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Xianghua Liu - One of the best experts on this subject based on the ideXlab platform.

  • theoretical analysis of minimum metal foil thickness achievable by asymmetric rolling with fixed identical roll diameters
    Transactions of Nonferrous Metals Society of China, 2016
    Co-Authors: Xin Liu, Xianghua Liu, Meng Song, Xiangkun Sun, Lizhong Liu
    Abstract:

    Abstract A novel approach is proposed for computing the minimum thickness of a metal foil that can be achieved by asymmetric rolling using rolls with identical diameter. This approach is based on simultaneously solving Tselikov equation for the rolling pressure and the modified Hitchcock equation for the roller flattening. To minimize the effect of the elastic deformation on the equal flow per second during the ultrathin foil rolling process, the law of conservation of mass was employed to compute the proportions of the forward slip, backward slip, and the cross shear zones in the Contact Arc, and then a formula was derived for computing the minimum thickness for asymmetric rolling. Experiment was conducted to find the foil minimum thickness for 304 steel by asymmetric rolling under the asymmetry ratios of 1.05, 1.15 and 1.30. The experimental results are in good agreement with the calculated ones. It was validated that the proposed formula can be used to calculate the foil minimum thickness under the asymmetric rolling condition.

  • calculation of rolling pressure distribution and force based on improved karman equation for hot strip mill
    International Journal of Mechanical Sciences, 2014
    Co-Authors: Shuixuan Chen, Xianghua Liu
    Abstract:

    An improved Karman equation for hot-rolled strip was deduced to generate a new rolling pressure formula, based on comprehensive consideration of the slipping and sticking friction on the Contact Arc between hot-rolled strip and work rolls. The Runge–Kutta method was applied to solve the improved differential equation, and then the distribution of rolling pressure on the Contact Arc was obtained. The roll force in the roll-bite can be calculated by integrating the vertical component of positive pressure and friction shear stress of every slice. This paper also analyzed the influence of friction condition, flow stress, roller distortion and other factors on the results of rolling pressure and force per unit width. Using 7 stands of hot strip mills as an example, this paper conducted actual industrial application verification. The computational results demonstrate that the proposed new model improves the setting precision of roll force and can be applied to online control of hot rolled strip.

Hideo Ohshita - One of the best experts on this subject based on the ideXlab platform.

  • grinding temperature within Contact Arc between wheel and workpiece in high efficiency grinding of ultrahard cutting tool materials
    Journal of Materials Processing Technology, 2003
    Co-Authors: Tsunemoto Kuriyagawa, Katsuo Syoji, Hideo Ohshita
    Abstract:

    Abstract This paper describes the grinding temperature characteristics during creep-feed grinding of cermet (Ti(C,N) alloy), which shows extreme difficulty in grinding. Creep-feed grinding is one of the high-efficiency grinding methods, and has inherent thermal problems because there is a long Contact Arc between wheel and workpiece. Furthermore, as cermet has low thermal conductivity, the grinding temperature tends to rise dramatically. Therefore, measurements of the grinding temperature distribution within the Contact Arc were made using a thermocouple method. Changes in the temperature distribution during grinding were also examined. It was found that when the grinding temperature within the Contact Arc reaches 150 °C or more, burnout occurs and the temperature suddenly increases to over 400 °C. The burnout starts from the rear of the Contact Arc and extends almost throughout the Contact Arc as grinding progresses. In other words, even if grinding fluid is being supplied, a phenomenon similar to dry grinding occurs in the Contact Arc in such circumstances. The optimum conditions of wheel and worktable speed were found in order to improve the cooling effects.

Ichiro Takasu - One of the best experts on this subject based on the ideXlab platform.

  • elastic plastic finite element analysis of cold ring rolling process
    Journal of Materials Processing Technology, 2002
    Co-Authors: Hiroshi Utsunomiya, Yoshihiro Saito, Tomoaki Shinoda, Ichiro Takasu
    Abstract:

    Abstract Although cold ring rolling is used in the manufacturing of bearing races, reseArch on this process has been limited. In this study, the fundamental characteristics of cold ring rolling are made clear by the finite element method. Steel rings with rectangular cross-sections are rolled on a mill with a driven mandrel and a work roll. The entire ring is analysed under the plane-strain condition. An elastic–plastic constitutive equation is used on non-steady-state scheme. It is found that at the beginning of the rolling, the ring vibrates irregularly. The reduction in thickness increases linearly during the first revolution of the ring, then increases slightly from the second revolution. At the early stage of rolling, the equivalent strain is higher at both surfaces and distributes inhomogeneously along the circumference. In the roll gap, the radial stress of surface elements shows a peak at the middle of the Contact Arc. The spatial distribution of stress components approaches the steady-state after a few revolutions. Outside the roll gap, generally compressive circumferential (hoop) stress acts on the outer surface, while tensile stress acts on the inner surface. The stress components change periodically with the revolution. The fundamental characteristics of the cold ring rolling process are discussed.

