CN 41-1243/TG ISSN 1006-852X

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

面向航空航天难加工材料磨削过程的模拟与智能控制

赵彪 雷小飞 陈涛 丁文锋 傅玉灿 徐九华 李海

赵彪, 雷小飞, 陈涛, 丁文锋, 傅玉灿, 徐九华, 李海. 面向航空航天难加工材料磨削过程的模拟与智能控制[J]. 金刚石与磨料磨具工程, 2023, 43(2): 127-143. doi: 10.13394/j.cnki.jgszz.2023.1002
引用本文: 赵彪, 雷小飞, 陈涛, 丁文锋, 傅玉灿, 徐九华, 李海. 面向航空航天难加工材料磨削过程的模拟与智能控制[J]. 金刚石与磨料磨具工程, 2023, 43(2): 127-143. doi: 10.13394/j.cnki.jgszz.2023.1002
ZHAO Biao, LEI Xiaofei, CHEN Tao, DING Wenfeng, FU Yucan, XU Jiuhua, LI Hai. Simulation and intelligent control during grinding process for difficult-to-machine materials in aerospace[J]. Diamond & Abrasives Engineering, 2023, 43(2): 127-143. doi: 10.13394/j.cnki.jgszz.2023.1002
Citation: ZHAO Biao, LEI Xiaofei, CHEN Tao, DING Wenfeng, FU Yucan, XU Jiuhua, LI Hai. Simulation and intelligent control during grinding process for difficult-to-machine materials in aerospace[J]. Diamond & Abrasives Engineering, 2023, 43(2): 127-143. doi: 10.13394/j.cnki.jgszz.2023.1002

面向航空航天难加工材料磨削过程的模拟与智能控制

doi: 10.13394/j.cnki.jgszz.2023.1002
基金项目: 航空发动机及燃气轮机基础科学中心项目(P2022-A-IV-002-001); 国家自然科学基金青年项目(52205475); 江苏省自然科学基金青年项目(BK20210295); 江苏省卓越博士后计划(2022ZB215); 直升机传动技术国家重点实验室项目(HTL-A-22G12)。
详细信息
    作者简介:

    赵彪,男,1991年生,博士、讲师。主要研究方向:难加工材料磨削机理与过程优化。E-mail:zhaobiao@nuaa.edu.cn

    通讯作者:

    丁文锋,男,1978年生,博士、教授。主要研究方向:难加工材料高效精密加工技术与应用研究。E-mail:dingwf2000@vip.163.com

  • 中图分类号: TG58; TG74

Simulation and intelligent control during grinding process for difficult-to-machine materials in aerospace

Funds: Science Center for Gas Turbine Project (P2022-A-IV-002-001), National Natural Science Foundation of China (52205475), Natural Science Foundation of Jiangsu Province ( BK20210295), Superior Postdoctoral Project of Jiangsu Province (2022ZB215), National Key Laboratory of Science and Technology on Helicopter Transmission (HTL-A-22G12)
More Information
    Author Bio:

    ZHAO Biao (1991—), Male, Ph.D., Lecturer, Research focus: mechanism and process optimization in grinding of difficult-to-cut materials

    Corresponding author: Email: dingwf2000@vip.163.com) Corresponding author: DING Wen-feng (1978—), Male, Ph.D., Professor, Research focus: high-efficiency precision machining technology and applications on difficult-to-cut materials.
  • 摘要:

    近年来,钛合金、高温合金、金属间化合物、高强度钢等难加工材料凭借优异性能广泛应用于航空航天领域关键构件。磨削作为难加工材料及关键构件精密制造的终加工方法,对制造质量与生产效率具有直接影响。然而,由于材料的难加工特性以及磨削过程的复杂性,导致磨削过程极易出现磨削力大、磨削温度高、砂轮磨损严重以及加工质量差等问题。本文针对航空航天难加工材料,以磨削加工过程模拟与智能控制技术为主线,总结了磨削过程力、温度、砂轮磨损及表面完整性等方面的研究进展和现存问题。最后,本文针对当前研究存在的主要问题,对未来磨削过程模拟与智能控制技术的发展趋势进行了展望。

