Study on On-Line Monitoring Method for Grinding Quality

Abstract

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...

Research on Web Based Remote Monitor and Control System for Grinding Process

“…Here, Acoustic emission sensor is used to monitor grinding process. The research results in literature [2] indicate that the peak value of RMS and FFT of high frequency AE signals produced by grinding operation has good corresponding relation with grinding burn and chatter, and the accumulated amplitude increment of AE signals has good corresponding relation with the dull degree of grinding wheel, so AE signals is adopted to monitor grinding process.…”

“…Grinding processing is often the last machining procedure of the important work-piece, and affects the working capability of equipments directly, so real-time monitor and control for grinding process and adjusting the grinding parameters on-line is important for steady processing quality. In the past, grinding parameters are adjusted only by skill workers depend on their experience, the problem of the shortage of skill workers arises along with the rapid development of industry, and pollution in grinding field is also a threat to the health of workers [1,2]. So, the remote monitor & control system on grinding process is becoming more and more important.…”

An adaptive parameters adjustment and planning method for robotic belt grinding using modified quality model

“…14,15 With the rapid development of artificial intelligence technology, artificial neural networks (ANN), genetic algorithms (GA) and fuzzy expert systems et al have been widely adopted in parameter adjustment due to their unique advantages. The nonlinear modeling theory and self-learning capability of neural network are proposed to setup mapping relations between the grinding conditions and their affecting factors by Liu et al 16 On-line intelligent adjustment of grinding conditions can be realized based on dull degree of grinding wheel. A fuzzy adaptive control strategy based on dynamic threshold is applied in the high hardness sphere grinding process by Li et al 17 The depth of cut and the swing angular speed which affect the grinding force can also be adjusted on-line by fuzzy strategy to maintain the grinding process stable.…”

A survey on insulator inspection robots for power transmission lines

“…At present, the inspection methods for insulators can be divided into two categories, namely, methods based on nonelectric quantities and methods based on electric quantities [1]. Methods based on non-electric quantities mainly include direct observing method, ultraviolet radiation imaging method, infrared imaging method and ultrasonic detecting method.…”

“…Methods based on non-electric quantities mainly include direct observing method, ultraviolet radiation imaging method, infrared imaging method and ultrasonic detecting method. Methods based on electric quantities include potential measurement, insulation resistance measurement, electric field measurement, leakage current measurement and impulse current measurement [1]. In practice, the most commonly used methods are insulation resistance measurement and electric field measurement.…”