A multi-sensing signal fusion monitoring method and system for dimensionality reduction of milling data of thin-walled parts

Abstract

The invention discloses a multi-sensing signal fusion monitoring method and system for dimensionality reduction of milling data of thin-walled parts. The technical solution is to: obtain the original signal characteristic matrix; calculate the natural frequency of the workpiece and the tool according to the frequency response function, and calculate the corresponding milling parameters The main shaft rotation frequency and the tooth passing frequency are calculated, and the frequency bands where all wavelet packets are located are calculated; whether the wavelet packet frequency band contains the natural frequency of the workpiece, the tool natural frequency, the main shaft rotation frequency and the tooth passing frequency, and the frequency bands of different wavelet packets are respectively Take the value to get the eigenvector of the fusion multi-sensor signal; according to the eigenvector of the fusion multi-sensor signal, the original signal feature matrix is ​​converted into the fusion feature matrix; according to the requirement of the contribution degree in the milling state identification, the dimension-reduced feature vector matrix is ​​obtained , and output data dimensionality reduction results. The invention can reduce the complexity of data, and can effectively fuse the features of multi-sensing signals without affecting the training accuracy of machine learning.

Description

technical field [0001] The invention relates to the technical field of machining process detection, in particular to a multi-sensing signal fusion monitoring method and system for dimensionality reduction of milling data of thin-walled parts. Background technique [0002] Thin-walled parts such as aero-engine fans and low-pressure compressor rotor blades are mostly made of difficult-to-machine materials. Due to their difficulty in machining and poor local rigidity at the edges, problems such as machining chatter, tool wear, and damage during cutting seriously affect production efficiency. and workpiece service fatigue performance. The data-driven milling process monitoring technology can realize real-time online monitoring of the machining process through the real-time interaction of sensor data and self-learning of historical processing data by means of signal processing. However, different sensing signals in the processing process have different sensitivities to milling s...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the deficiencies in the prior art, the purpose of the present invention is to provide a multi-sensor signal fusion monitoring method and system for dimensionality reduction of thin-walled milling data, which solves the problem of multi-sensor signal feature data dimensionality reduction in the existing multi-sensor signal feature data dimensionality reduction method. The problem of low efficiency of sensory signal fusion strategy, low data compression efficiency, and large amount of calculation in the training process can reduce data complexity, high data compression rate, high calculation efficiency, and effectively integrate multiple sensors without affecting the accuracy of machine learning training. signal characteristics

Images