IF 6.1 1区工程技术

Wear Pub Date : 2025-08-21 DOI: 10.1016/j.wear.2025.206302

Jun Hua , Tai Peng , Xiaotong Zhu , Ruiming Ren , Zhiqiu Huang , Guanzhen Zhang

{"title":"Effect of rail surface hard defects on the wear damage and properties of high-speed wheel steel","authors":"Jun Hua , Tai Peng , Xiaotong Zhu , Ruiming Ren , Zhiqiu Huang , Guanzhen Zhang","doi":"10.1016/j.wear.2025.206302","DOIUrl":"10.1016/j.wear.2025.206302","url":null,"abstract":"<div><div>The effects of hard defects on rail steel surfaces on the wear damage and properties of the ER8C wheel material were studied. Results showed that abrasive and fatigue wear jointly determined the wear damage behavior of the wheel surface. High-hardness debris generated by hard defects during wear and post-spalling pits produced furrows and led to abrasive wear on the wheel surface. In the mild wear stage, the wear mechanisms were mainly abrasive, oxidative, and adhesive wear. In the severe wear stage, abrasive, oxidative, and fatigue wear were aggravated, which resulted in increased wear mass loss. Cracks were mainly initiated at the surface during different wear stages. In the mild wear stage, the length and angle of crack propagation were short. As the wear process intensified, the number of multilayer cracks in the surface material increased. One part of the crack bent toward the surface, while the other propagated deeper into the material at a larger angle, which considerably increased the crack propagation length and angle. This led to increased fatigue wear. With an increase in the slip ratio, the friction coefficient, fatigue and abrasive wear, and crack propagation degree increased. The wear damage further intensified, which aggravated the wear and substantially increased the wear mass loss.</div></div>","PeriodicalId":23970,"journal":{"name":"Wear","volume":"580 ","pages":"Article 206302"},"PeriodicalIF":6.1,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Frictional oxidation behavior of MoS2-Ti-LaF3 films under low temperature vacuum 低温真空条件下MoS2-Ti-LaF3薄膜的摩擦氧化行为

IF 6.1 1区工程技术

Wear Pub Date : 2025-08-20 DOI: 10.1016/j.wear.2025.206301

Yajun Li , Pengfei Ju , Yongqi Zhu , Shifan Ju , Jingzhou Liu , Panpan Li , Rui Zhang , Hongxuan Li , Xiaohong Liu , Huidi Zhou , Jianmin Chen , Li Ji

{"title":"Frictional oxidation behavior of MoS2-Ti-LaF3 films under low temperature vacuum","authors":"Yajun Li , Pengfei Ju , Yongqi Zhu , Shifan Ju , Jingzhou Liu , Panpan Li , Rui Zhang , Hongxuan Li , Xiaohong Liu , Huidi Zhou , Jianmin Chen , Li Ji","doi":"10.1016/j.wear.2025.206301","DOIUrl":"10.1016/j.wear.2025.206301","url":null,"abstract":"<div><div>Low temperatures induce novel effects and phenomena. Tribological studies of MoS<sub>2</sub>-Ti-LaF<sub>3</sub> films in a low temperature vacuum environment revealed that trace water molecular condensation at a specific temperature promotes a unique MoS<sub>2</sub> tribochemical reaction, resulting in high friction and wear. The MoS<sub>2</sub>-Ti-LaF<sub>3</sub> films were characterized using Raman spectroscopy, XPS, FESEM, TEM, and in situ vacuum quadrupole mass spectrometer. The results indicate that at 150 K, water molecules condense and physically adsorb on the film's surface, inhibiting MoS<sub>2</sub> shearing and rearrangement during friction. Under heavy load and friction heat, the adsorbed water molecules and trace O<sub>2</sub> further promote the chemical reaction with many damaged MoS<sub>2</sub> edge suspension bonds, defects, and other active sites, forming non-lubricious MoO<sub>3</sub>. MoO<sub>3</sub> is transferred to the steel ball's surface, resulting in increased friction and wear, and its oxidation degree is positively correlated with the friction coefficient. This new discovery provides a reference for the research of low temperature solid lubrication materials and broadens the consideration and selection of actual working conditions.</div></div>","PeriodicalId":23970,"journal":{"name":"Wear","volume":"580 ","pages":"Article 206301"},"PeriodicalIF":6.1,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144893084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Twin thickness dominates the tribological properties of (111)-oriented nanotwinned copper with micro-scale grain 微观晶粒（111）取向纳米孪晶铜的摩擦学性能主要受孪晶厚度的影响

