Experimental Testing and Theoretical Modeling of Tool Wear in Turning GH901 Superalloy
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摘要: 用A、B和C这3种不同型号的刀具，采用40 m/min和60 m/min两种切削速度对高温合金GH901进行了车削实验，通过观察刀具后刀面磨损状态，建立了各类型刀具的主后刀面磨损曲线，并根据刀具磨损情况和磨损曲线对刀具类型进行了优选，最后利用典型磨损曲线建立了刀具磨损预测模型。实验与模型预测结果的最大均方根误差和最大平均绝对误差分别为0.043 49，0.039 43 mm，误差偏小证明了该预测模型的有效性。综合车削性能由高到低依次是A、B和C刀具。切屑类型以长环形螺旋切屑为主，切削速度越高，刀具寿命明显降低。Abstract: The turning experiments of Superalloy GH901 were carried out with three different types of cutting tools A, B and C at 40m/min and 60m/min.By observing the tool flank wear state, the life curves of tool are established and the tool types are optimized according to the tool wear and life curves. Finally, the prediction model for tool wearis established.The maximum root mean square error and mean absolute error of the experimental and the prediction results are 0.04349 and 0.03943 mm, respectively,the small error proves the effectiveness of the prediction model. The comprehensive cutting performance from high to low is toolA, tool B and tool C. The chip type is dominated by long annular spiral chips, and the tool life is significantly reduced with the higher cutting speed.
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Key words:
- tool wear /
- superalloy GH901 /
- wear prediction model /
- turning experiment
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图 3 A型刀具40 m/min和60 m/min最终磨损状态
Figure 3. The final wear condition of A-type tool at the cutting speed of 40 m/min and 60 m/min
图 5 B型刀具40 m/min和60 m/min最终磨损状态
Figure 5. The final wear condition of B-type tool at the cutting speed of 40 m/min and 60 m/min
图 7 C型刀具40 m/min和60 m/min最终磨损状态
Figure 7. The final wear condition of C-type tool at the cutting speed of 40 m/min and 60 m/min
图 8 Vc = 40 m/min时，A,B,C型刀具磨损曲线
Figure 8. Wear curve of A, B, C type tool when Vc = 40 m/min
图 9 Vc = 60 m/min，A,B,C型刀具磨损曲线
Figure 9. Wear curve of A, B, C type tool when Vc = 60 m/min
图 10 A，B，C型刀具在不同切削速度下的刀具寿命
Figure 10. The tool life of A, B, C types under different cutting speeds
图 13 A刀具预测与实验结果对比
Figure 13. Comparison of the prediction of tool A with experimental results
图 14 B刀具预测与实验结果对比
Figure 14. Comparison of The prediction of tool B withexperimental results
图 15 C刀具预测与实验结果对比
Figure 15. Comparison of the prediction of tool C with experimental results
表 1 高温合金GH901各元素的质量分数
Table 1. Chemical composition of GH901 superalloy
% C Si Cr Ni Ti Al P B Cu Co Mo Fe 0.031 0.045 12.43 42.2 3.06 0.22 0.015 0.015 0.003 0.015 6.0 余量 
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表 2 GH901高温合金的力学性能（室温下）
Table 2. Mechanical properties of GH901 superalloy at room temperature
抗拉强度 屈服强度 伸长率 收缩率 1203 MPa 892 MPa 22% 41.5%
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表 3 3种类型刀具的磨损预测模型参数值
Table 3. Parameter values of wear prediction model for three types of tools
模型参数 A刀具 B刀具 C刀具 C 0.0641 0.0682 0.0782 D 0.352 0.373 0.532 K 0.00000033 0.00000063 0.00000043
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表 4 磨损预测模型误差值
Table 4. The error value of wear prediction model
刀具类型 pmae/mm prmse A 0.02897 0.03334 B 0.02400 0.02782 C 0.03943 0.04349
