Vibration Prediction Method of Spindle and Workpiece Based on Stacked Sparse Autoencoding Network

Abstract

The invention belongs to the field of cutting processing, and specifically discloses a method for predicting the vibration of a spindle and a workpiece based on a stack sparse automatic coding network, including S1 obtaining the spindle current signal, the cutting force signal and the actual vibration signal of the spindle and the workpiece under different cutting parameters ; S2 Input the spindle current signal, cutting force signal and cutting processing parameters into the sparse auto-encoding network layer for training, obtain deep time series features, and input them into the fully connected layer, train the entire network on the basis of pre-training parameters, and get Spindle and workpiece vibration signal prediction; S3 adjusts the stack sparse auto-encoding network according to the spindle and workpiece prediction and actual vibration signals, and completes the training to obtain a prediction model; the prediction model realizes the prediction of the spindle and workpiece vibration signals in cutting processing, which can replace The dynamic frequency response function has a good prediction effect in the time domain and frequency domain, can adapt to the working conditions of various combinations of processing parameters, and has strong generalization ability.

Description

technical field [0001] The invention belongs to the field of cutting processing, and more specifically relates to a method for predicting the vibration of a spindle and a workpiece based on a stacked sparse auto-encoding network. Background technique [0002] Today, the manufacturing industry is moving forward from digitization and informatization to intelligence. The monitoring of the manufacturing process is the core of intelligent manufacturing. How to effectively monitor the processing status is the core area of ​​research and development that countries around the world are committed to. [0003] During the cutting process, it is very difficult to obtain the dynamic frequency response function of a certain subsystem of the machining system. Most of the existing research methods are mathematical analysis methods, or the static response function of the subsystem is obtained through hammering experiments. The static response function approximates the dynamic response functi...

AI Technical Summary

Problems solved by technology

Although these two methods can reflect some characteristics of the dynamic frequency response to a certain extent, they each have their own shortcomings. The analysis and calculation process of the former is very complicated, and the latter directly loses the influence of the dynamic cutting process on the frequency response.

Images