Pub Date : 2015-12-31DOI: 10.2174/1874155X01509011110
Liu Fei, Zhang Dongliang
*Address correspondence to this author at the Guilin University of Aerospace Technology, Guilin 541004, P.R. China; Tel: 15878350053; E-mail: 28455208@qq.com RETRACTION The Publisher and Editor have retracted this article [1] in accordance with good ethical practices. After a thorough investigations we believe that the peer review process was compromised. The article was published on-line on 26-06-2015.
{"title":"Retraction Note: Research on Desk Personalized Ventilation in Winter Based on CFD","authors":"Liu Fei, Zhang Dongliang","doi":"10.2174/1874155X01509011110","DOIUrl":"https://doi.org/10.2174/1874155X01509011110","url":null,"abstract":"*Address correspondence to this author at the Guilin University of Aerospace Technology, Guilin 541004, P.R. China; Tel: 15878350053; E-mail: 28455208@qq.com RETRACTION The Publisher and Editor have retracted this article [1] in accordance with good ethical practices. After a thorough investigations we believe that the peer review process was compromised. The article was published on-line on 26-06-2015.","PeriodicalId":267392,"journal":{"name":"The Open Mechanical Engineering Journal","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124109845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The temperature rise exceeds over 1000°C in cutting process while working with PCBN tools high speed machining. This intense temperature results in wearing of tools and thus diffusion and oxidation are mainly responsible for wearing. This paper proposes the oxidation and diffusion wearing for PCBN cutting tools and analysis has been carried out considering thermodynamics principles. The next step is to find out dissoluation concentration of PCBN tool materials. The temperature range is different for these materials. The Gibbs free energy criterion is utilized for analysis purposes and validates the formed diffusion reaction rules in extreme temperature scenario. The PCBN tools were used for carrying out machining tests at different speeds 50, 95,100 and 180 m/min, feed of 0.1, 0.2 and depth of cut was 0.1, 0.8, 1, and 1.5 mm respectively on controlled lathe machine PUMA300LM. It was revealed that experimentals results matches with theoratical data. These findings will be utilized for future reference for tools designing and material selection.
{"title":"PCBN Tool Wear for Hard Materials Based on ThermodynamicsPrincipals","authors":"Fang Shao, Yu Ting Wang, Yingqun Xiao, Lihua Xiao, Kecheng Zhang","doi":"10.2174/1874155X01509011103","DOIUrl":"https://doi.org/10.2174/1874155X01509011103","url":null,"abstract":"The temperature rise exceeds over 1000°C in cutting process while working with PCBN tools high speed machining. This intense temperature results in wearing of tools and thus diffusion and oxidation are mainly responsible for wearing. This paper proposes the oxidation and diffusion wearing for PCBN cutting tools and analysis has been carried out considering thermodynamics principles. The next step is to find out dissoluation concentration of PCBN tool materials. The temperature range is different for these materials. The Gibbs free energy criterion is utilized for analysis purposes and validates the formed diffusion reaction rules in extreme temperature scenario. The PCBN tools were used for carrying out machining tests at different speeds 50, 95,100 and 180 m/min, feed of 0.1, 0.2 and depth of cut was 0.1, 0.8, 1, and 1.5 mm respectively on controlled lathe machine PUMA300LM. It was revealed that experimentals results matches with theoratical data. These findings will be utilized for future reference for tools designing and material selection.","PeriodicalId":267392,"journal":{"name":"The Open Mechanical Engineering Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114360409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-11-09DOI: 10.2174/1874155X01509011092
Xu Xi
{"title":"The Design and Application of Mechanical Automation Technology inFood Factory Network Management System Based on FractionalAlgorithm","authors":"Xu Xi","doi":"10.2174/1874155X01509011092","DOIUrl":"https://doi.org/10.2174/1874155X01509011092","url":null,"abstract":"","PeriodicalId":267392,"journal":{"name":"The Open Mechanical Engineering Journal","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126068397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-11-09DOI: 10.2174/1874155X01509011097
Zhu Lini
This paper introduces and analyses the data mining in the management of students' sports grades. We use the decision tree in analysis of grades and investigate attribute selection measure including data cleaning. We take sports course score of some university for example and produce decision tree using ID3 algorithm which gives the detailed cal- culation process. Because the original algorithm lacks termination condition, we propose an improved algorithm which can help us to find the latency factor which impacts the sports grades. With the rapid development of higher education, sports grade analysis as an important guarantee for the scientific management constitutes the main part of the sports educa- tional assessment. The research on application of data min- ing in management of students' grades wants to talk how to get the useful uncovered information from the large amounts of data with the data mining and grade management (1-5). It introduces and analyses the data mining in the management of students' grades. It uses the decision tree in analysis of grades. It describes the function, status and deficiency of the management of students' grades. It tells us how to employ the decision tree in management of students' grades. It im- proves the ID3 arithmetic to analyze the students' grades so that we could find the latency factor which impacts the grades. If we find out the factors, we can offer the decision- making information to teachers. It also advances the quality of teaching (6-10). The sports grade analysis helps teachers to improve the teaching quality and provides decisions for school leaders. The decision tree-based classification model is widely used as its unique advantage. Firstly, the structure of the de- cision tree method is simple and it generates rules easy to understand. Secondly, the high efficiency of the decision tree model is more appropriate for the case of a large amount of data in the training set. Furthermore the computation of the decision tree algorithm is relatively not large. The decision tree method usually does not require knowledge of the train- ing data, and specializes in the treatment of non-numeric data. Finally, the decision tree method has high classification accuracy, and it is to identify common characteristics of li- brary objects, and classify them in accordance with the clas- sification model.
