The Study of the Effect of Pre-Existing Residual Stress in Rough Hard Turning on New Residual Stress in Dry SuperfinishHard Turning

粗硬车削中已有残余应力对干式超精硬车削中新残余应力影响的研究

基本信息

  • 批准号:
    9612022
  • 负责人:
  • 金额:
    $ 7.62万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    1996
  • 资助国家:
    美国
  • 起止时间:
    1996-10-01 至 1998-09-30
  • 项目状态:
    已结题

项目摘要

9612022 Liu Hard turning is an attractive alternative to the conventional precision machining practice of forming, turning, hardening, grinding and superfinishing, because several process steps can be eliminated. However, achieving this attractive result has been limited by lack of knowledge and the risk of thermal damage and residual stresses. This project's objectives are to understand and model: how pre-existing residual stresses affect subsequent residual stresses and chip formation, and how cutting conditions and the ratio of feed to depth of cut affect residual stresses. This investigation will involve both mathematical modeling and experiment. An experimental design matrix, replicated twice, will be used to assess the effects of 3 levels of flank wear, 2 feeds, two depths of cut, 2 cutting speeds and 2 tool nose radii. X-ray diffraction will be used to measure residual stress, and the experimental method of Tay et. al. used to determine temperature distributions. This will be complemented by a modeling effort using the finite element method to predict residual stresses based on a thermal-mechanical model of machining. Success in this project potentially eliminates lengthy and costly operations like finish grinding and superfinishing, in the production of precision components like bearings. This means higher productivity while maintaining quality.
9612022刘 硬车削是一种有吸引力的替代传统的精密加工实践的形成,车削,淬火,磨削和超精加工,因为几个工艺步骤可以消除。 然而,由于缺乏知识以及热损伤和残余应力的风险,实现这种有吸引力的结果受到限制。 该项目的目标是了解和建模:预先存在的残余应力如何影响随后的残余应力和切屑形成,以及切削条件和进给量与切削深度之比如何影响残余应力。 这项研究将涉及数学建模和实验。 将使用重复两次的实验设计矩阵来评估3个水平的后刀面磨损、2个进给、2个切削深度、2个切削速度和2个刀尖半径的影响。 X射线衍射将被用来测量残余应力,和Tay等的实验方法。用于确定温度分布。这将通过使用有限元法的建模工作来补充,以预测基于热机械加工模型的残余应力。 该项目的成功可能会消除轴承等精密部件生产中的精磨和超精加工等冗长而昂贵的操作。 这意味着在保持质量的同时提高生产率。

项目成果

期刊论文数量(0)
专著数量(0)
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会议论文数量(0)
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C. Richard Liu其他文献

Predicting Machine Low-Frequency Domain Errors by Finite Information Mapping Using Artificial Neural Network
  • DOI:
    10.1016/s1474-6670(17)49457-2
  • 发表时间:
    1992-10-01
  • 期刊:
  • 影响因子:
  • 作者:
    Hung-Kang Jan;C. Richard Liu
  • 通讯作者:
    C. Richard Liu

C. Richard Liu的其他文献

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{{ truncateString('C. Richard Liu', 18)}}的其他基金

Manufacturing of Self-Powered Nanosensor Systems by Pulsed Laser Processing
通过脉冲激光加工制造自供电纳米传感器系统
  • 批准号:
    1663214
  • 财政年份:
    2017
  • 资助金额:
    $ 7.62万
  • 项目类别:
    Standard Grant
Collaborative Research: Modeling Material Microstructure Evolution and Fatigue Life of High Strength Metal Components Produced by Laser Melting Additive Process
合作研究:模拟激光熔化增材工艺生产的高强度金属部件的材料微观结构演变和疲劳寿命
  • 批准号:
    1562960
  • 财政年份:
    2016
  • 资助金额:
    $ 7.62万
  • 项目类别:
    Standard Grant
Collaborative Research: SGER: Feasibility of a New Nano-Composite cBN Coating Method for Next Generation Cutting Tools for Harsh Hard Machining
合作研究:SGER:新型纳米复合材料 cBN 涂层方法的可行性,用于下一代严酷硬加工切削刀具
  • 批准号:
    0548357
  • 财政年份:
    2005
  • 资助金额:
    $ 7.62万
  • 项目类别:
    Standard Grant
A Novel Single-Step Superfinish Hole Making Process for Maximum Fatigue Life
一种新颖的单步超精加工孔加工工艺,可实现最大疲劳寿命
  • 批准号:
    9900169
  • 财政年份:
    1999
  • 资助金额:
    $ 7.62万
  • 项目类别:
    Standard Grant
Modeling and Eliminating Thermal Damage of Surface Integrity in Dry and Cryogenic Superfinish Hard Turning
干式和低温超精硬车削中表面完整性热损伤的建模和消除
  • 批准号:
    9700095
  • 财政年份:
    1997
  • 资助金额:
    $ 7.62万
  • 项目类别:
    Continuing Grant
Optimal Pre-stressing the Surface of a Component by Superfinish Hard Turning for Maxium Fatigue Life
通过超精硬车削对部件表面施加最佳预应力,以实现最大疲劳寿命
  • 批准号:
    9713748
  • 财政年份:
    1997
  • 资助金额:
    $ 7.62万
  • 项目类别:
    Standard Grant
The Study of the Mechanisms and the Construction of Models Of Residual Stress Formation in Machining Steels
切削加工钢材残余应力形成机制的研究及模型构建
  • 批准号:
    8021049
  • 财政年份:
    1981
  • 资助金额:
    $ 7.62万
  • 项目类别:
    Continuing Grant

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