Shuixuan Chen - One of the best experts on this subject based on the ideXlab platform.

  • calculation of rolling pressure distribution and force based on improved karman equation for hot strip mill
    International Journal of Mechanical Sciences, 2014
    Co-Authors: Shuixuan Chen, Xianghua Liu
    Abstract:

    An improved Karman equation for hot-rolled strip was deduced to generate a new rolling pressure formula, based on comprehensive consideration of the slipping and sticking friction on the Contact Arc between hot-rolled strip and work rolls. The Runge–Kutta method was applied to solve the improved differential equation, and then the distribution of rolling pressure on the Contact Arc was obtained. The roll force in the roll-bite can be calculated by integrating the vertical component of positive pressure and friction shear stress of every slice. This paper also analyzed the influence of friction condition, flow stress, roller distortion and other factors on the results of rolling pressure and force per unit width. Using 7 stands of hot strip mills as an example, this paper conducted actual industrial application verification. The computational results demonstrate that the proposed new model improves the setting precision of roll force and can be applied to online control of hot rolled strip.

Tong Ziyuan - One of the best experts on this subject based on the ideXlab platform.

  • Arc Source Recognition and Locating Based on Electromagnetic field analysis
    School of Electrical and Information Engineering, 2017
    Co-Authors: Tong Ziyuan
    Abstract:

    An electric Arc is the breakdown of gas discharge between electrodes. In daily life and industrial production, electric Arcs can be divided into two categories: normal Arcs and Arc faults. An Arc refers to the spark discharge during the normal operation of electrical equipment, such as a Contact Arc generated by a switching-in in power supply, a spark used in electric welding, or an Arc produced in the electric insertion process. Arc faults are the discharging caused by equipment failure or working under abnormal conditions, such as circuit failures including aging, overload, short circuit, current instability, fusing, loosening of a conductor, or ground fault. It is a dangerous sign in electric system operation when an unusual Arc occurs, since an Arc fault can not only affect the equipment’s normal operation, but it can also cause an electric breakdown that triggers a power outage. In certain working environments, Arc faults can even cause a fire hazard or explosion. According to fire statistics, about 30% of industrial accidents are fire disasters, and most fires are caused by Arc faults. Therefore, Arc fault monitoring is necessary to guarantee the safety and security of power system operations. If Arcs can be found in advance, accidents can be effectively avoided, and electric faults can be discovered accordingly to prevent systems from being affected. In order to effectively protect a system, an analysis and recognition of the Arc signal is important. This reseArch not only includes type identification of electric Arcs, but also identifies the locations of Arc sources for timely detection of electrical faults and the prevention of potential accidents. In the process of Arc discharging, there are a series of physical phenomena such as optical radiation, light, high temperature, and detonation. An initial current pulse with a fast rise time and short duration is generated in the Arc production process, along with an electromagnetic field and wave generated by strong electromagnetic radiation. This provides a new way for analysing an Arc signal. The main objective of this thesis is to recognise, classify, and locate Arc faults by analysing electromagnetic signals and provide a basis for fault diagnosis and monitoring. The recognition and classification involved in the thesis includes clustering AC and DC Arc discharge, Arc signal recognition from an electromagnetic environment with strong noise, recognition of quantified features extracted from Arc signals, and location recognition of an Arc source. The key procedures of the framework include five steps, A) Arc-generation mechanism analysis and electromagnetic field (EMF) model establishment: The numerical analysis of the electromagnetic signal of an Arc source explores electromagnetic distribution in order to provide a basis for the location selection of the positioning of sensors and Arc source locating; B) Electromagnetic signal identification from electromagnetic noise environment: specifically, reseArch on noise suppression and signal separation, laying the foundation for the analysis of electrical Arcs and breakdowns; C) Rough clustering of electrical Arcs: analysis of Arc signals in a time-frequency domain, which has important significance for the practical diagnosis of an Arc fault; D) Extraction features of an Arc signal: Takes the brush work state of a DC motor as an experiment target, and extracts and quantifies the features from the signal of the Arc generated in abnormal brush conditions, based on the different working conditions identified; E) Identification of Arc source location: includes establish an EMF map for visual observation of Arc source in a small range of two-dimensional space and calculation of Arc coordinates in a wide range of three-dimensional space. The method used for electromagnetic signals generated by electric Arcs includes finite difference time domain (FDTD), the wavelet transform algorithm, the blind separation algorithm, the local mean decomposition (LMD) algorithm, particle swarm optimisation algorithm (PSO) combining extreme learning machine (ELM), or PSO-ELM, the band entropy algorithm, a bi-spectrum analysis, a trend surface polynomial model, linear interpolation, ternary symmetric matrix method, and the weighted centroid algorithm. The FDTD method is applied to the analysis of the electromagnetic model of the electric Arc. The algorithm of wavelet transform and blind source separation algorithm are for Arc signal separation and identification from noise. The LMD algorithm and PSO-ELM are for AC and DC Arc distinguishing; the band entropy and bi-cepstrum analysis are for quantification and extraction of Arc features to achieve Arc fault recognition; and the trend surface polynomial model and linear interpolation are for two-dimensional spatial electrical Arc source location identification. The ternary symmetric matrix method and weighted centroid algorithm are combined for three-dimensional space electric Arc source location identification.Access is restricted to staff and students of the University of Sydney . UniKey credentials are required. Non university access may be obtained by visiting the University of Sydney Library