{"title":"Application Research of Decision Tree Algorithm in Sports Grade Analysis","authors":"Zhu Lini","doi":"10.2174/1874155X01509011097","DOIUrl":"https://doi.org/10.2174/1874155X01509011097","url":null,"abstract":"This paper introduces and analyses the data mining in the management of students' sports grades. We use the decision tree in analysis of grades and investigate attribute selection measure including data cleaning. We take sports course score of some university for example and produce decision tree using ID3 algorithm which gives the detailed cal- culation process. Because the original algorithm lacks termination condition, we propose an improved algorithm which can help us to find the latency factor which impacts the sports grades. With the rapid development of higher education, sports grade analysis as an important guarantee for the scientific management constitutes the main part of the sports educa- tional assessment. The research on application of data min- ing in management of students' grades wants to talk how to get the useful uncovered information from the large amounts of data with the data mining and grade management (1-5). It introduces and analyses the data mining in the management of students' grades. It uses the decision tree in analysis of grades. It describes the function, status and deficiency of the management of students' grades. It tells us how to employ the decision tree in management of students' grades. It im- proves the ID3 arithmetic to analyze the students' grades so that we could find the latency factor which impacts the grades. If we find out the factors, we can offer the decision- making information to teachers. It also advances the quality of teaching (6-10). The sports grade analysis helps teachers to improve the teaching quality and provides decisions for school leaders. The decision tree-based classification model is widely used as its unique advantage. Firstly, the structure of the de- cision tree method is simple and it generates rules easy to understand. Secondly, the high efficiency of the decision tree model is more appropriate for the case of a large amount of data in the training set. Furthermore the computation of the decision tree algorithm is relatively not large. The decision tree method usually does not require knowledge of the train- ing data, and specializes in the treatment of non-numeric data. Finally, the decision tree method has high classification accuracy, and it is to identify common characteristics of li- brary objects, and classify them in accordance with the clas- sification model.","PeriodicalId":267392,"journal":{"name":"The Open Mechanical Engineering Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122873304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High-voltage circuit breakers are mechanical switching devices which connect and break current circuits (operating currents and fault currents) and carry the nominal current in closed position. As a result of multi-running, the shaft sleeve in operation mechanism could slip and even strip from the shaft at the hinge joint, which decreases the system reliability. In this work, investigations on the cause of sleeve slippage are proceeded, and the dimension parameters of shafting components where sleeve slippage occurs are optimized by incorporating a quasi-static mechanical model with Taguchi method. By developing and analyzing the mechanical model for the shaft sleeve slippage, it indicates that the sleeve slippage displacement has a same variation tendency with the shaft deflection. Theoretical equations are derived by using force analysis and superposition method to descript the analytic function of the shaft deflection. As the variables within the analytic function of the shaft deflection, the diameter and length of the shaft and the corresponding shaft sleeve length are selected as the control parameters in the optimization model. Moreover, several experiments are conducted by using the L18 (mixed orthogonal array) design method. Considering that the local mechanical characteristics such as sleeve strain are difficult to monitor via experimental method, an FEM simulation model is established to give the sleeve slippage displacement. Different levels of control parameters are introduced into the mechanical model and FEM simulation according to Taguchi method. The results from signal-to-noise (S/N) and ANOVA analysis (analysis of variance) reveal that shaft diameter is the most significant factor determining sleeve slippage in high-voltage circuit breaker operation mechanism, and that a larger diameter of shaft, a shorter shaft length and a longer sleeve length can reduce the sleeve slippage effectively. Meanwhile, the theoretical model is verified and enhanced by the FEM model.
