Cutter life dynamic prediction method

Abstract

The invention relates to the field of cutter service life prediction, and discloses a cutter service life dynamic prediction method, which comprises the following steps: S1, determining features influencing the cutter service life, collecting related information data, obtaining historical data, and carrying out standardization processing on the historical data; s2, performing correlation analysis on the historical data, and deleting features of correlation within a critical range; s3, performing principal component analysis on the features, and performing dimension reduction and simplification on historical data to obtain modeling data; s4, using a gradient lifting regression tree to train modeling data, and establishing a cutter life prediction model; s5, collecting real-time data according to the characteristics of the modeling data, carrying out standardization processing on the real-time data, inputting the data into the tool life prediction model, and outputting to obtain the tool life; according to the dynamic prediction method for the service life of the cutter, the information data influencing the service life of the cutter is optimized, a perfect cutter service life prediction model is established, and the accuracy of cutter service life prediction is further improved.

Description

technical field [0001] The invention relates to the field of tool life prediction, in particular to a tool life dynamic prediction method. Background technique [0002] Tool is an important part of machine tool processing. During the processing, the tool is constantly worn out. When the wear reaches a certain level, continued processing will lead to quality problems of the parts. The prediction of tool life is a problem that has plagued machine tool processing for a long time. The actual use of tools When the time is less than the tool life, it will cause tool waste and increase the cost of parts manufacturing. When the actual processing time of the tool is greater than the tool life, it will bring huge quality risks, and there may be problems such as milling damage and out-of-tolerance of parts, resulting in scrap parts. [0003] At this stage, the tool life in the industry is mainly determined by experience. When the same tool is used for rough machining, the speed is fast...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the invention is: for the prediction method of tool life in the prior art, the tool life prediction model established is not perfect enough, and there is a problem that it is difficult to accurately predict the tool life , to provide a dynamic prediction method for tool life, by optimizing the information data affecting tool life, a more complete tool life prediction model is established, which can effectively and dynamically predict tool life according to the collected real-time data, and further improve tool life. The accuracy of life prediction

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