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[1] 刘建伟, 李寒荣, 万方前. 汽轮机高温合金GH901材质阀杆车削工艺设计[J]. 机械研究与应用, 2020, 33(4): 177-179.LIU J W, LI H R, WAN F Q. Turning technology design for the steam turbine superalloy GH901 valve turbine[J]. Mechanical Research & Application, 2020, 33(4): 177-179. (in Chinese) [2] 高奇, 郭光岩, 李文博. 微铣削单晶高温合金刀具磨损实验研究[J]. 现代制造工程, 2021(4): 99-103.GAO Q, GUO G Y, LI W B. Experimental study on tool wear of single crystal superalloy in micro-milling[J]. Modern Manufacturing Engineering, 2021(4): 99-103. (in Chinese) [3] 韩变枝, 刘公雨, 陈明, 等. 高效铣削钛合金涂层硬质合金刀具优选及磨损试验研究[J]. 制造技术与机床, 2018(8): 152-157.HAN B Z, LIU G G, CHEN M, et al. Experimental study on wear and optimization of coated carbide tools for high-efficiency cutting titanium alloys[J]. Manufacturing Technology & Machine Tool, 2018(8): 152-157. (in Chinese) [4] 柯清禅, 孙剑飞, 王天明. 硬质合金涂层刀具车削钛合金TA15的磨损研究及切削参数优选[J]. 工具技术, 2017, 51(12): 23-28.KE Q C, SUN J F, WANG T M. Study on tool wear and cutting parameters optimization during turning work of Titanium Alloy TA15 with coated carbide tool[J]. Tool Engineering, 2017, 51(12): 23-28. (in Chinese) [5] 严复钢, 吕亚飞, 李艳国, 等. 硬质合金刀具高温磨损过程机理分析[J]. 工具技术, 2017, 51(12): 22-27.YAN F G, LV Y F, LI Y G, et al. Analysis of high temperature wear process mechanism of cemented carbide tool[J]. Tool Engineering, 2017, 51(12): 22-27. (in Chinese) [6] LIU E L, AN W Z, XU Z C, et al. Experimental study of cutting-parameter and tool life reliability optimization in Inconel 625 machining based on wear map approach[J]. Journal of Manufacturing Processes, 2020, 53: 34-42. doi: 10.1016/j.jmapro.2020.02.006 [7] 常昊, 马廉洁, 万学文, 等. 车削可加工陶瓷刀具磨损模型研究[J]. 机械科学与技术, 2020, 39(1): 83-87.CHANG H, MA L J, WAN X W, et al. Study on wear model for cutting tools of machinable ceramic[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(1): 83-87. (in Chinese) [8] LAGARDE Q, WAGNER V, DESSEIN G, et al. Effect of temperature on tool wear during milling of Ti64[J]. Journal of Manufacturing Science and Engineering, 2021, 143(7): 071007. doi: 10.1115/1.4049847 [9] HAN D Y, YU J S, TANG D Y. An HDP-HMM based approach for tool wear estimation and tool life prediction[J]. Quality Engineering, 2021, 33(2): 208-220. doi: 10.1080/08982112.2020.1813760 [10] 董靖川, 徐明达, 王太勇, 等. 分布式卷积神经网络在刀具磨损量预测中的应用[J]. 机械科学与技术, 2020, 39(3): 329-335.DONG J C, XU M D, WANG T Y, et al. Application of distributed convolutional neural network in wear prediction of tool[J]. Mechanical Science and Technology for Aerospace Engineering, 2020, 39(3): 329-335. (in Chinese) [11] 何彦, 凌俊杰, 王禹林, 等. 基于长短时记忆卷积神经网络的刀具磨损在线监测模型[J]. 中国机械工程, 2020, 31(16): 1959-1967.HE Y, LING J J, WANG Y L, et al. In-process tool wear monitoring model based on LSTM-CNN[J]. China Mechanical Engineering, 2020, 31(16): 1959-1967. (in Chinese) [12] 肖鹏飞, 张超勇, 罗敏, 等. 基于自适应动态无偏最小二乘支持向量机的刀具磨损预测建模[J]. 中国机械工程, 2018, 29(7): 842-849.XIAO P F, ZHANG C Y, LUO M, et al. Modeling method for tool wear prediction based on ADNLSSVM[J]. China Mechanical Engineering, 2018, 29(7): 842-849. (in Chinese) [13] 袁军, 刘丽冰, 张艳蕊, 等. 刀具磨损状况的检测方法研究综述[J]. 现代制造工程, 2021(3): 152-160.YUAN J, LIU L B, ZHANG Y R, et al. Survey of research on detection methods for tool wear condition[J]. Modern Manufacturing Engineering, 2021(3): 152-160. (in Chinese) [14] 杨树宝, 倪宏超, 朱国辉. TC4钛合金切削刀具磨损对切削过程影响的研究[J]. 机械科学与技术, 2014, 33(8): 1183-1185.YANG S B, NI H C, ZHU G H. Effect of the tool wear on the cutting process of Ti6Al4V titanium alloy[J]. Mechanical Science and Technology for Aerospace Engineering, 2014, 33(8): 1183-1185. (in Chinese) [15] ZHANG Y, ZHU K P, DUAN X Y, et al. Tool wear estimation and life prognostics in milling: model extension and generalization[J]. Mechanical Systems and Signal Processing, 2021, 155: 107617. doi: 10.1016/j.ymssp.2021.107617 [16] ZHU K P, ZHANG Y. A generic tool wear model and its application to force modeling and wear monitoring in high speed milling[J]. Mechanical Systems and Signal Processing, 2019, 115: 147-161. doi: 10.1016/j.ymssp.2018.05.045 -
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