An investigation of the grinding wheel wear with the use of root-mean-square value of acoustic emission
This paper presents the results of an investigation of the grinding wheel wear in a peripheral surface grinding process. During this investigation varying geometrical parameters of an active grinding wheel surface have been measured and the components of the grinding force and acoustic emission signal have been recorded. On the basis of these output quantities an analysis has been conducted of an acoustic emission descriptor's practicability, i.e. a root-mean-square (RMS) value, to characterize the cutting abilities of a grinding wheel in the time progress of its work. This article also presents a new energy coefficient, which determines the rate of grinding wheel wear in the form of a RMS value of acoustic emission signal falling on a contact surface of a grinding wheel. Moreover, the article presents some examples of a correlation between the root-mean-square value of acoustic emission signal and the surface roughness of a workpiece, which shows that the parameters of acoustic emission signal can be a useful tool to monitor the surface roughness during the grinding process.
Evaluation of neural models applied to the estimation of tool wear in the grinding of advanced ceramics
Tool condition monitoring in grinding of advanced ceramics using neural networks.Acoustic emission and power signals were used in several statistical parameters.Results showed that the ANN were highly successful in estimating tool wear.Errors was less than 4%.The models will help to improve product quality and increase productivity. Grinding wheel wear, which is a very complex phenomenon, causes changes in most of the shapes and properties of the tool during machining, reducing the efficiency of the grinding operation and impairing workpiece quality. Therefore, monitoring the condition of the tool during the grinding process plays a key role in the quality of workpieces being manufactured. In this study, diamond tool wear was estimated during the grinding of advanced ceramics using intelligent systems composed of four types of neural networks. Experimental tests were performed on a surface grinding machine and tool wear was measured by the imprint method throughout the tests. Acoustic emission and cutting power signals were acquired during the tests and statistics were obtained from these signals. Training and validating algorithms were developed for the intelligent systems in order to automatically obtain the best estimation models. The combination of signals and statistics along with the intelligent systems brings an innovative aspect to the grinding process. The results indicate that the models are highly successful in estimating tool wear.
Magnetic properties of Lu2Fe17 crystals
Abstract We present the results of crystal structure and magnetization studies of three single crystals grown from melts of different nominal compositions in the vicinity of the Lu2Fe17 stoichiometry. The ferromagnetic state of the crystals is strongly dependent on the real sample composition and the local atomic ordering. A drastic difference is observed in the behaviour of the ferro–antiferromagnetic transition, even in crystals for which almost identical composition and crystal parameters were determined by microprobe and X-ray analysis.