SDU-QIT立铣刀磨损试验数据集

doi:10.3901/JME.2022.09.166

机械工程学报 ›› 2022, Vol. 58 ›› Issue (9): 166-171.doi: 10.3901/JME.2022.09.166

扫码分享

SDU-QIT立铣刀磨损试验数据集

信苗苗¹, 曹凤¹, 江铭炎^2,3, 厉相宝¹, 李东阳^2,3, 张明强^2,3, 雷腾飞¹, 袁东风^2,3

1. 齐鲁理工学院机电工程学院济南 250200;
2. 山东大学信息科学与工程学院青岛 266237;
3. 山东大学先进信息技术研究院济南 250100

收稿日期:2021-06-19 修回日期:2022-01-10 出版日期:2022-05-05 发布日期:2022-06-23
通讯作者: 袁东风(通信作者)，男，1958年出生，博士，教授，博士研究生导师。主要研究方向为5G通信，新一代信息技术，智能制造等。E-mail：dfyuan@sdu.edu.cn E-mail:dfyuan@sdu.edu.cn
作者简介:信苗苗，1989年出生，讲师。主要研究方向为智能制造。E-mail：xingong102@126.com
基金资助:
山东省重大科技创新工程(2019JZZY010111)和山东省重点研发计划(2019GGX104092)资助项目

SDU-QIT End Milling Cutter Accelerated Life Test Datasets

XIN Miaomiao¹, CAO Feng¹, JIANG Mingyan^2,3, LI Xiangbao¹, LI Dongyang^2,3, ZHANG Mingqiang^2,3, LEI Tengfei¹, YUAN Dongfeng^2,3

1. School of Mechanic and Electronic Engineering, Qilu Institute of Technology, Jinan 250200;
2. School of Information Science and Engineering, Shandong University, Qingdao 266237;
3. Institute of Advanced Information Technology, Shandong University, Jinan 250100

Received:2021-06-19 Revised:2022-01-10 Online:2022-05-05 Published:2022-06-23

摘要/Abstract

摘要： 刀具故障预测与健康管理(Prognostic and health management, PHM)是机床制造领域的关键问题。作为数控机床的“牙齿”，刀具的健康状态直接影响着机床加工效率和产品质量。借助大数据与人工智能(AI)技术实现对刀具运行状态的实时精准监测，目前已成为学术界和工业界关注的热点问题。然而，刀具高质量全寿命周期数据的匮乏，严重制约了机械装备PHM技术的理论研究与工程应用。为解决上述问题，开展了面向刀具全寿命周期的数控加工中心立铣刀多工况试验与数据采集工作，并将获取的试验数据集面向全球学者公开发布。该数据集共包含2种工况下的立铣刀全寿命周期振动信号，且明确标注了刀具主后刀面最大VB值、主后刀面1/2a_p(背吃刀量)处VB值、主后刀面S_VB值，副后刀面最大VB值、副后刀面S_VB值等5种标签，可为PHM领域基于AI的刀具故障诊断与预测性维护研究提供数据支撑。

关键词: 数据集开源, 刀具全寿命周期, 故障预测与健康管理

Abstract: The Prognostic Health Monitoring (PHM) of milling cutters is key issue in the field of machine tool manufacturing. As the "teeth" of computer numerical control (CNC) machine tools, its health status directly affects the machining efficiency and the quality of products. It has become a hot topic in both academic research and industries, with the help of big data and deep learning technology to realize high reliable tool fault diagnosis and predictive maintenance. However, the lack of high-quality yet life-cycle data has become the bottleneck restricting academic research and engineering application. To solve this problem, the end milling cutter life test in CNC machining center are carried out, and the obtained test data set are published for scholars all over the world. This data set contains the vibration signals of the end milling cutter in the whole life cycle under two working conditions, and clearly marks five groups of data, such as the maximum wear width at the main back of the cutter, wear area S_VB and wear width VB at 1/2a_p (back feed), and the maximum wear width and wear area S_VB at the auxiliary back, which provides data support for the research in PHM field.

Key words: open source of data set, tool life cycle, prognostic and health management

中图分类号:

TG714

信苗苗, 曹凤, 江铭炎, 厉相宝, 李东阳, 张明强, 雷腾飞, 袁东风. SDU-QIT立铣刀磨损试验数据集[J]. 机械工程学报, 2022, 58(9): 166-171.

XIN Miaomiao, CAO Feng, JIANG Mingyan, LI Xiangbao, LI Dongyang, ZHANG Mingqiang, LEI Tengfei, YUAN Dongfeng. SDU-QIT End Milling Cutter Accelerated Life Test Datasets[J]. Journal of Mechanical Engineering, 2022, 58(9): 166-171.

