Prediction and control of surface roughness for high speed machining of titanium alloy TC4

ZHANG Yun-jian; QIN Guo-hua; HOU Yuan-jun; ZHAO Yang-yang; XIE Wen-bin

doi:10.3788/OPE.20162413.0543

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Prediction and control of surface roughness for high speed machining of titanium alloy TC4

更新时间：2020-08-13

- Prediction and control of surface roughness for high speed machining of titanium alloy TC4
- Optics and Precision Engineering Vol. 24, Issue 10s, Pages: 543-550(2016)
- 作者机构：
  
  1. 南昌航空大学航空制造工程学院,江西南昌,330063
  2. 中国人民解放军 94829部队, 江西向塘,330201
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20162413.0543
  CLC： TG75
- Received：20 May 2016，
  
  Revised：10 June 2016，
  
  Published：14 November 2016
- 稿件说明：
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张运建, 秦国华, 侯源君等. 高速切削钛合金TC4的表面粗糙度预测与控制方法[J]. 光学精密工程, 2016,24(10s): 543-550

ZHANG Yun-jian, QIN Guo-hua, HOU Yuan-jun etc. Prediction and control of surface roughness for high speed machining of titanium alloy TC4[J]. Editorial Office of Optics and Precision Engineering, 2016,24(10s): 543-550
张运建, 秦国华, 侯源君等. 高速切削钛合金TC4的表面粗糙度预测与控制方法[J]. 光学精密工程, 2016,24(10s): 543-550 DOI： 10.3788/OPE.20162413.0543.

ZHANG Yun-jian, QIN Guo-hua, HOU Yuan-jun etc. Prediction and control of surface roughness for high speed machining of titanium alloy TC4[J]. Editorial Office of Optics and Precision Engineering, 2016,24(10s): 543-550 DOI： 10.3788/OPE.20162413.0543.

摘要

零件表面粗糙度是衡量工件表面质量的重要参数，实际加工中表面粗糙度的影响因数具有复杂性与不确定性。然而在众多因数中，切削参数对表面粗糙度具有显著影响，并且能够在加工中人为控制。因此，选取适合的切削参数，提高工件表面质量是一项非常重要的任务。本文采用均匀设计法进行钛合金TC4的切削实验，利用德国马尔MarSurf M 300C精密型表面粗糙度测量仪测得工件表面粗糙度，然后运用非线性回归求解技术，建立了表面粗糙度的预测模型。采用方差分析法检验预测模型的拟合度及各独立输入参数的显著性，并进行预测误差对比分析。实验结果表明，所建立的回归预测模型预测误差低至0.019%，具有精度高、可靠性强等特点。最后，提出了以最小表面粗糙度为目标，使用遗传算法优化求解技术，建立切削参数优化模型。这项研究的结果为加工表面粗糙度的预测提供理论上的依据，为提高加工表面质量提供切削参数的合理选择。

Abstract

Surface roughness of part is an important parameter to measure surface quality of workpiece. In actual processing

impact factor of surface roughness is complex and uncertain

but in many factors

cutting parameter has obvious effect on surface roughness

and it can be controlled manually in processing. Therefore

selecting proper cutting parameter and improving surface quality of workpiece are a very important tasks. Uniform design method is adopted in this paper to perform cutting experiment of titanium alloy TC4. Measure surface roughness of workpiece with Marl MarSurf M 300C precise surface roughness tester of Germany

and then employ nonlinear regression solution technology to establish prediction model of surface roughness. Inspect fitting degree of prediction model and significance of each independent input parameter by adopting analysis of variance. Perform contrastive analysis of prediction error. Experimental result shows that prediction error of regression prediction model established is as low as 0.019% with feature of high precision and strong reliability etc. Finally it puts forward to take the minimum surface roughness as target

use genetic algorithm to optimize solution technology and establish optimization model of cutting parameter. The research result provides theoretical basis for prediction of surface roughness processing and provides reasonable choice of cutting parameter to improve surface quality of processing.

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references

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