     

  • 图  1  高强韧难加工材料应用范围

    Figure  1.  Applications of difficult-to-cut materials

    图  2  磨削过程各参量间关系

    Figure  2.  Relationship of each factor during grinding processes

    图  3  砂轮与齿面接触几何建模[18]

    Figure  3.  Geometric modeling of contact between grinding wheel and tooth surface[18]

    图  4  榫头成型磨削温度场预测[27]

    Figure  4.  Temperature field prediction of creep feed grinding blade root[27]

    图  5  考虑磨粒与工件相互作用(包括摩擦、犁耕和切削)的理论三维温度图[6]

    Figure  5.  Theoretical 3D temperature map considering different regimes of grain-workpiece interactions (including rubbing, ploughing and cutting)[6]

    图  6  砂轮轮廓磨损采集[34]

    Figure  6.  Grinding wheel profile wear collection[34]

    图  7  表面完整性范畴[35]

    Figure  7.  Scope of surface integrity[35]

    图  8  二维仿真磨粒轨迹与工件轮廓形貌[36]

    Figure  8.  2D simulation of abrasive trajectory and workpiece profile[36]

    图  9  两种模型在不同磨削方向上的残余应力分布[41]

    Figure  9.  Residual stress distribution of two models in different grinding directions [41]

    图  10  齿轮切削几何模型建模与形貌预测对比图[43]

    Figure  10.  Comparison between gear cutting geometric model modeling and profile prediction[43]

    图  11  机器人磨削系统与控制策略框图[49]

    Figure  11.  Block diagram of robot grinding system and control strategy[49]

    图  12  有限元模型与数字孪生结合过程示意图[52]

    Figure  12.  Schematic diagram of combination process of finite element model and digital twin[52]

    图  13  磨削温度监测流程图[53]

    Figure  13.  Flow chart of grinding temperature monitoring[53]

    图  14  视觉轮廓磨削系统及工件局部轮廓在线误差检测原理图[60]

    Figure  14.  Schematic diagram of visual contour grinding system and local contour image of workpiece online error detection[60]

    图  15  在线监测自适应控制系统模型[61]

    Figure  15.  On-line monitoring adaptive control system model[61]

    图  16  改进烟花算法优化过程结构图[65]

    Figure  16.  Improved fireworks algorithm optimization process structure diagram[65]

    图  17  航天航空难加工材料磨削过程的模拟与智能控制总结与展望

    Figure  17.  Summary and prospect of simulation and intelligent control of grinding process of difficult materials in aerospace

  • [1] 李本凯, 丁文锋, 马艳艳, 等. 新型刚玉砂轮磨削GH4169镍基高温合金的性能评价研究 [J]. 航空制造技术,2021,64(4):14-19. doi: 10.16080/j.issn1671-833x.2021.04.014

    LI Benkai, DING Wenfeng, MA Yanyan, et al. Performance evaluation on grinding of nickel-based superalloy GH4169 using new corundum abrasive wheel [J]. Aeronautical Manufacturing Technology,2021,64(4):14-19. doi: 10.16080/j.issn1671-833x.2021.04.014
    [2] KUANG W J, MIAO Q, DING W F, et al. Fretting wear behaviour of machined layer of nickel-based superalloy produced by creep-feed profile grinding [J]. Chinese Journal of Aeronautics,2021,35:401-411. doi: 10.1016/j.cja.2021.10.007
    [3] 韩璐, 康仁科, 张园, 等. GH4169超声辅助磨削表面完整性研究 [J]. 金刚石与磨料磨具工程,2021,41(5):46-51. doi: 10.13394/j.cnki.jgszz.2021.5.0008