IF 6.1 1区工程技术

Wear Pub Date : 2025-08-20 DOI: 10.1016/j.wear.2025.206299

Zhidong Zheng , Mingyan Huang , Xiaoye Huang , Hongfa Zhang , Yongjin Mai

引用次数: 0

Wear progress monitoring in torpedo ladles in steel industry 钢铁工业鱼雷钢包磨损过程监测

IF 6.1 1区工程技术

Wear Pub Date : 2025-08-18 DOI: 10.1016/j.wear.2025.206297

M. Maj , F. Tatzgern , H. Rojacz , K. Adam , M. Varga

{"title":"Wear progress monitoring in torpedo ladles in steel industry","authors":"M. Maj , F. Tatzgern , H. Rojacz , K. Adam , M. Varga","doi":"10.1016/j.wear.2025.206297","DOIUrl":"10.1016/j.wear.2025.206297","url":null,"abstract":"<div><div>Torpedo ladles are necessary transport carriages in steel production to move the molten crude iron from the blast furnace to the steel refining plant. This requires the ladles to be high temperature resistant and insulate well to preserve the temperature and hold the risk of solidification at bay. Therefore, the involved refractories lining the inside of the torpedo ladles are chosen mostly according to their thermal properties, although wear resistance of the materials to the transported good is also of major importance. In this work we combined investigations of the thermal behaviour with wear monitoring of the lining of a torpedo ladle to establish a methodology for investigating the wear evolution over the whole lifetime of this large-scale high temperature machinery.</div><div>The core of the investigation were detailed 3D measurements of the ladle's cavity and thereby quantitative information of the wear progress at different time intervals during the lifecycle of the ladles. The measurements allowed for a separation of different wear zones according to severity, namely the “splash zone” where the melt directly hits the ladle, the “melt zone” where during transport always liquid melt is present, and the “slag zone”, where the slag floats on the melt causing severe wear loss. Thermal imaging and localised long-term temperature measurements allowed for a study of the thermal consequences entailed by the wear onset. Thereby a wear assessment based on temperature measurements from the outside of the ladle was made possible. Additional “classical” damage analysis of the worn refractories completes the investigation to identify the wear mechanisms leading to the substantial wear losses observed.</div></div>","PeriodicalId":23970,"journal":{"name":"Wear","volume":"580 ","pages":"Article 206297"},"PeriodicalIF":6.1,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Determination of mechanical properties of physical vapor deposition tool coatings using machine learning 用机器学习测定物理气相沉积工具涂层的机械性能

