Artificial neural network-based method and device for automatically trimming grinding wheel of grinding machine

Abstract

The invention discloses an artificial neural network-based method and a device for automatically trimming a grinding wheel of a grinding machine. The method comprises the following steps: collecting data, i.e. the voltage U of a grinding wheel motor, the current I of the grinding wheel motor, the revolving speed n of the grinding wheel, the relative translational velocity v of parts to be machined and the grinding wheel, and the passivation coefficient d of the grinding wheel corresponding to each experiment, from the experiment field at each experiment through evenly-grinding experiments; selecting training sample data from the data to train the built artificial neural network prediction model; and sending data in the built artificial neural network prediction model to calculate an output y after collecting the data on site, i.e. the voltage U of the grinding wheel motor, the current I of the grinding wheel motor, the revolving speed n of the grinding wheel and the relative translational velocity v of the parts to be machined and the grinding wheel, and comparing the y with a preset trimming set value so as to control a trimming device to trim the grinding wheel. With the adoption of the method and the device disclosed by the invention, the artificial judging factors in grinding and machining can be reduced, so that the degree of automation of the grinding machine in the processes of grinding and machining is enhanced, and the cost for machining and the rejection rate are reduced.

Description

technical field [0001] The invention relates to the field of mechanical grinding, in particular to an automatic dressing method and a dressing device for a grinding wheel of a grinding machine based on an artificial neural network. Background technique [0002] Grinding machine is the main tool for grinding. Grinding is a processing method that removes material by means of a large number of abrasive cutting edges on the surface of the grinding wheel. As a process for obtaining high-precision, low-roughness processing surfaces and finishing high-hardness surfaces, it is used in the field of mechanical processing technology. occupy a very important position. The blunted grinding wheel will affect the surface quality and geometric accuracy of the workpiece to be ground. Therefore, the blunted grinding wheel must be trimmed and compensated in time to ensure the processing quality of the part. Traditional grinding wheel dressing of grinding machines is mostly through manual ins...

AI Technical Summary

Problems solved by technology

Static detection mainly includes rolling copy method and stylus method. The disadvantage of these methods is offline and subsequent detection, which is not conducive to improving work efficiency and ensuring product qualification rate; dynamic detection mainly includes light section method, light point automatic measurement method, Laser power spectrum method, electron microscope observation method, light reflection method and acoustic emission monitoring method, etc. The disadvantages of these methods are difficult installation and debugging, complex structure, and high quality requirements for personnel, so they have not yet been used in production and processing.

Due to the above reasons, the grinding wheel dressing of grinding machines is basically not automated at present.

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