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Effect of cutting parameters on tool wear in diamond turning of new optical aluminium grade

OTIENO T ABOU-El-HOSSEIN K

切削参数对新型光学铝级金刚石车削刀具磨损的影响[J]. 中国光学(中英文), 2016, 9(5): 579-587. doi: 10.3788/CO.20160905.0579
引用本文: 切削参数对新型光学铝级金刚石车削刀具磨损的影响[J]. 中国光学(中英文), 2016, 9(5): 579-587. doi: 10.3788/CO.20160905.0579
OTIENO T, ABOU-El-HOSSEIN K. Effect of cutting parameters on tool wear in diamond turning of new optical aluminium grade[J]. Chinese Optics, 2016, 9(5): 579-587. doi: 10.3788/CO.20160905.0579
Citation: OTIENO T, ABOU-El-HOSSEIN K. Effect of cutting parameters on tool wear in diamond turning of new optical aluminium grade[J]. Chinese Optics, 2016, 9(5): 579-587. doi: 10.3788/CO.20160905.0579

切削参数对新型光学铝级金刚石车削刀具磨损的影响

详细信息
  • 中图分类号: TH752

Effect of cutting parameters on tool wear in diamond turning of new optical aluminium grade

doi: 10.3788/CO.20160905.0579
Funds: Supported by the National Research Foundation, South Africa through the National Equipment Programme and the Technology Innovation Agency
More Information
    Author Bio:

    Timothy Otieno is a PhD Engineering student at the Nelson Mandela Metropolitan University. He received his BEng and MEng degree in Mechatronics from Nelson Mandela Metropolitan University in 2011 and 2012, respectively. His research focuses on the ultra-high precision machining of rapidly solidified aluminium alloys.E-mail:timothy.otieno@nmmu.ac.za

    Corresponding author: E-mail:khaled.abou-el-hossein@nmmu.ac.za
  • 摘要: 光学器件和光学测量系统的关键部件主要通过超精密加工制造。铝合金具有很多优势,通常用于光子产业。光学领域对铝合金使用和需求的不断增加,促进了在铸造过程中采用快速凝固技术对铝合金等级重新改良的发展。优异的微观结构和改进的机械和物理性能是新型铝合金等级的特点。目前主要问题在于采用金刚石车削时,由于在切削性方面缺乏对铝合金性能的充分研究,导致机械加工数据库非常有限。本文通过改变金刚石的切削参数,测量切齿安装距超过4 km时金刚石刀具的磨损,研究了快速凝固铝合金RSA 905的切削性能。改变的机械加工参数为切削速度、进给速度和切削深度。结果表明切削速度对金刚石刀具的磨损影响最大。主轴转速为500 rpm、进给速度为25 mm/min、切削深度为15 μm时,刀具磨损达到最大值12.2 μm;主轴转速为1750 rpm、进给速度为5 mm/min、切削深度为5 μm时,刀具磨损达到最小值2.45 μm。通常,较高的切削速度、较低的进给速度和较短的切削深度的组合可以减少金刚石刀具磨损。建立了模型统计以分析金刚石刀具磨损。通过该模型可以生成磨损图,从而确定切削参数产生最小磨损的区域。结果证明,快速凝固铝是更好的选择,为机械工程师使用这种材料提供了参考。

     

  • Figure 1.  Diamond turning setup

    Figure 2.  Diamond tool tip wear area

    Figure 3.  SEM image of wear land measured for Experiment 3 tool

    Figure 4.  Chip build-up during the low speed cutting

    Figure 5.  Normal probability plot for the tool wear model

    Figure 6.  Effect of the cutting speed on the tool wear model

    Figure 7.  Effect of the feed rate on the tool wear model

    Figure 8.  Effect of the depth of cut on the tool wear model

    Figure 9.  Wear map for 5 m depth of cut

    Figure 10.  Wear map for 15 m depth of cut

    Figure 11.  Wear map for 25 m depth of cut

    Table  1.   Physical and mechanical properties of AA 6061, AA 6082 and RSA 905[8-10]

    Physical mechanical properties AA 6061 AA 6082 RSA 905
    Density/(g·cm-3) 2.7 2.7 2.95
    Thermal expansion(10-6/K) 23 24 19
    Thermal conductivity.(W·m-1·K-1) 166 180 100
    Modulus of elasticity/GPa 70 70 91
    Ultimate tensile strength/MPa 310 340 600
    Yield strength/MPa 270 290 480
    Elongation/% 12 6 6
    Hardness 100 95 180
    下载: 导出CSV

    Table  2.   Wear results

    Exp No. Spindle
    speed/rpm
    Feed rate/
    (mm·min-1)
    Feed rate/
    (μm·rev-1)
    Depth of
    cut/μm
    Passes Total
    time/s
    MRR/
    (mm3·min-1)
    Wear/μm
    1 1 750 25 14.29 25 20 24 117.81 5.37
    2 1 750 25 14.29 5 20 24 23.56 5.07
    3 3 000 5 1.67 15 2 14 14.14 2.40
    4 1 750 15 8.57 15 12 24 42.41 5.72
    5 1 750 15 8.57 15 12 24 42.41 5.72
    6 1750 5 2.86 25 4 24 23.56 4.01
    7 500 15 30.00 25 42 85 70.69 8.00
    8 3 000 25 8.33 15 12 14 70.69 3.36
    9 3 000 15 5.00 25 7 14 70.69 3.05
    10 500 25 50.00 15 71 85 70.69 12.20
    11 3 000 15 5.00 5 7 14 14.14 2.84
    12 500 5 10.00 15 14 85 14.14 3.26
    13 1 750 5 2.86 5 4 24 4.71 2.45
    14 1 750 15 8.57 15 12 24 42.41 5.72
    15 500 15 30.00 5 42 85 14.14 3.76
    下载: 导出CSV

    Table  3.   Lack of fit tests for tool wear model

    Source Sum of squares Mean square P-value
    Linear 1.840 881 0.204 542 0.002 2
    2FI 0.448 910 0.074 818 0.005 9
    Quadratic 0.034 532 0.011 510 0.037 4
    Cubic 0
    Pure Error 0.000 889 0.000 444
    下载: 导出CSV

    Table  4.   Analysis of variance for tool wear model

    Source Sum of squares Mean square P-value
    Model 3.439 845 0.491 406 <0.000 1
    A-Spindle speed 0.767 201 0.767 201 <0.000 1
    B-Feed rate 0.603 784 0.603 784 <0.000 1
    C-Depth of cut 0.264 068 0.264 068 0.000 2
    AB 1.185 989 1.185989 <0.000 1
    AC 0.171 462 0.171 462 0.000 7
    BC 0.034 519 0.034519 0.037 8
    C^2 0.412 819 0.412 819 <0.000 1
    Residual 0.036 980 0.005 282
    Lack of fit 0.036 090 0.007 218 0.059 0
    Pure error 0.000 889 0.000 444
    Cor total 3.476 826
    下载: 导出CSV
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出版历程
  • 收稿日期:  2016-04-21
  • 修回日期:  2016-05-30
  • 刊出日期:  2016-10-01

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