{"title":"Optimization of Shaft Sleeve Slippage in High-Voltage Circuit BreakerOperation Mechanism","authors":"Zhao Wenqiang, Zhang Haibo, Wu Shijing, Meng Fangang","doi":"10.2174/1874155X01509011081","DOIUrl":"https://doi.org/10.2174/1874155X01509011081","url":null,"abstract":"High-voltage circuit breakers are mechanical switching devices which connect and break current circuits (operating currents and fault currents) and carry the nominal current in closed position. As a result of multi-running, the shaft sleeve in operation mechanism could slip and even strip from the shaft at the hinge joint, which decreases the system reliability. In this work, investigations on the cause of sleeve slippage are proceeded, and the dimension parameters of shafting components where sleeve slippage occurs are optimized by incorporating a quasi-static mechanical model with Taguchi method. By developing and analyzing the mechanical model for the shaft sleeve slippage, it indicates that the sleeve slippage displacement has a same variation tendency with the shaft deflection. Theoretical equations are derived by using force analysis and superposition method to descript the analytic function of the shaft deflection. As the variables within the analytic function of the shaft deflection, the diameter and length of the shaft and the corresponding shaft sleeve length are selected as the control parameters in the optimization model. Moreover, several experiments are conducted by using the L18 (mixed orthogonal array) design method. Considering that the local mechanical characteristics such as sleeve strain are difficult to monitor via experimental method, an FEM simulation model is established to give the sleeve slippage displacement. Different levels of control parameters are introduced into the mechanical model and FEM simulation according to Taguchi method. The results from signal-to-noise (S/N) and ANOVA analysis (analysis of variance) reveal that shaft diameter is the most significant factor determining sleeve slippage in high-voltage circuit breaker operation mechanism, and that a larger diameter of shaft, a shorter shaft length and a longer sleeve length can reduce the sleeve slippage effectively. Meanwhile, the theoretical model is verified and enhanced by the FEM model.","PeriodicalId":267392,"journal":{"name":"The Open Mechanical Engineering Journal","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125438711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-11-02DOI: 10.2174/1874155X01509011076
L. Gong, Z. Li, Zhen Zhang
Metal magnetic memory (MMM) signals can reflect stress concentration and cracks on the surface of ferromagnetic components, but the traditional criteria used to distinguish the locations of these stress concentrations and cracks are not sufficiently accurate. In this study, 22 indices were extracted from the original MMM signals, and the diagnosis results of 4 kernel functions of support vector machine (SVM) were compared. Of these 4, the radial basis function (RBF) kernel performed the best in the simulations, with a diagnostic accuracy of 94.03%. Using the principles of adaptive genetic algorithms (AGA), a combined AGA-SVM diagnosis model was created, resulting in an improvement in accuracy to 95.52%, using the same training and test sets as those used in the simulation of SVM with an RBF kernel. The results show that AGA-SVM can accurately distinguish stress concentrations and cracks from normal points, enabling them to be located more accurately.
{"title":"Diagnosis Model of Pipeline Cracks According to Metal Magnetic MemorySignals Based on Adaptive Genetic Algorithm and Support VectorMachine","authors":"L. Gong, Z. Li, Zhen Zhang","doi":"10.2174/1874155X01509011076","DOIUrl":"https://doi.org/10.2174/1874155X01509011076","url":null,"abstract":"Metal magnetic memory (MMM) signals can reflect stress concentration and cracks on the surface of ferromagnetic components, but the traditional criteria used to distinguish the locations of these stress concentrations and cracks are not sufficiently accurate. In this study, 22 indices were extracted from the original MMM signals, and the diagnosis results of 4 kernel functions of support vector machine (SVM) were compared. Of these 4, the radial basis function (RBF) kernel performed the best in the simulations, with a diagnostic accuracy of 94.03%. Using the principles of adaptive genetic algorithms (AGA), a combined AGA-SVM diagnosis model was created, resulting in an improvement in accuracy to 95.52%, using the same training and test sets as those used in the simulation of SVM with an RBF kernel. The results show that AGA-SVM can accurately distinguish stress concentrations and cracks from normal points, enabling them to be located more accurately.","PeriodicalId":267392,"journal":{"name":"The Open Mechanical Engineering Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130079585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-28DOI: 10.2174/1874155X01509011067
Chen Li, Hu Niansu
With the increase of thermal power capacity, ultra supercritical units have become the mainstream of power industry. At the same time, with the improvement of the steam parameters and the shafting lengthened, making the steam flow excited vibration frequently happen in the ultra supercritical units, which will seriously affect the reliability of the unit. This paper takes steam flow excited vibration of a 1000MW turbine as an example, according to the experimental and theoretical causes of steam flow excited vibration, so as to solve the steam flow excited vibration by the proposed treatment plan, the reliability of operation and the units with high load capacity can also be greatly improved.
