Quality prediction of steel pipe extrusion based on RBF neural network

Abstract

Predicting and optimizing extrusion molding process parameters is a significant and challenging research topic in the field of manufacturing, but machine learning-based approaches remain relatively scarce. In this study, orthogonal experiments were designed to simulate the extrusion process using Simu fact Forming software. The thinning rate, stamping speed, and friction coefficient were used as independent variables. Subsequently, prediction models for extrusion force and equivalent force were established based on a Radial Basis Function (RBF) neural network. The data obtained from the simulation software were used as the training set, and additional experiments outside the training set were designed for validation. The model achieved a relative error below 6%, demonstrating its reliability. This study not only proposes a novel method for predicting process parameters in steel pipe extrusion but also holds significant value for optimizing these parameters and improving product quality. The application of this model can benefit various manufacturing sectors, including automotive, aerospace, and construction, where precise control of extrusion parameters is critical for enhancing product performance and reducing material waste.