IF 6.1 1区工程技术

Wear Pub Date : 2025-08-16 DOI: 10.1016/j.wear.2025.206296

C. Kalscheuer, K. Bobzin, X. Liu

{"title":"Determination of mechanical properties of physical vapor deposition tool coatings using machine learning","authors":"C. Kalscheuer, K. Bobzin, X. Liu","doi":"10.1016/j.wear.2025.206296","DOIUrl":"10.1016/j.wear.2025.206296","url":null,"abstract":"<div><div>The wear resistance of physical vapor deposition (PVD) coatings is heavily influenced by their elastic and plastic properties. These properties serve as essential inputs for finite element method (FEM) simulations of the thermomechanical load experienced by the coating during the cutting process to predict tool wear, including the elastic modulus for the characterization of elastic properties and parameters of the Ludwik-Hollomon model for plastic behavior. In this study, machine learning models are developed to directly map load-depth curves from nanoindentation to elastic modulus and Ludwik-Hollomon parameters of the coating. A FEM simulation model of nanoindentation is employed to generate a dataset comprising load-depth curves from a wide range of input mechanical properties. For each definition of mechanical properties, simulations of nanoindentation at two different indentation forces are run to generate the dataset. Several machine learning models including support vector regression (SVR), multilayer perceptron (MLP), long short-term memory (LSTM) and gated recurrent unit (GRU) are then trained, validated and compared using this dataset. The inputs to these models consist of simulated load-depth curves, with the target being mechanical properties of coatings. Among these machine learning models, SVR achieves the best accuracy for predicting elastic modulus and GRU achieves the best accuracy for predicting plastic properties. Ultimately, a hybrid model combining SVR and GRU is used to predict mechanical properties of TiAlCrN coatings using experimental load-depth curves. FEM simulations using the predicted mechanical properties show good alignment with nanoindentation experiments at two different forces. The determined properties can serve as input parameters for FEM models simulating thermomechanical load during the cutting process.</div></div>","PeriodicalId":23970,"journal":{"name":"Wear","volume":"580 ","pages":"Article 206296"},"PeriodicalIF":6.1,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144885691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A novel grey-box based friction model for a wide range of machining conditions 一种适用于多种加工条件的基于灰盒的新型摩擦模型

IF 6.1 1区工程技术

Wear Pub Date : 2025-08-16 DOI: 10.1016/j.wear.2025.206295

Jan Wolf , Nithin Kumar Bandaru , Martin Dienwiebel , Hans-Christian Möhring

{"title":"A novel grey-box based friction model for a wide range of machining conditions","authors":"Jan Wolf , Nithin Kumar Bandaru , Martin Dienwiebel , Hans-Christian Möhring","doi":"10.1016/j.wear.2025.206295","DOIUrl":"10.1016/j.wear.2025.206295","url":null,"abstract":"<div><div>Modelling the friction behaviour of cutting tools is a vital step towards understanding the complex tribo-mechanical system in cutting necessary for further improving coatings. However, measuring the friction behaviour during actual cutting is challenging due to its dependence on locally changing process conditions along the cutting tool such as sliding velocity and normal pressure. Thus this study introduces a novel tribometer to identify friction coefficients under a wide variety of normal pressures (914.7 MPa–2170 MPa) and sliding velocities (20 m/min to 250 m/min) relevant for machining. Subsequently, the adhesive friction coefficient is determined inversely by modelling the experiments via Finite Element Analysis. The wear behaviour of coated pins is discussed for a wide range of contact pressures and sliding velocities relevant for cutting. A custom Python interface is presented which enables the local prediction of velocity and normal pressure dependent friction coefficients along the cutting edge within machining simulations. Common machine learning libraries can therefore directly be introduced in the FEA engine. Supervised machine learning regression models are trained and evaluated regarding their predictive capability. The Grey-Box model allows the AI-based local prediction of friction coefficients in cutting simulations based on the process conditions at the tool-chip interface.</div></div>","PeriodicalId":23970,"journal":{"name":"Wear","volume":"580 ","pages":"Article 206295"},"PeriodicalIF":6.1,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Grey-box modelling for tool wear prediction in milling: Fusion of finite element insights, time-resolved cutting signals and metaheuristic feature selection 铣削中刀具磨损预测的灰盒建模：有限元见解、时间分辨切削信号和元启发式特征选择的融合

IF 6.1 1区工程技术

Wear Pub Date : 2025-08-16 DOI: 10.1016/j.wear.2025.206292

Amirmohammad Jamali , Amod Kashyap , Johannes Schneider , Michael Stueber , Volker Schulze