    HAN Lu, KANG Renke, ZHANG Yuan, et al. Research on surface integrity of GH4169 machined by ultrasonic assisted grinding [J]. Diamond and Abrasives Engineering,2021,41(5):46-51. doi: 10.13394/j.cnki.jgszz.2021.5.0008
    [4] LI H N, YU T B, ZHU L D, et al. Modeling and simulation of grinding wheel by discrete element method and experimental validation [J]. The International Journal of Advanced Manufacturing Technology,2015,81:1921-1938. doi: 10.1007/s00170-015-7205-0
    [5] LI H N, YU T B, WANG Z X, et al. Detailed modeling of cutting forces in grinding process considering variable stages of grain-workpiece micro interactions [J]. International Journal of Mechanical Sciences,2017,126:319-339. doi: 10.1016/j.ijmecsci.2016.11.016
    [6] LI H N, AXINTE D. On a stochastically grain-discretized model for 2D/3D temperature mapping prediction in grinding [J]. International journal of Machine Tools and Manufacture,2017,116:60-76. doi: 10.1016/j.ijmachtools.2017.01.004
    [7] 赵庆军, 尹胜, 向瑶, 等. 基于ABAQUS切削仿真加工技术应用 [J]. 工具技术,2022,56(2):76-80. doi: 10.3969/j.issn.1000-7008.2022.02.017

    ZHAO Qingjun, YIN Sheng, XIANG Yao, et al. Application of cutting simulation technology based on ABAQUS [J]. Tool Engineering,2022,56(2):76-80. doi: 10.3969/j.issn.1000-7008.2022.02.017
    [8] WEN J, TANG J Y, ZHOU W H. Study on formation mechanism and regularity of residual stress in ultrasonic vibration grinding of high strength alloy steel [J]. Journal of Manufacturing Processes,2021,66:608-622. doi: 10.1016/j.jmapro.2021.04.040
    [9] 闫艳燕, 闫浩哲, 刘俊利, 等. TC4钛合金纵扭超声磨削力热耦合模型及其试验研究 [J]. 中国机械工程,2022,10:1-13. doi: 10.3969/j.issn.1004-132X.2017.01.001

    Yan Yanyan, Yan Hanzhe, Liu Junli. et al. The thermo-mechanical coupling model and experimental research of longitudinal and torsional ultrasonic grinding of TC4 titanium alloy [J]. China Mechanical Engineering,2022,10:1-13. doi: 10.3969/j.issn.1004-132X.2017.01.001
    [10] LEI X F, XIANG D H, PENG P C, et al. Establishment of dynamic grinding force model for ultrasonic-assisted single abrasive high-speed grinding [J]. Journal of Materials Processing Technology,2022,300:117420. doi: 10.1016/j.jmatprotec.2021.117420
    [11] 张银霞, 韩程宇, 杨鑫, 等. GCr15钢平面磨削力仿真分析与实验研究 [J]. 表面技术,2019,48(10):342-348. doi: 10.16490/j.cnki.issn.1001-3660.2019.10.042

    ZHANG Yinxia, HAN Chengyu, YANG Xin, et al. Simulation analysis and experimental research on surface grinding force of GCr15 steel [J]. Surface Technology,2019,48(10):342-348. doi: 10.16490/j.cnki.issn.1001-3660.2019.10.042
    [12] MOSLEH A O, MIKHAYLOVSKAYA A V, KOTOV A D, et al. Experimental, modelling and simulation of an approach for optimizing the superplastic forming of Ti-6% Al-4% V titanium alloy [J]. Journal of Manufacturing Processes,2019,45:262-272. doi: 10.1016/j.jmapro.2019.06.033
    [13] YASMEEN T, SHAO Z T, ZHAO L, et al. Constitutive modeling for the simulation of the superplastic forming of TA15 titanium alloy [J]. International Journal of Mechanical Sciences,2019,164:105178. doi: 10.1016/j.ijmecsci.2019.105178
    [14] 夏江, 丁文锋, 仇博, 等. 镍基高温合金高速超高速磨削成屑过程的三维仿真研究 [J]. 金刚石与磨料磨具工程,2020,40(6):58-69. doi: 10.13394/j.cnki.jgszz.2020.6.0011

