XGBoost (just-in-time learning-extreme gradient boosting) optimization model. It enhances the thickness-control capability of the system during acceleration. The TCN-LSTM-AM model extends the hierarchical information sensing capabilities. It effectively captures time dependencies and enhances attention to important hidden information with temporal correlations. A JITL-XGBoost model was proposed to regress the roll gap during the acceleration based on historical superior data. Combined with the predicted future strip exit thickness, the roll gap optimization strategy was proposed. Experiments show that the TCN-LSTM-AM model has a mean absolute error of 0.259 <span>(:{upmu:}text{m})</span>, significantly improving the thickness prediction accuracy. The JITL-XGBoost model has a regression mean absolute error of 3.7 <span>(:{upmu:}text{m})</span>, which accurately estimates the optimal roll gap value under the current process conditions. The effectiveness of the roll gap optimization strategy has been confirmed in practical applications.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148782432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Governing mechanism of mechanical vibration on solidification behavior in compound cast-rolling","authors":"Mingjie Wang,&nbsp;Yaocheng Liang,&nbsp;Kun Hu,&nbsp;Hongbo He,&nbsp;Yujun Yuan,&nbsp;Jingzhe Xiong,&nbsp;Xiong Nie,&nbsp;Zhenwei Chen,&nbsp;Chao Zhao,&nbsp;Minghan Sun","doi":"10.1007/s12289-026-02071-1","DOIUrl":"10.1007/s12289-026-02071-1","url":null,"abstract":"<div><p>Compound strip vibration cast-rolling (CVCR) has demonstrated excellent effectiveness in regulating interfacial bonding strength through mechanical vibration. However, as a highly coupled thermal-mechanical-flow process, the influence of vibration on the flow and solidification behavior of the molten pool remains insufficiently understood. To address this gap, thermal-fluid coupled (TFC) numerical simulation of the process was combined with experimental characterization of the solidification microstructure of the cast-rolled strips. Results show that vibration induces periodic evolution of the flow field, generating a stirring effect that refines the solidification microstructure and promotes dendrite equiaxation. Quantitative simulation analysis further reveals that increasing frequency and amplitude produce comparable stirring effects, whereas amplitude exerts a stronger influence on the solid-liquid interface (SLI) position, which is more likely to compromise process stability. Accordingly, a process control strategy of restraining amplitude while increasing frequency is proposed. This study provides theoretical guidance for the process design of CVCR and contributes to advancing its practical application.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148782088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Energy analysis of thick plate rolling forces based on tangential velocity field","authors":"Li Wang,&nbsp;Zhihang Li,&nbsp;Lei Zhang,&nbsp;Jie Sun,&nbsp;Tongyu Zhang","doi":"10.1007/s12289-026-02073-z","DOIUrl":"10.1007/s12289-026-02073-z","url":null,"abstract":"<div><p>Rolling force prediction is crucial for process automation and quality enhancement in thick plate rolling. Existing velocity field models, however, fail to accurately characterize progressive metal flow and cannot reliably predict rolling forces. This study develops a kinematically admissible tangential velocity field derived from metal flow behaviour within the deformation zone. The geometric midline (GM) yield criterion is then incorporated to analytically derive the rolling energy. A comprehensive rolling force prediction model is established by formulating the power components of internal deformation, friction, and shear. The model is validated against experimental data and Sims’ classical model. Parametric analysis reveals that a higher friction coefficient shifts the neutral point toward the entry side. While both the reduction rate and the thickness-to-radius rate significantly influence the rolling force, the latter exerts a more pronounced effect. These findings demonstrate that integrating the tangential velocity field with the GM yield criterion substantially improves prediction accuracy, providing valuable theoretical insights for optimizing the rolling process.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148782089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effect of pulsed magnetic field aging treatment on the microstructure and mechanical property of a Mg-Gd-Y alloy","authors":"Yiping Wu,&nbsp;Yuzhen Jia,&nbsp;Yuan Xiong,&nbsp;Jia Peng,&nbsp;Xiangdi Liu,&nbsp;Jian Li","doi":"10.1007/s12289-026-02064-0","DOIUrl":"10.1007/s12289-026-02064-0","url":null,"abstract":"<div><p>Both conventional aging and pulsed magnetic field aging treatments were conducted on an extruded Mg-8Gd-4Y-1Nd-0.5Zr alloy by a self-developed magnetic field aging treatment device. The as-extruded alloy exhibits an ultimate tensile strength of 293.4 MPa, a yield strength of 229.8 MPa, and an elongation of 4.1%. After conventional aging, its strength increases significantly while the elongation decreases. In contrast, pulsed magnetic field aging enhances both the strength and elongation of the alloy. Following the 3 A pulsed magnetic field aging, the alloy achieves an ultimate tensile strength of 321 MPa, a yield strength of 250.4 MPa, and an elongation of 5.4%. Pulsed magnetic field aging not only promotes the growth of strengthening precipitates, but also alters the grain boundary morphology, transforming originally straight grain boundaries into serrated ones.