Milling machine operation monitoring method and system based on artificial intelligence

Abstract

The invention provides a milling machine operation monitoring method based on artificial intelligence. The milling machine operation monitoring method includes the following steps that milling force of a milling machine, the spindle rotating speed of the milling machine, the feeding speed of a feeding shaft of the milling machine, the milling angle and the milling depth are monitored; the wear condition of a milling cutter is monitored; above data are sent to a milling machine monitoring center; the wear condition of the milling cutter is sent to the milling machine monitoring center; historical data of the relevant values exist, and meanwhile historical data of the wear condition of the milling cutter are acquired; based on the historical data of the relevant values and the historical data of the wear condition of the milling cutter, a first incidence relation between the milling force of the milling machine and the wear condition of the milling cutter is produced, and meanwhile a second incidence relation between all parameters and the wear condition of the milling cutter is produced; and based on the first incidence relation and the current milling force of the milling machine,the current wear condition of the milling cutter is judged. According to the milling machine operation monitoring method based on artificial intelligence, the problems that convergence is too slow andcannot be conducted are avoided, and forecasting precision is greatly improved.

Description

technical field [0001] The invention relates to the field of milling processing, in particular to an artificial intelligence-based milling machine operation monitoring method and system. Background technique [0002] As one of the production elements of mechanical processing, cutting tools directly affect the processing quality of workpieces and the stability and reliability of machine tool operation. According to relevant statistics, a reliable tool condition monitoring system can increase the utilization rate of machine tools by 50%. , can reduce up to 70% of downtime, can increase productivity by 15% -60%. The system can not only greatly prolong tool life, but also effectively reduce product scrap due to tool failure. The traditional tool status monitoring is offline, and it is usually necessary to stop the machine regularly to check the status of the tool, which will greatly increase the processing auxiliary time and affect the machining efficiency. And because the ins...

AI Technical Summary

Problems solved by technology

The traditional tool status monitoring is offline, and it is usually necessary to stop the machine regularly to check the tool status, which will greatly increase the processing auxiliary time and affect the machining efficiency

And because the tool inspection is carried out regularly, in order to avoid tool failure between two inspections, the tool life can only be estimated conservatively, which will not guarantee the full utilization of the tool

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