CNN-GRU network-based force prediction approach for variable working condition milling clamping points of deformable parts

Enming Li,Zeyu Li,Tengyuan Jiang,Jingtao Zhou,Huibin Zhang,Changsen Yang,Mingwei Wang

doi:10.1007/s00170-021-08520-2

Abstract

Improper clamping is one of the major causes of part deformation. Improving the fixture arrangement through force analysis of clamping points is an effective means to suppress or improve machining deformation. However, the existing research focuses on the monitoring and off-line optimization of the clamping point force, which has a certain lag on the machining deformation control, and it is difficult to predict the clamping point force due to the time-varying coupling effect of multiple factors such as process parameters, cutting force, and clamping point force in the machining process. Inspired by the excellent performance of convolutional neural networks and gated recurrent networks in feature extraction and learning of temporal association laws, this paper proposes a CNN-GRU-based method for predicting the force state of clamping points under variable working conditions. Firstly, a force prediction model of clamping point during milling process with variable working conditions is established. Secondly, a convolutional neural network is designed to extract the features of dynamic coupled machining conditions. Then, a network of gated recurrent units is constructed to learn the temporal correlation law between the machining conditions and the forces on the clamping points to achieve force prediction of the clamping points during machining. Finally, it was verified by the milling process of the piston skirt. The results show that CNN-GRU can effectively predict the clamping force. In addition, CNN-GRU has higher computational efficiency and accuracy compared with CNN-LSTM, CNN-RNN and CNN-BP.

Highlights

Part processing deformation is a common problem in CNC machining, such as the machining deformation of large-size integral parts in aerospace industry, diesel engine piston skirt, automobile impeller, etc., which seriously affects the machining accuracy and surface quality of the workpiece [1, 2]
This paper proposes a CNN-based feature extraction of time-varying working conditions of machining process, with gated recurrent unit (GRU)-based learning of dynamic time-series relationship between variable working conditions and clamping point force for clamping point force prediction
We propose a CNN-GRU based force prediction model for clamping points

Summary

Introduction

Part processing deformation is a common problem in CNC machining, such as the machining deformation of large-size integral parts in aerospace industry, diesel engine piston skirt, automobile impeller, etc., which seriously affects the machining accuracy and surface quality of the workpiece [1, 2]. Deformation caused by external loads (clamping, cutting forces) that elastically deform the workpiece during processing, being recovered afterwards [3] This means that the fixture becomes a key component to avoid the geometrical error associated to deformation caused by external loads during machining processes. Accurate prediction of clamping point force can help to correct fixture arrangement and clamping force, and effectively restrain or improve machining deformation. From this perspective, it is a good entry point to study the prediction of clamping point force during the machining of thin-walled parts. The variation of clamping point force is influenced by a combination of several factors such as cutting parameters, cutting force, and vibration during machining, and these factors show complex coupling changes in time sequence as the working conditions change, making it a challenge to accurately predict the clamping point force under variable working conditions

Methods

Discussion

Conclusion