    XIA Jiang, DING Wenfeng, QIU Bo, et al. 3D simulation study on the chip formation process in high speed and ultra-high speed grinding of nickel-based superalloy [J]. Diamond and Abrasives Engineering,2020,40(6):58-69. doi: 10.13394/j.cnki.jgszz.2020.6.0011
    [15] 田欣利, 王龙, 刘谦, 等. 20CrMnTi钢齿面磨削力模型构建与分析 [J]. 机械工程学报,2018,54(3):227-232. doi: 10.3901/JME.2018.03.227

    TIAN Xinli, WANG Long, LIU Qian, et al. Construction and analysis of grinding force model of 20CrMnTi steel tooth surface [J]. Journal of Mechanical Engineering,2018,54(3):227-232. doi: 10.3901/JME.2018.03.227
    [16] 段继豪, 牛强, 杨元, 等. TC4钛合金磨削机理和仿真研究 [J]. 计算机仿真,2022,39(1):218-232. doi: 10.3969/j.issn.1006-9348.2022.01.047

    DUAN Jihao, NIU Qiang, YANG Yuan, et al. Study on grinding mechanism and simulation of TC4 titanium alloy [J]. Computer Simulation,2022,39(1):218-232. doi: 10.3969/j.issn.1006-9348.2022.01.047
    [17] LI B K, DAI C W, DING W F, et al. Prediction on grinding force during grinding powder metallurgy nickel-based superalloy FGH96 with electroplated CBN abrasive wheel [J]. Chinese Journal of Aeronautics,2021,34(8):65-74. doi: 10.1016/j.cja.2020.05.002
    [18] MA X F, CAI Z Q, YAO B, et al. Dynamic grinding force model for face gear based on the wheel-gear contact geometry [J]. Journal of Materials Processing Technology,2022,306:117633. doi: 10.1016/j.jmatprotec.2022.117633
    [19] 马志飞, 梁国星, 张昊, 等. 单颗磨粒高速磨削Ti6Al4V仿真与试验验证 [J]. 工具技术,2019,53(4):49-53. doi: 10.3969/j.issn.1000-7008.2019.04.012

    MA Zhifei, LIANG Guoxing, ZHANG Hao, et al. Simulation and experimental investigation of high-speed grinding Ti6Al4V with single grain [J]. Tool Engineering,2019,53(4):49-53. doi: 10.3969/j.issn.1000-7008.2019.04.012
    [20] GRIMMERT A, WIEDERKEHR P. Macroscopic process simulation of surface and profile grinding processes estimating forces for the production of turbine blades [J]. Procedia CIRP,2021,102:126-131. doi: 10.1016/j.procir.2021.09.022
    [21] NOSENKO V A, DANILENKO M V. Mathematical simulation of cutting force during grinding using theory of Markov processes [J]. Materials Today:Proceedings,2021,38:1602-1606. doi: 10.1016/j.matpr.2020.08.163
    [22] GHANDEHARIUN A, HUSSEIN H M, KISHAWY H A. Machining metal matrix composites: novel analytical force model [J]. International Journal of Advanced Manufacturing Technology,2016,83:233-241. doi: 10.1007/s00170-015-7554-8
    [23] 谢黎明, 邹崇磊, 沈浩, 等. 冷作磨具钢Cr12MoV磨削温度场解析模型的建立 [J]. 机械与电子,2010,2:126-131. doi: 10.3969/j.issn.1001-2257.2010.02.023

