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...
To identify the fatigue crack propagation of tubular joints in the offshore platform, we applied the wavelet coefficient energy theory to monitor and analyze the acoustic emission(AE) signal of marine structures with typical fatigue crack propagation. Based on typical tubular joints model fracture experiments of the offshore platforms, we successfully extracted the characteristics of AE signals during crack propagation. Further analysis results demonstrated that the elastic wave generated by the crack extension was superimposed by a variety of high frequency signals, and it changed with the evaluation of crack. The variation of wavelet coefficient energy and the change of crack propagation state showed a strong correlation. Thus the characteristics of AE signals can be used to identify different stages of crack propagation accurately, intuitively, and clearly, so as to label the development process of crack.
The grinding quality assessing is a complex decision-making process, which must optimize and balance multi-influence factors. A grinding quality assessing method based on fuzzy synthetic evaluation theory combined with analytic hierarchy process (AHP) is presented in this paper, and used to quality assessing for grinding process. The result of analyzing example indicates that this method can be used to estimate grinding quality based on the monitoring result of grinding process and grinding condition, and to assist grinding worker to select optimum grinding parameters for the steady grinding quality.
Engineering ceramics are hard and brittle materials, that is very easy to crack when grinding, and processing mechanism is rather complex than that of metallic materials. One kind of AE signal based grinding contact detection and feeding control method for ceramic material processing is presented in this paper based on ceramic grinding mechanism study. Through a large number of experimental data analyzing, the relationship between grinding touch and the feature of AE signals is obtained, and the automatic and intelligent grinding process can be realized by grinding contact signal driving grinding numeral program. Application results indicate this method can prove grinding efficiency and process quality.
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