Cutter residual life prediction method based on machine learning regression algorithm

Abstract

The invention relates to the field of machine tool cutter remaining life prediction, and discloses a cutter residual life prediction method based on a machine learning regression algorithm. The cutterresidual life prediction method comprises two parts including model training and online life prediction. The model training comprises the steps of collecting original data of a complete life cycle and establishing a corresponding relation with the actual life of a cutter, preprocessing signals, extracting signal features to form feature vectors, performing cross validation to obtain an optimal cutter life model, and performing hyper-parameter adjustment and optimization. The online life prediction comprises real-time data acquisition, signal preprocessing, signal feature extraction to form afeature vector, input of THE optimal cutter life model based on optimal hyper-parameters and output of the residual life of the cutter. The number of eigenvalues extracted from each channel during model training is large, so that the training precision is high, the residual life of the cutter is accurately predicted, a residual life intelligent prediction model of the cutter is established, different regression models can be intelligently selected according to different working condition environments, and the model is good in generalization performance and high in portability.

Description

technical field [0001] The invention relates to the field of predicting the remaining life of a machine tool tool, in particular to a method for predicting the remaining life of a tool based on a machine learning regression algorithm. Background technique [0002] In the field of cutting processing, the state of the tool and its life are the main objects of concern during the machining process. The remaining life of the tool (wear degree) is a key factor affecting the quality of the processed workpiece. Tool failure may cause the surface roughness and dimensional accuracy of the workpiece to decline. Or cause more serious workpiece scrapping or machine tool damage. Adopting excessive protection strategies will cause waste of remaining tool life and unnecessary tool change downtime. It will effectively optimize the work schedule and reduce the cost of tool purchase. Therefore, in order to avoid tool damage caused by tool wear failure, damage to parts and machine tools, peop...

AI Technical Summary

Problems solved by technology

[0003] The traditional methods for predicting the remaining tool life are mainly divided into two types. The first method uses the method of accumulating tool equivalent cutting time to evaluate the tool life. This traditional life prediction method is not suitable for complex working conditions and will cause a large number of tools The conservativeness has gradually no longer adapted to the development of modern manufacturing

The other is regression prediction based on a certain machine learning algorithm model. Although this life prediction method can predict the life of the tool, it has a narrow scope of application, a single model, and low accuracy.

Images