    XIE Liming, ZOU Chonglei, SHEN Hao, et al. Establishment of analytical model in grinding temperature field of cold steel Cr12MoV [J]. Machinery and electronics,2010,2:126-131. doi: 10.3969/j.issn.1001-2257.2010.02.023
    [24] JIANG J L, GE P Q, SUN S F, et al. From the microscopic interaction mechanism to the grinding temperature field: an integrated modelling on the grinding process [J]. International Journal of Machine Tools and Manufacture,2016,110:27-42. doi: 10.1016/j.ijmachtools.2016.08.004
    [25] JAMSHIDI H, BUDAK E. Grinding temperature modeling based on a time dependent heat source [J]. Procedia CIRP,2018,77:299-302. doi: 10.1016/j.procir.2018.09.020
    [26] YANG S Y, CHEN W F, NONG S, et al. Temperature field modelling in the form grinding of involute gear based on high-order function moving heat source [J]. Journal of Manufacturing Processes,2022,81:1028-1039. doi: 10.1016/j.jmapro.2022.07.014
    [27] CHEN T, MIAO Q, XIONG M Y, et al. On the residual stresses of turbine blade root of γ-TiAl intermetallic alloys induced by non-steady-state creep feed profile grinding [J]. Journal of Manufacturing Processes,2022,82:800-817. doi: 10.1016/j.jmapro.2022.08.051
    [28] 王龙, 汪刘应, 唐修检, 等. 成形法磨削齿轮的磨削温度模型构建与分析 [J]. 机械工程学报,2022,58(3):295-304. doi: 10.3901/JME.2022.03.295

    WANG Long, WANG Liuying, TANG Xiujian, et al. Construction and analysis of grinding temperature model for gear processed by form grinding technology [J]. Journal of Mechanical Engineering,2022,58(3):295-304. doi: 10.3901/JME.2022.03.295
    [29] HANDA D, KUMAR S, SURENDRAN S B, et al. Simulation of intermittent grinding for Ti-6Al-4V with segmented wheel [J]. Materials Today: Proceedings,2021,44:2537-2542. doi: 10.1016/j.matpr.2020.12.626
    [30] NASKAR A, CHOUDHARY A, PAUL S. Wear mechanism in high-speed superabrasive grinding of titanium alloy and its effect on surface integrity [J]. Wear,2020,462:203475.
    [31] 蓝善超, 王宏芳, 李文斌. 磨料粒度对电镀CBN砂轮磨损影响的有限元仿真分析 [J]. 工具技术,2012,46(1):21-24. doi: 10.3969/j.issn.1000-7008.2012.01.006

    LAN Shanchao, WANG Hongfang, LI Wenbin. Finite element simulation and analysis of influence of grain size on wear of electroplated CBN grinding wheel [J]. Tool Engineering,2012,46(1):21-24. doi: 10.3969/j.issn.1000-7008.2012.01.006
    [32] AGNARD S, LIU Z H, HAZEL B. Material removal and wheel wear models for robotic grinding wheel profiling [J]. Procedia Manufacturing,2015,2:35-40. doi: 10.1016/j.promfg.2015.07.007
    [33] AHRENS M, DAMM J, DAGEN M, et al. Estimation of dynamic grinding wheel wear in plunge grinding [J]. Procedia CIRP,2017,58:422-427. doi: 10.1016/j.procir.2017.03.247
    [34] MIAO Q, DING W F, KUANG W J, et al. Tool wear behavior of vitrified microcrystalline alumina wheels in creep feed profile grinding of turbine blade root of single crystal nickel-based superalloy [J]. Tribology International,2020,145:106144. doi: 10.1016/j.triboint.2019.106144
    [35] 周生合, 王军, 吕玉山, 等. 磨粒有序排布的电镀CBN砂轮磨削表面粗糙度仿真 [J]. 工具技术,2015,49(5):98-100. doi: 10.3969/j.issn.1000-7008.2015.05.029

    ZHOU Shenghe, WANG Jun, LV Yushan, et al. Simulation of grinding surface roughness of electroplated CBN grinding wheel with pattern [J]. Tool Engineering,2015,49(5):98-100. doi: 10.3969/j.issn.1000-7008.2015.05.029
    [36] MOHAMMAD R, JOSEPH L Z W. Simulation of workpiece surface roughness after flat grinding by electroplated wheel [J]. Procedia CIRP,2018,77:303-306. doi: 10.1016/j.procir.2018.09.021
    [37] 肖军民, 谢晋. 20CrMnTi高速外圆磨削试验研究及参数优化 [J]. 机床与液压,2015,43(11):56-58. doi: 10.3969/j.issn.1001-3881.2015.11.016

