制造工艺与装备

高速切削过程绝热剪切局部化断裂预测

  • 谷丽瑶 ,
  • 王敏杰
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  • 1. 大连理工大学精密和特种加工教育部重点实验室 大连 116024;
    2. 西南交通大学材料科学与工程学院 成都 611756
谷丽瑶,男,1983年出生,博士,讲师。主要研究方向为高速切削机理与应用。E-mail:guliyao0922@163.com;王敏杰(通信作者),男,1958年出生,教授,博士研究生导师。主要研究方向为高速切削与先进制造技术、微成型及其模具技术、模具CAD/CAE/CAM技术、高分子材料成型技术、水中航行体流体动力学。E-mail:mjwang@dlut.edu.cn

网络出版日期: 2016-03-05

基金资助

* 国家自然科学基金资助项目(51175063)

Prediction of Adiabatic Shear Localization Fracture in High Speed Machining

  • GU Liyao ,
  • WANG Minjie
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  • 1. Key Laboratory of Ministry of Education for Precision and Non-traditional Machining, Dalian University of Technology, Dalian 116024;
    2. School of Materials Science and Engineering, Southwest Jiaotong University, Chengdu 611756

Online published: 2016-03-05

摘要

基于高速切削过程绝热剪切饱和极限理论,结合锯齿形切屑绝热剪切带的变形和受力条件,以及材料的动态塑性本构关系,建立以切削速度、切削厚度和刀具前角为预测变量的高速切削过程绝热剪切局部化断裂的预测模型,并以淬硬45钢和FV520(B)不锈钢为例,预测其发生绝热剪切局部化断裂的临界切削条件。通过高速切削试验和金相试验,讨论了切削条件对绝热剪切局部化断裂过程的影响规律和敏感程度,验证了绝热剪切局部化断裂的预测结果。结果表明:较大切削厚度和较小刀具前角会降低绝热剪切局部化断裂的临界切削速度,建立的绝热剪切局部化断裂预测模型能有效预测切屑发生绝热剪切局部化断裂的临界切削条件。

本文引用格式

谷丽瑶 , 王敏杰 . 高速切削过程绝热剪切局部化断裂预测[J]. 机械工程学报, 2016 , 52(5) : 185 -192 . DOI: 10.3901/JME.2016.05.186

Abstract

Based on the theory of adiabatic shear saturation limit, combining with the deformation and loading conditions of adiabatic shear band (ASB) in serrated chip and dynamic constitutive relation, the prediction model of adiabatic shear localization fracture with variables of cutting speed, cutting depth and rake angle is established. The critical cutting condition of ASLF of hardened 45 steel is predicted and verified through high speed cutting experiment and metallography experiment. The influence of cutting conditions on adiabatic shear localization fracture and the corresponding sensibility are discussed. The results show that large cutting depth and small rake angle can reduce the critical cutting speed of adiabatic shear localization fracture. The critical cutting condition of adiabatic shear localization fracture in chip can be predicted effectively through adiabatic shear localization fracture prediction model.

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