  • Arc Source Recognition and Locating Based on Electromagnetic field analysis
    School of Electrical and Information Engineering, 2017
    Co-Authors: Tong Ziyuan
    Abstract:

    An electric Arc is the breakdown of gas discharge between electrodes. In daily life and industrial production, electric Arcs can be divided into two categories: normal Arcs and Arc faults. An Arc refers to the spark discharge during the normal operation of electrical equipment, such as a Contact Arc generated by a switching-in in power supply, a spark used in electric welding, or an Arc produced in the electric insertion process. Arc faults are the discharging caused by equipment failure or working under abnormal conditions, such as circuit failures including aging, overload, short circuit, current instability, fusing, loosening of a conductor, or ground fault. It is a dangerous sign in electric system operation when an unusual Arc occurs, since an Arc fault can not only affect the equipment’s normal operation, but it can also cause an electric breakdown that triggers a power outage. In certain working environments, Arc faults can even cause a fire hazard or explosion. According to fire statistics, about 30% of industrial accidents are fire disasters, and most fires are caused by Arc faults. Therefore, Arc fault monitoring is necessary to guarantee the safety and security of power system operations. If Arcs can be found in advance, accidents can be effectively avoided, and electric faults can be discovered accordingly to prevent systems from being affected. In order to effectively protect a system, an analysis and recognition of the Arc signal is important. This reseArch not only includes type identification of electric Arcs, but also identifies the locations of Arc sources for timely detection of electrical faults and the prevention of potential accidents. In the process of Arc discharging, there are a series of physical phenomena such as optical radiation, light, high temperature, and detonation. An initial current pulse with a fast rise time and short duration is generated in the Arc production process, along with an electromagnetic field and wave generated by strong electromagnetic radiation. This provides a new way for analysing an Arc signal. The main objective of this thesis is to recognise, classify, and locate Arc faults by analysing electromagnetic signals and provide a basis for fault diagnosis and monitoring. The recognition and classification involved in the thesis includes clustering AC and DC Arc discharge, Arc signal recognition from an electromagnetic environment with strong noise, recognition of quantified features extracted from Arc signals, and location recognition of an Arc source. The key procedures of the framework include five steps, A) Arc-generation mechanism analysis and electromagnetic field (EMF) model establishment: The numerical analysis of the electromagnetic signal of an Arc source explores electromagnetic distribution in order to provide a basis for the location selection of the positioning of sensors and Arc source locating; B) Electromagnetic signal identification from electromagnetic noise environment: specifically, reseArch on noise suppression and signal separation, laying the foundation for the analysis of electrical Arcs and breakdowns; C) Rough clustering of electrical Arcs: analysis of Arc signals in a time-frequency domain, which has important significance for the practical diagnosis of an Arc fault; D) Extraction features of an Arc signal: Takes the brush work state of a DC motor as an experiment target, and extracts and quantifies the features from the signal of the Arc generated in abnormal brush conditions, based on the different working conditions identified; E) Identification of Arc source location: includes establish an EMF map for visual observation of Arc source in a small range of two-dimensional space and calculation of Arc coordinates in a wide range of three-dimensional space. The method used for electromagnetic signals generated by electric Arcs includes finite difference time domain (FDTD), the wavelet transform algorithm, the blind separation algorithm, the local mean decomposition (LMD) algorithm, particle swarm optimisation algorithm (PSO) combining extreme learning machine (ELM), or PSO-ELM, the band entropy algorithm, a bi-spectrum analysis, a trend surface polynomial model, linear interpolation, ternary symmetric matrix method, and the weighted centroid algorithm. The FDTD method is applied to the analysis of the electromagnetic model of the electric Arc. The algorithm of wavelet transform and blind source separation algorithm are for Arc signal separation and identification from noise. The LMD algorithm and PSO-ELM are for AC and DC Arc distinguishing; the band entropy and bi-cepstrum analysis are for quantification and extraction of Arc features to achieve Arc fault recognition; and the trend surface polynomial model and linear interpolation are for two-dimensional spatial electrical Arc source location identification. The ternary symmetric matrix method and weighted centroid algorithm are combined for three-dimensional space electric Arc source location identification