    XIAO Junmin, XIE Jin. Experimental research and parameters optimization of high-speed cylindrical grinding for 20CrMnTi [J]. Machine Tool and Hydraulics,2015,43(11):56-58. doi: 10.3969/j.issn.1001-3881.2015.11.016
    [38] WANG Y Z, LIU Y, CHU X M, et al. Calculation model for surface roughness of face gears by disc wheel grinding [J]. International Journal of Machine Tools and Manufacture,2017,123:76-88. doi: 10.1016/j.ijmachtools.2017.08.002
    [39] LING H, YANG C M, FENG S C, et al. Predictive model of grinding residual stress for linear guideway considering straightening history [J]. International Journal of Mechanical Sciences,2020,176:105536. doi: 10.1016/j.ijmecsci.2020.105536
    [40] WANG Y Z, ZHANG W, LIU Y. Analysis model for surface residual stress distribution of spiral bevel gear by generating grinding [J]. Mechanism and Machine Theory,2018,130:477-490. doi: 10.1016/j.mechmachtheory.2018.08.027
    [41] KUANG W J, MIAO Q, DING W F, et al. Residual stresses of turbine blade root produced by creep-feed profile grinding: three-dimensional simulation based on workpiece-grain interaction and experimental verification [J]. Journal of Manufacturing Processes,2021,62:67-79. doi: 10.1016/j.jmapro.2020.11.045
    [42] 雷瑛, 李达, 罗森怡. 磨削加工件表面残余应力测试及其线性回归预测分析 [J]. 工具技术,2021,55(10):19-23. doi: 10.3969/j.issn.1000-7008.2021.10.004

    LEI Ying, LI Da, LUO Senyi. Surface residual stress measurement and linear regression prediction analysis of grinding workpiece [J]. Tool Engineering,2021,55(10):19-23. doi: 10.3969/j.issn.1000-7008.2021.10.004
    [43] ZHOU W H, TANG J Y, SHAO W. Study on surface generation mechanism and roughness distribution in gear profile grinding [J]. International Journal of Mechanical Sciences,2020,187:105921. doi: 10.1016/j.ijmecsci.2020.105921
    [44] 巩亚东, 苏志朋, 孙瑶, 等. 镍基单晶高温合金微磨削形貌仿真及实验研究 [J]. 东北大学学报(自然科学版),2020,41(7):949-954. doi: 10.12068/j.issn.1005-3026.2020.07.007

    GENG Yadong, SU Zhipeng, SUN Yao, et al. Morphology simulation and experimental study on micro-grinding of nickel-based single crystal superalloy [J]. Journal of Northeastern University (Natural Science),2020,41(7):949-954. doi: 10.12068/j.issn.1005-3026.2020.07.007
    [45] XIAO Y L, WANG S L, MA C, et al. Numerical modeling of material removal mechanism and surface topography for gear profile grinding [J]. Journal of Manufacturing Processes,2022,76:719-739. doi: 10.1016/j.jmapro.2022.02.052
    [46] 杨磊, 李郝林, 迟玉伦. 基于自适应模糊神经网络的砂轮磨损评估 [J]. 轻工机械,2020,38(6):72-76.

    YANG Lei, LI Haolin, CHI Yulun. Wear evaluation of grinding wheel based on adaptive fuzzy neural network [J]. Light Industry Machinery,2020,38(6):72-76.
    [47] 苏史博, 毕果, 郑守红, 等. 基于LSTM和声发射的金刚石砂轮磨损状态识别 [J]. 组合机床与自动化加工技术,2021,8:169-172.