{"title":"Grey-box modelling for tool wear prediction in milling: Fusion of finite element insights, time-resolved cutting signals and metaheuristic feature selection","authors":"Amirmohammad Jamali , Amod Kashyap , Johannes Schneider , Michael Stueber , Volker Schulze","doi":"10.1016/j.wear.2025.206292","DOIUrl":"10.1016/j.wear.2025.206292","url":null,"abstract":"<div><div>Reliable prediction of tool wear is essential for ensuring productivity, quality, and cost-efficiency in modern machining operations. This study presents a hybrid Grey-Box machine learning framework that combines White-Box finite element simulation outputs (interface temperature, relative sliding velocity), process parameters (feed speed, cutting velocity, depth of cut), dynamic time-series features from cutting force measurements, with a Black-Box machine learning model to predict tool wear in high-speed milling. The experimental campaign involved TiN-coated and uncoated carbide tools under dry machining conditions, with flank and rake wear measured after each cutting pass. Finite element simulations were conducted to extract localized thermomechanical features, such as interface temperature and relative sliding velocity, which were used as physically meaningful inputs. A two-step feature selection method—based on analysis of variance (ANOVA) and the whale optimization algorithm (WOA)—was employed to identify the most relevant input features. Among the machine learning models tested, the gradient boosting regressor (GBR) showed the highest accuracy, achieving coefficient of determination (R<sup>2</sup>) scores of 0.953 for rake wear and 0.920 for flank wear. Omitting White-Box features resulted in significantly lower performance, confirming their critical role. The model's predictions were also in line with the unseen test cases. These results highlight the effectiveness of combining simulation-informed features with empirical data for tool condition monitoring, offering a scalable and interpretable approach for predictive maintenance in smart manufacturing.</div></div>","PeriodicalId":23970,"journal":{"name":"Wear","volume":"580 ","pages":"Article 206292"},"PeriodicalIF":6.1,"publicationDate":"2025-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144885690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time monitoring of polycrystalline diamond compact drill bit wear using semi-supervised clustering and deep learning 基于半监督聚类和深度学习的聚晶金刚石钻头磨损实时监测

IF 6.1 1区工程技术

Wear Pub Date : 2025-08-15 DOI: 10.1016/j.wear.2025.206294

Mohamed Zinelabidine Doghmane , Idir Kessai , Kong Fah Tee , Hossein Emadi , Qingwang Yuan

{"title":"Real-time monitoring of polycrystalline diamond compact drill bit wear using semi-supervised clustering and deep learning","authors":"Mohamed Zinelabidine Doghmane , Idir Kessai , Kong Fah Tee , Hossein Emadi , Qingwang Yuan","doi":"10.1016/j.wear.2025.206294","DOIUrl":"10.1016/j.wear.2025.206294","url":null,"abstract":"<div><div>Optimizing drilling operations is crucial for companies seeking to enhance their systems' performance and minimize operational issues. Since smooth progress is difficult to achieve with a worn drill bit, real-time monitoring of its wear state is essential for optimizing the drilling process. Recent advances in machine learning and deep learning algorithms have enabled the widespread adoption of real-time models in the upstream oil and gas industry. This study developed a deep learning-based decision system for monitoring the drill bit wear state using the Seeded K-Means algorithm and a Convolutional Neural Network (CNN), followed by a Bernoulli distribution model. We used drilling data of thirteen wells from Algerian oilfields as a case study to develop, train, and test the proposed real-time system. The results demonstrated that the developed model successfully classified cutter wear rates with 99 % precision, an F1-score of 100 %, and 99 % recall. Compared to the Random Forest (RF) classifier, which required seven input features to achieve an overall precision of 96 %, the proposed model achieved superior results using only a single input feature. Furthermore, we tested the generalizability of the developed real-time model on two additional wells, where a mathematical wear model was also employed to determine the optimal moment for pulling the bit out of the hole. As expected, the outcomes provided by the proposed model aligned with those obtained from the mathematical model. Finally, field test results confirmed that the developed system can assist rig operators in making instantaneous, data-driven decisions on the PDC bit wear state during drilling.</div></div>","PeriodicalId":23970,"journal":{"name":"Wear","volume":"580 ","pages":"Article 206294"},"PeriodicalIF":6.1,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Substrate pre-treatment mediated surface integrity and its effect on wear characteristics of TiAlN-coated tools 基体预处理介导的表面完整性及其对钛合金涂层刀具磨损特性的影响