{"title":"Fault Diagnosis for Steam-flow Exciting Vibration of Ultra Supercritical1000 MW Steam Turbine","authors":"Chen Li, Hu Niansu","doi":"10.2174/1874155X01509011067","DOIUrl":"https://doi.org/10.2174/1874155X01509011067","url":null,"abstract":"With the increase of thermal power capacity, ultra supercritical units have become the mainstream of power industry. At the same time, with the improvement of the steam parameters and the shafting lengthened, making the steam flow excited vibration frequently happen in the ultra supercritical units, which will seriously affect the reliability of the unit. This paper takes steam flow excited vibration of a 1000MW turbine as an example, according to the experimental and theoretical causes of steam flow excited vibration, so as to solve the steam flow excited vibration by the proposed treatment plan, the reliability of operation and the units with high load capacity can also be greatly improved.","PeriodicalId":267392,"journal":{"name":"The Open Mechanical Engineering Journal","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129750720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-28DOI: 10.2174/1874155X01509011062
Liu Shenyang, G. Qi, Li Zhen, Li Si, Li Zhiwei
Material cost prediction should be based on the scientific mathematical models, to reduce the influence of subjective factors on the quota and other indicators of decomposition. This paper analyzes the particle swarm optimization (PSO) algorithm to optimize the parameters of support vector machine and establishes the prediction model of material cost after preprocessing the actual data and using the support vector regression (SVR) machine to perform data mining. In the forecasting process, the total cost of material is predicted, the predicted results are fitted with the actual value, and the relative errors are tested. The result shows that the forecasting effect is satisfied. The prediction of material cost is utilizing special methods to estimate and predict the level of material cost on the basis of historical data and relevant information. Its characteristic is predicting the future on the basis of history, and predicting unknown level on the basis of known information. The renewal of statistical data and a series of characteristics of material production of enterprises determine that the sequence formed by material cost data generally has the nonstationarity, nonlinearity and the point of abrupt change. The support vector machine can realize the minimization of structural risk, and the error rate on test data (namely generalization error rate) of learning machines takes the sum of training error rate and a dependent item as boundary. The support vector machine doesn't utilize the internal problems of the field, which can provide the good generalization performance in problems of pattern classification, and that is the specific characteristic of support vector machine. This paper conducts the data mining to establish the prediction model of material cost according to the learning method of support vector regression machine. This method is a type of relational schema between the spatial pattern of learning input and function mapping of learning output, and researchers generally call the function set in this type of mapping relation as learning machine. It starts from the research on observation data (namely sample). Researchers obtains some rules that can't be obtained from principle in the current situations, and meanwhile utilizes these rules to analyze the data obtained. Thus they can reach the value prediction and conduct the decision making and value estimation. 2. PSO-SVR MODELING ALGORITHM
{"title":"Research on the Prediction Model of Material Cost Based on Data Mining","authors":"Liu Shenyang, G. Qi, Li Zhen, Li Si, Li Zhiwei","doi":"10.2174/1874155X01509011062","DOIUrl":"https://doi.org/10.2174/1874155X01509011062","url":null,"abstract":"Material cost prediction should be based on the scientific mathematical models, to reduce the influence of subjective factors on the quota and other indicators of decomposition. This paper analyzes the particle swarm optimization (PSO) algorithm to optimize the parameters of support vector machine and establishes the prediction model of material cost after preprocessing the actual data and using the support vector regression (SVR) machine to perform data mining. In the forecasting process, the total cost of material is predicted, the predicted results are fitted with the actual value, and the relative errors are tested. The result shows that the forecasting effect is satisfied. The prediction of material cost is utilizing special methods to estimate and predict the level of material cost on the basis of historical data and relevant information. Its characteristic is predicting the future on the basis of history, and predicting unknown level on the basis of known information. The renewal of statistical data and a series of characteristics of material production of enterprises determine that the sequence formed by material cost data generally has the nonstationarity, nonlinearity and the point of abrupt change. The support vector machine can realize the minimization of structural risk, and the error rate on test data (namely generalization error rate) of learning machines takes the sum of training error rate and a dependent item as boundary. The support vector machine doesn't utilize the internal problems of the field, which can provide the good generalization performance in problems of pattern classification, and that is the specific characteristic of support vector machine. This paper conducts the data mining to establish the prediction model of material cost according to the learning method of support vector regression machine. This method is a type of relational schema between the spatial pattern of learning input and function mapping of learning output, and researchers generally call the function set in this type of mapping relation as learning machine. It starts from the research on observation data (namely sample). Researchers obtains some rules that can't be obtained from principle in the current situations, and meanwhile utilizes these rules to analyze the data obtained. Thus they can reach the value prediction and conduct the decision making and value estimation. 