Transactions of Materials and Heat Treatment

Title：Prediction on wear loss of warm extrusion die based on BP neural network

Authors： Zhang Tao Fan Wenxin Guo Daifeng Shi Yongpeng

Unit： North University of China

KeyWords： warm extrusion die Deform-3D wear loss BP neural network orthogonal experiment

ClassificationCode：TG376

year,vol(issue):pagenumber：2017,42(2):178-182

Abstract：

For punch badly worn in the process of connecting rod bushing blank production, according to the warm extrusion principle and processing characteristics, four major factors influencing the wear life of warm extrusion punch were obtained, namely die initial hardness, friction coefficient, extrusion speed and pre-heating temperature．Then, the standard orthogonal experiment table with four factors and three levels was designed to minimize punch wear loss. Furthermore, based on the theory of Archard wear, the orthogonal simulation experiments of warm extrusion punch wear were executed by software Deform-3D. Finally, according to data from the experiment, three-layer BP neural network predicted model of 4-15-1 was established, and the error between the predicted value and the numerical simulation value was less than 3%. Therefore, the above method could predict the wear loss of warm extrusion die quickly.

Funds：

山西省自然科学基金资助项目(2012011023-2);山西省高校高新技术产业化项目（20120021）

张涛(1992-)，男，硕士研究生樊文欣(1964-)，男，博士，教授

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