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148782087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence assisted data-driven design and lifecycle forecasting of cement-based hybrid materials for environmental sustainability","authors":"Vikrant S. Vairagade","doi":"10.1007/s12289-026-02069-9","DOIUrl":"10.1007/s12289-026-02069-9","url":null,"abstract":"&lt;div&gt;&lt;p&gt;In view of the increasing urgent necessity to curtail the environmental footprint of cement production, there has been a rapid development in hybrid cement-based materials, which incorporate alternative binders, supplementary cementitious materials (SCMs), or eco-efficient admixtures. This research work discusses an iterative artificial intelligence assisted data-driven design framework for cement-based hybrid materials targeting enhanced mechanical performance and environmental sustainability. The proposed framework integrates five novel artificial intelligence (AI) approaches to optimizing hybrid cement composition, durability, and lifecycle performance. A self-supervised multi-modal feature selector (SSMM-FS) identifies critical input parameters from chemical, structural, or environmental datasets, thereby simplifying the model by 50% without sacrificing its predictive power. A physics-informed GAN-HMS (Generative adversarial network), reinforced by thermodynamic constraints, conceives feasible hybrid formulations that can boost their strengths up to 20% and reduce CO2 emissions by ~ 30%. A graph neural network-based microstructural integrity classifier (GNN-MIC) is capable of assessing internal microstructural performance with 99.1% accuracy when classifying crack propagation and interfacial bond dynamics. These insights go into quantum inspired reinforcement learning model (QIRL-CDO) that recommends the optimal compositions based on durability, strength, and sustainability trade-offs. Finally, a transformer-based degradation forecasting model (TMD-F) for predicting long-term deterioration enables predictive maintenance while extending structural service life. This work manifests artificial intelligence-driven, scalable strategies in the sustainable cement development process. The integrated system improves candidate-formulation feasibility by 22%, guarantees a 45% improvement in computational efficiency, and reduces the risks of premature failure in the materials by a large extent. A multimodal feature screening, physics-constrained formulation generation, graph-based microstructure assessment, durability-oriented reinforcement optimization, and transformer-based lifecycle forecasting are some of the other things that are being tested in this work. The most important new idea that has been developed as a result of this work is not simply the use of machine learning to predict how strong cement will be. Within a single iterative decision pipeline, the proposed methodology establishes connections between composition, microstructure, environmental exposure, and lifecycle risk considerations. In contrast to prior research, which typically focused on strength prediction, mixture optimization, or microstructure classification in isolation, this type of investigation is more comprehensive. Consequently, the framework generates potential hybrid cement compositions, in addition to readable physical indications, estimates of durability that ta","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fatigue life of roll-corrected aerospace structural components: a coupled fem study on residual stress and process parameters","authors":"Qilong Shi,&nbsp;Laixiao Lu,&nbsp;Changguan Xu,&nbsp;Shourong Jiao,&nbsp;Zhonglei Wang,&nbsp;Qingqiang Chen,&nbsp;Rui Wang","doi":"10.1007/s12289-026-02065-z","DOIUrl":"10.1007/s12289-026-02065-z","url":null,"abstract":"<div><p>Bilateral rolling process (BRP) is widely used to correct machining distortions in aluminum alloy aerospace structural components. However, it alters the original microplastic deformation and residual stress, affecting fatigue performance. In this study, the finite element method (FEM) is employed to create a simulation model for rolling correction–fatigue analysis, and the effects of rolling parameters and positions on fatigue life are examined. First, a continuous simulation model was developed and experimentally validated. Afterward, the impact of the rolling parameters and positions under periodic external loads was analyzed using a three-compartment frame as the research object. Finally, the principle of stress superposition was applied to reveal the coupled impact of rolling-induced residual stress and operational stress on the fatigue life of workpieces. The results show that increased rolling depth decreases fatigue life, and stress concentration caused by gaps in rolling positions significantly reduces fatigue life. These findings confirm that the rolling process parameters and position significantly affect the fatigue life of aeronautical structural components, providing a theoretical basis for optimizing the BRP to enhance structural reliability of aeronautical structural components under the studied uniaxial cyclic loading.</p></div>","PeriodicalId":591,"journal":{"name":"International Journal of Material Forming","volume":"19 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148751209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}