2. PSO-SVR MODELING ALGORITHM","PeriodicalId":267392,"journal":{"name":"The Open Mechanical Engineering Journal","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115683486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-22DOI: 10.2174/1874155X01509011057
Zhu Yili, Zhang Yongchun
In an active magnetic bearing (AMB) system, the Auxiliary bearings (ABs) are indispensable to protect the rotor and stator in case of AMB failure. Most of the former researches try to modify relevant design parameters of ABs to buffer the following impacts and heating after rotor drop. Based on the analysis of the disadvantages of traditional ABs, a new type of AB with the support of metal rubber ring is proposed to enhance the AB work performance in AMB system. Detailed simulation models containing rigid rotor model, contact model between rotor and inner race as well as AB system model after rotor drop are established. Then, using those established models the dynamic responses are simulated to obtain proper metal rubber ring support characteristics. Finally, relevant rotor drop experiments are carried out on the established AMB test bench. The experiment results verify the advantages of the new type ABs and the correctness of simulation analysis.
{"title":"Dynamic Responses of Rotor Drops onto Auxiliary Bearing with the Support of Metal Rubber Ring","authors":"Zhu Yili, Zhang Yongchun","doi":"10.2174/1874155X01509011057","DOIUrl":"https://doi.org/10.2174/1874155X01509011057","url":null,"abstract":"In an active magnetic bearing (AMB) system, the Auxiliary bearings (ABs) are indispensable to protect the rotor and stator in case of AMB failure. Most of the former researches try to modify relevant design parameters of ABs to buffer the following impacts and heating after rotor drop. Based on the analysis of the disadvantages of traditional ABs, a new type of AB with the support of metal rubber ring is proposed to enhance the AB work performance in AMB system. Detailed simulation models containing rigid rotor model, contact model between rotor and inner race as well as AB system model after rotor drop are established. Then, using those established models the dynamic responses are simulated to obtain proper metal rubber ring support characteristics. Finally, relevant rotor drop experiments are carried out on the established AMB test bench. The experiment results verify the advantages of the new type ABs and the correctness of simulation analysis.","PeriodicalId":267392,"journal":{"name":"The Open Mechanical Engineering Journal","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132194791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2015-10-09DOI: 10.2174/1874155X015090101051
Ding Ming
In this paper, the gear material 20CrMnTi was selected as the research object. Friction and wear behavior was performed on the M2000 friction and abrasion tester. The friction and wear mechanisms of 20CrMnTi steel were discussed under coupling of rolling and sliding. The results show that damage of steel-steel couples under coupling of rolling and sliding is caused by the interaction of mechanical fatigue with dynamical phenomena of rolling and sliding friction. Lubrication directly determines the friction and wear behaviors. Under dry friction, the wear mechanisms of 20CrMnTi steel are mainly adhesive wear, abrasive wear, oxidation wear and fatigue pitting under dry friction. Under lubricating conditions, the wear mechanism of 20CrMnTi steel is mainly surface fatigue wear.
{"title":"Friction and Wear Behaviors of Gear Steel under Coupling of Rolling andSliding","authors":"Ding Ming","doi":"10.2174/1874155X015090101051","DOIUrl":"https://doi.org/10.2174/1874155X015090101051","url":null,"abstract":"In this paper, the gear material 20CrMnTi was selected as the research object. Friction and wear behavior was performed on the M2000 friction and abrasion tester. The friction and wear mechanisms of 20CrMnTi steel were discussed under coupling of rolling and sliding. The results show that damage of steel-steel couples under coupling of rolling and sliding is caused by the interaction of mechanical fatigue with dynamical phenomena of rolling and sliding friction. Lubrication directly determines the friction and wear behaviors. Under dry friction, the wear mechanisms of 20CrMnTi steel are mainly adhesive wear, abrasive wear, oxidation wear and fatigue pitting under dry friction. Under lubricating conditions, the wear mechanism of 20CrMnTi steel is mainly surface fatigue wear.","PeriodicalId":267392,"journal":{"name":"The Open Mechanical Engineering Journal","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125715225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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