A Tool Life Prediction Method

Abstract

The invention discloses a cutter service life predicting method. A spindle current signal of a machining machine table is collected through a current sensor, and the real-time current signal and the historical current signal are obtained; the feature value of the current signal is extracted through feature learning; current signal data cleaning is conducted according to the feature value of the current signal, and real-time input data and historical input data re obtained separately; the relation between the current signal and the service life of a cutter is analyzed through a machine learningand deep learning method, and after training is conducted with the historical input data serve as a training sample, a cutter service life prediction model is built; and the real-time input data serve as a test sample to be input into the cutter service life prediction model for testing, and a cutter service life prediction assessment result is obtained. By collecting the spindle current data ofa machine tool and the operation and historical data of machine tools of the same types, the cutter state is monitored in real time and the residual life of the cutter is predicted through a big dataanalysis platform, potential safety hazards in the production process are greatly reduced, and the rate of finished produced products is improved.

Description

technical field [0001] The invention belongs to the technical field of processing tool detection, in particular to a tool life prediction method. Background technique [0002] Due to the large loss of tools used in the processing process, the processing cost is relatively high. Therefore, maximizing the tool life will greatly reduce the production cost of the enterprise. However, at present, most tool suppliers usually estimate the life of the tool (according to the processing time or the number of times) as follows: carry out a pressure test on the tool, and measure the average ultimate life of the tool. On this basis, it is generally converted into Tool life. If the tool use unit manages the tool according to the tool life provided by the tool supplier, then each knife loses an average of 20% of its life. Coupled with the different test environments, the tool life provided by the tool supplier will be further affected by the actual working conditions, and its reference ...

AI Technical Summary

Problems solved by technology

If the tool use unit manages the tools according to the tool life provided by the tool supplier, then each knife loses an average of 20% of its life

Coupled with the difference in the test environment, the tool life provided by the tool supplier will be further affected by the actual working conditions, and its reference will be further reduced

The uncontrollability of tool life greatly increases the safety hazards in the production process and reduces the yield of products produced

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