    SU Shibo, BI Guo, ZHENG Shouhong, et al. Identification of wear status of diamond grinding wheel based on LSTM and acoustic emission [J]. Modular Machine Tool and Automatic Manufacturing Technique,2021,8:169-172.
    [48] 张铁, 胡广, 陈首彦. 基于模糊自整定PID的力控制磨削实验研究 [J]. 现代制造工程,2016,9:121-125. doi: 10.3969/j.issn.1005-2895.2020.06.014

    ZHANG Tie, HU Guang, CHEN Shouyan. Experiment research on force control grinding based on fuzzy-PID [J]. Modern Manufacturing Engineering,2016,9:121-125. doi: 10.3969/j.issn.1005-2895.2020.06.014
    [49] LI D W, YANG J X, ZHAO H, et al. Contact force plan and control of robotic grinding towards ensuring contour accuracy of curved surfaces [J]. International Journal of Mechanical Sciences,2022,227:107449. doi: 10.1016/j.ijmecsci.2022.107449
    [50] 王雨, 孙龙, 王玉伟, 等. 风电叶片打磨机器人柔性末端磨削力抗扰控制 [J]. 计算机仿真,2020,37(7):384-390. doi: 10.3969/j.issn.1006-9348.2020.07.076

    WANG Yu, SUN Long, WANG Yuwei, et al. Auto disturbance rejection control of the flexible end grinding force of a wind turbine blade grinding robot [J]. Computer Simulation,2020,37(7):384-390. doi: 10.3969/j.issn.1006-9348.2020.07.076
    [51] MITROFANOV A, PARSHEVA K, NOSENKO V. Simulation of an artificial neural network for predicting temperature and cutting force during grinding using CAMQL [J]. Materials Today:Proceedings,2021,38:1508-1511. doi: 10.1016/j.matpr.2020.08.139
    [52] HEININEN A, PROD’HON R, MOKHTARIAN H, et al. Finite element modelling of temperature in cylindrical grinding for future integration in a digital twin [J]. Procedia CIRP,2021,104:875-880. doi: 10.1016/j.procir.2021.11.147
    [53] 路建萍, 董涛, 侯丽雅. 基于神经网络的磨削温度在线监测预报系统 [J]. 南京理工大学学报,2000,24(3):273-276. doi: 10.14177/j.cnki.32-1397n.2000.03.019

    LU Jianping, DONG Tao, HOU Liya. On-line monitoring and forecasting system for grinding arc temperature based on artificial neural network [J]. Journal of Nanjing University of Science and Technology,2000,24(3):273-276. doi: 10.14177/j.cnki.32-1397n.2000.03.019
    [54] PENG R T, TONG J W, TANG X Z, et al. Application of a pressurized internal cooling method in grinding Inconel 718: Modeling-simulation and testing-validation [J]. International Journal of Mechanical Sciences,2021,189:105985. doi: 10.1016/j.ijmecsci.2020.105985
    [55] SHARMIN I, MOON M, TALUKDER S, et al. Impact of nozzle design on grinding temperature of hardened steel under MQL condition [J]. Materials Today:Proceedings,2021,38:3232-3237. doi: 10.1016/j.matpr.2020.09.717
    [56] XU X P, YU Y Q, HUANG H. Mechanisms of abrasive wear in the grinding of titanium (TC4) and nickel (K417) alloys [J]. Wear,2003,255:1421-1426. doi: 10.1016/S0043-1648(03)00163-7
    [57] CARAGUAY S J, BOARON A, WEINGAERTNER W L, et al. Wear assessment of microcrystalline and electrofused aluminum oxide grinding wheels by multi-sensor monitoring technique [J]. Journal of Manufacturing Processes,2022,80:141-151. doi: 10.1016/j.jmapro.2022.05.052
    [58] 郭维诚, 李蓓智, 杨建国, 等. 磨削过程信号监测与砂轮磨损预测模型构建 [J]. 上海交通大学学报,2019,53(12):1475-1481. doi: 10.16183/j.cnki.jsjtu.2019.12.010

    GUO Weicheng, LI Beizhi, YANG Jianguo, et al. Monitoring of grinding signals and development of wheel wear prediction model [J]. Journal of Shanghai Jiao Tong University,2019,53(12):1475-1481. doi: 10.16183/j.cnki.jsjtu.2019.12.010
    [59] 丁宁, 段景淞, 石建, 等. 基于声发射砂轮磨损监测系统的研究 [J]. 南京航空航天大学学报,2020,52(1):48-52. doi: 10.16356/j.1005-2615.2020.01.005

