By using fracture mechanics theory of rock, the rock fragmentation mechanism of tunnel boring machine (TBM) cutters is analyzed and the analysis of forces of cutter is carried out. A new method to predict disc cutter specific energy for TBM is developed in this study. By using the dynamic models of TBM cutters interaction with the rock, specific energy prediction model for TBM cutter head is developed. The data from the actual tunnel construction is analyzed by using an example of Qinling tunnel and the comparison is made with the field data. The results indicate that the model developed in this study could not only replace the experiment to disc cutter specific energy for TBM, but also provide a theoretical basis for the performance prediction and optimal design of cutter head for TBM.
A on-line monitoring new method for grinding quality is presented based on theory analysis and test study, which collects the information of grinding quality from the AE signals produced by friction and grinding process to realize the on-line intelligent detection and prediction of grinding quality by A wavelet neural network & Fuzzy BP algorithm based network. The reliability and feasibility of the method is proved by actual test. The test results indicate that this method can be used to monitor grinding quality on line. PrefaceThe process of intelligence and automation is a general tendency in manufacturing [1]. Grinding, as an important finishing process, is preventing this trend now, because there is not suitable on-line monitor method for grinding quality [2]. Therefore, the development of the intelligent monitoring techniques for permanent on-line system identification is a very important task in order to enable detection of incipient deterioration and remedial action to be taken before the fatal breakdown for grinding process. The majority of standard methods for the grinding surface roughness estimation are not suitable for in process measurements and on-line monitoring. This paper describes an attempt to obtain on-line information about the grinding quality in grinding process. For this purpose, a wavelet neural network & Fuzzy BP algorithm based intelligent method for on-line monitoring grinding surface roughness, grinding burning and grinding chatter is presented based on theory analysis and practical test in this paper. Feasibility Analysis of On-line Detecting Grinding Quality by Acoustic EmissionFriction is a complex phenomenon influenced by the combined effects of adhesion, asperity deformation, plowing by hard asperities, and wear of entrapped particles. The changes in the stress distribution around asperity contact at surface traction generate transient stress waves called acoustic emission (AE), which can be detected by suitable AE sensor [3][4]. It is well known that elastic distortion and the plastic deformation become large under same pressure as surface roughness worsening in friction process, grinding burning and grinding chatter states. This will further result in more strength elastic stress wave. Therefore, if the mapping relationship between the some feature value of AE signals and grinding surface roughness, grinding burning and grinding chatter, then AE signals can be used to on-line detect grinding surface roughness, grinding burning and grinding chatter continually. J.S. Kwak and J.B. Song [5] have developed a diagnosis system for grinding process by using acoustic emission signals, and verified this system has high trouble recognition rates by test. E. Susic and I. Grabec presents a newly developed for on-line estimation of the roughness and hardness parameters of surfaces involved in sliding friction process, in which the information about the process is extracted from the acoustic emission signal generated by friction. The system performance was tested in the on-line pin-in-disk...
According to problems of flatness error measurement of revolving body end-face, sampling method and evaluation method are studied in this paper. First, spiral sampling method is presented, in order to meet the needs of flatness error measurement sampling of revolving body end-face. Second, combined with least squares method, mathematical model applied to flatness error measurement evaluation of revolving body end-face is proposed. Finally, through the simulation, the mathematical model is proved to be correct.
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