Prediction system and method for life of machine tool cutter

Abstract

The invention discloses a prediction system and method for the life of a machine tool cutter. The prediction system comprises a built-in machine tool sensor, a data acquisition module, a preprocessingmodule, a feature extraction module, and a prediction module, wherein the built-in machine tool sensor is connected to the data acquisition module, the data acquisition module is connected to the preprocessing module, the preprocessing module is connected to the feature extraction module, and the feature extraction module is connected to the prediction module. The method comprises the following steps: a spindle current signal and cutting condition parameter of a machine tool are measured; the spindle current signal and the cutting condition parameter are acquired; the spindle current signal is preprocessed; a feature value is extracted from the preprocessed spindle current signal; and according to the spindle current signal feature value and the cutting condition parameter, the remainingservice life of a cutter is predicted. The prediction system and method for the life of the machine tool cutter disclosed by the invention can obtain a relatively good prediction result with lower cost under the condition of not affecting production.

Description

technical field [0001] The invention relates to the technical field of machine tool tool life prediction, in particular to a machine tool tool life prediction system and method. Background technique [0002] The tool is one of the most important parts of the machine tool, and its performance directly affects the quality of the machining result. Using tools with excessive wear or exceeding life expectancy will not only increase energy consumption, reduce machining accuracy, but also cause the risk of blade breakage, which will bring production safety problems. Therefore, for mass production, tool life management is indispensable, and it will directly affect production cost control, product quality control and production safety and other issues. [0003] Most of the currently existing tool prediction methods require external sensors, which is not only difficult to operate, but also increases costs, and may even affect the production of the machine tool itself. [0004] There...

AI Technical Summary

Problems solved by technology

Using tools with excessive wear or exceeding life expectancy will not only increase energy consumption, reduce machining accuracy, but also cause the risk of blade breakage, which will bring production safety problems

Therefore, for mass production, tool life management is indispensable, it will directly affect production cost control, product quality control and production safety and other issues

[0003] Most of the currently existing tool prediction methods require external sensors, which is not only difficult to operate, but also increases costs, and may even affect the production of the machine tool itself

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