IF 6.1 1区工程技术

Wear Pub Date : 2025-08-15 DOI: 10.1016/j.wear.2025.206293

Runzi Wang , Ruitao Peng , Xinzi Tang , Linfeng Zhao , Xiaofang Huang

{"title":"Substrate pre-treatment mediated surface integrity and its effect on wear characteristics of TiAlN-coated tools","authors":"Runzi Wang , Ruitao Peng , Xinzi Tang , Linfeng Zhao , Xiaofang Huang","doi":"10.1016/j.wear.2025.206293","DOIUrl":"10.1016/j.wear.2025.206293","url":null,"abstract":"<div><div>This study systematically investigates the mediatory mechanism of different surface pre-treatment processes (rough grinding, fine grinding, polishing, and sandblasting composite pre-treatment) on the surface integrity and wear characteristics of TiAlN-coated Ti(C, N)-based cermet cutting tools. Experimental results show that substrate pre-treatment significantly affects the surface micro-morphology, mechanical properties, and film-substrate interfacial adhesion of TiAlN-coated tools. Among the pre-treatments, coatings deposited on substrates subjected to polishing composite pre-treatment exhibit the smoothest surface. Substrates subjected to sandblasting composite pre-treatment significantly enhance coating hardness, H/E∗ ratio, H<sup>3</sup>/E∗<sup>2</sup> ratio, and adhesion strength by introducing higher residual compressive stress on the coating surface, while also demonstrating the best oxidation resistance and the lowest oxidation weight gain. Tests on dry cutting of ductile iron show that, compared with rough grinding, fine grinding, and polishing pre-treatments, the sandblasted pre-treated tool exhibits the lowest flank face wear due to its excellent surface integrity (high hardness, high residual compressive stress, and high adhesion strength) and outstanding oxidation resistance, as well as the lowest cutting force and fluctuation index, which reduce the accumulation of cutting heat and stress impact. The tool wear failure modes are mainly characterized by local spalling and slight notch fracture, with its service life being 25 % longer than that of the rough grinding pre-treatment process and demonstrating superior wear resistance. This research provides a theoretical basis and technical reference for optimizing the surface pre-treatment process of high-performance coated cutting tools.</div></div>","PeriodicalId":23970,"journal":{"name":"Wear","volume":"580 ","pages":"Article 206293"},"PeriodicalIF":6.1,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144858302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cracking mechanisms of different grain boundaries in a high-nitrogen austenitic stainless steel under cavitation erosion 高氮奥氏体不锈钢在空化腐蚀作用下不同晶界的开裂机理

IF 6.1 1区工程技术

Wear Pub Date : 2025-08-12 DOI: 10.1016/j.wear.2025.206290

Zhenhua Wang , Qingrui Xiao

{"title":"Cracking mechanisms of different grain boundaries in a high-nitrogen austenitic stainless steel under cavitation erosion","authors":"Zhenhua Wang , Qingrui Xiao","doi":"10.1016/j.wear.2025.206290","DOIUrl":"10.1016/j.wear.2025.206290","url":null,"abstract":"<div><div>High-nitrogen austenitic stainless steels exhibit beneficial mechanical and chemical properties and are often used in environments involving a high risk of cavitation erosion (CE). These steels contain many types of grain boundaries with various CE resistances. In this study, 18Mn18Cr0.6N steel was subjected to CE, and cracks at different grain boundaries were investigated by electron backscatter diffraction, focused ion beam milling, and transmission electron microscopy. Step formation was found to be a key prerequisite for the crack initiation in CE. The step formation at the Σ3 boundary is unique owing to grain rotation, which is caused by the narrow deformation band composed of dense stacking faults and deformation twins in the Σ3 boundary region. For other types of boundaries, such as Σ9, Σ33c, and random high-angle grain boundaries, steps form because of the uncoordinated deformation of grains on both sides of the boundary. Cracks are prone to propagate along {111} planes; stacking fault regions, such as the Σ3 boundary region and slip band, provide the major paths for crack propagation. CE cracks have difficulty passing through the stacking fault regions laterally. Finally, approaches aiming to improve the CE resistance and factors affecting stacking fault formation were considered herein.</div></div>","PeriodicalId":23970,"journal":{"name":"Wear","volume":"580 ","pages":"Article 206290"},"PeriodicalIF":6.1,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144841609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

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