    DING Ning, DUAN Jingsong, SHI Jian, et al. Research on grinding wheel wear monitoring system based on acoustic emission [J]. Journal of Nanjing University of Aeronautics and Astronautics,2020,52(1):48-52. doi: 10.16356/j.1005-2615.2020.01.005
    [60] XU L M, NIU M, ZHAO D, et al. Methodology for the immediate detection and treatment of wheel wear in contour grinding [J]. Precision Engineering,2019,60:405-412. doi: 10.1016/j.precisioneng.2019.09.006
    [61] NGUYEN D T, YIN S H, TANG Q C, et al. Online monitoring of surface roughness and grinding wheel wear when grinding Ti-6Al-4V titanium alloy using ANFIS-GPR hybrid algorithm and Taguchi analysis [J]. Precision Engineering,2019,55:275-292. doi: 10.1016/j.precisioneng.2018.09.018
    [62] 尹国强, 王东, 关云匀, 等. 基于声发射监测的砂轮磨损实验 [J]. 东北大学学报(自然科学版),2022,43(8):1127-1133. doi: 10.12068/j.issn.1005-3026.2022.08.009

    YIN Guoqiang, WANG Dong, GUAN Yunyun, et al. Grinding wheel wear experiment based on acoustic emission [J]. Journal of Northeastern University (Natural Science),2022,43(8):1127-1133. doi: 10.12068/j.issn.1005-3026.2022.08.009
    [63] MAHATA S, SHAKYA P, BABU N R. A robust condition monitoring methodology for grinding wheel wear identification using Hilbert Huang transform [J]. Precision Engineering,2021,70:77-91. doi: 10.1016/j.precisioneng.2021.01.009
    [64] 陈廉清, 郭建亮, 杨勋, 等. 基于进化神经网络的磨削粗糙度预测模型 [J]. 计算机集成制造系统,2013,19(11):2854-2863. doi: 10.13196/j.cims.2013.11.chenlianqing.2854.10.20131123

    CHEN Lianqing, GUO Jianliang, YANG Xun, et al. Grinding roughness prediction model based on evolutionary artificial neural network [J]. Computer Integrated Manufacturing Systems,2013,19(11):2854-2863. doi: 10.13196/j.cims.2013.11.chenlianqing.2854.10.20131123
    [65] LI Y, LIU Y H, TIAN Y B, et al. Application of improved fireworks algorithm in grinding surface roughness online monitoring [J]. Journal of Manufacturing Processes,2022,74:400-412. doi: 10.1016/j.jmapro.2021.12.046
    [66] GOPAN V, WINS K L D, SURENDRAN A. Integrated ANN-GA approach for predictive modeling and optimization of grinding parameters with surface roughness as the response [J]. Materials today:proceedings,2018,5(5):12133-12141. doi: 10.1016/j.matpr.2018.02.191
    [67] ALI Y M, ZHANG L C. Estimation of residual stresses induced by grinding using a fuzzy logic approach [J]. Journal of Materials Processing Technology,1997,63(1-3):875-880. doi: 10.1016/S0924-0136(96)02742-2
    [68] WANG P Z, HE Z S, ZHANG Y S, et al. Control of grinding surface residual stress of Inconel 718 [J]. Procedia Engineering,2017,174:504-511. doi: 10.1016/j.proeng.2017.01.174
    [69] 李峰, 李学崑, 融亦鸣. 强化感应加热辅助磨削Inconel718的残余应力主动调控 [J]. 机械工程学报,2018,3:216-226. doi: 10.3901/JME.2018.03.216

    LI Feng, LI Xuekun, RONG Yiming. Active control of the residual stress in Inconel718 grinding assisted by the strengthen induction heating [J]. Journal of Mechanical Engineering,2018,3:216-226. doi: 10.3901/JME.2018.03.216
  • 加载中
图(18)
计量
  • 文章访问数:  938
  • HTML全文浏览量:  245
  • PDF下载量:  142
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-02-24
  • 修回日期:  2023-03-05
  • 录用日期:  2023-03-08
  • 刊出日期:  2023-04-20

目录

    /

    返回文章
    返回