参考文献

[1] ISO 8688-2: 1989. Tool life testing in milling-Part 2: End milling[S]. London: IX-ISO, 1989.
[2] General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China, Standardization Administration of the People's Republic of China. 16460-2016 End Milling Cutter Life Test [S]. Beijing: Standards Press of China, 2010. 中华人民共和国国家质量监督检验检疫总局, 中国国家标准化管理委员会. GB/T 16460—2016立铣刀寿命试验[S]. 北京: 中国标准出版社, 2010.
[3] LI Xiwen, ZHANG Jie, DU Runsheng, et al. Research on wear land on major flank of end mills with Small-diameter[J]. Tool Engineering, 2000(6): 7-10. 李锡文, 张杰, 杜润生, 等. 小直径立铣刀后刀面磨损带的研究[J]. 工具技术, 2000(6): 7-10.
[4] KIOUS M, OUAHABI A, BOUDRAA M, et al. Detection process approach of tool wear in high speed milling[J]. Measurement, 2010, 43(10): 1439-1446.
[5] ZHU Kunpeng, YU Xiaolong. The monitoring of micro milling tool wear conditions by wear area estimation[J]. Mechanical Systems and Signal Processing, 2017, 93(9): 80-91.
[6] SZYDOWSKI M, POWAKA B, MATUSZAK M, et al. Machine vision micro-milling tool wear inspection by image reconstruction and light reflectance[J]. Precision Engineering, 2016, 44(4): 236-244.
[7] KONG Dongdong, CHEN Yongjie, LI Ning, et al. Tool wear monitoring based on kernel principal component analysis and v-support vector regression[J]. The International Journal of Advanced Manufacturing Technology, 2017, 89(1/2/3/4): 175-190.
[8] TAO Xin, ZHU Kunpeng, GAO Siyu. Tool wear online monitoring of high-speed milling based on morphological component analysis[J]. Journal of University and Technology of China, 2017, 47(8): 699-707. 陶欣, 朱锟鹏, 高思煜. 基于形态分量分析的高速铣工刀具磨损在线监测[J]. 中国科学技术大学学报, 2017, 47(8): 699-707.
[9] CAI Ligang, LI Haibo, YANG Congbin, et al. Tool wear state recognition model based on modified variational mode decomposition and LS-SVM with the adaptive backtracking search algorithm[J]. Journal of Beijing University of Technology, 2021, 47(1): 10-22. 蔡力钢, 李海波, 杨聪彬, 等. 基于改进VMD和自适应BSA优化LS-SVM的刀具磨损状态监测方法[J]. 北京工业大学学报, 2021, 47(1): 10-22.
[10] DROUILLET C, KARANDIKAR J, NATH C, et al. Tool life predictions in milling using spindle power with the network technique[J]. Journal of Manufacturing Processes, 2016, 22: 161-168.
[11] NOURI M, FUSSELL B K, ZINITI B L, et al. Real-time tool wear monitoring in milling using a cutting condition independent method[J]. International Journal of Machine Tools and Manufacture, 2015, 89: 1-13.
[12] LEI Yaguo, HAN Tianyu, WANG Biao, et al. XJTU-SY rolling element bearing accelerated life test tutorial[J]. Journal of Mechanical Engineering, 2019, 55(16): 1-6. 雷亚国, 韩天宇, 王彪, 等. XJTU-SY滚动轴承加速寿命试验数据集解读[J]. 机械工程学报, 2019, 55(16): 1-6.

SDU-QIT立铣刀磨损试验数据集

SDU-QIT End Milling Cutter Accelerated Life Test Datasets

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 10

编辑推荐

Metrics

本文评价

[1]	赵国龙, 夏宏军, 李亮, 信连甲, 何宁. 激光诱导可控氧化辅助微细铣削TiAl金属间化合物的研究[J]. 机械工程学报, 2021, 57(9): 254-263.
[2]	刘献礼, 范梦超, 计伟, 王广越, 赵泽民. 椭球头铣刀设计及其刀具路径生成算法[J]. 机械工程学报, 2018, 54(15): 199-212.
[3]	姜彬, 张明慧, 姚贵生, 白锦轩. 高速铣刀安全性跨尺度设计方法[J]. 机械工程学报, 2016, 52(5): 202-206.
[4]	李国超, 孙杰. 整体式立铣刀刃磨仿真技术研究现状与发展趋势[J]. 机械工程学报, 2015, 51(9): 165-175.
[5]	刘献礼, 姜志鹏, 李茂月, 丁云鹏, 王广越, 刘立佳. 基于残留高度设计的模具加工用新型圆角端铣刀[J]. 机械工程学报, 2015, 51(5): 192-204.
[6]	刘显波;龙新华;孟光;郭志明. 基于频域多目标优化的铣削力系数及偏心参数识别[J]. , 2011, 47(7): 185-190.
[7]	李广慧;刘广军;谭光宇;李振加;融亦鸣. 基于遗传算法的铣刀片三维复杂槽型重构技术[J]. , 2005, 41(6): 113-117.
[8]	谭光宇;刘广军;李振加;刘美娟;李广慧;融亦鸣. 复杂槽型铣刀片三维温度场分析及其模糊综合评价[J]. , 2004, 40(3): 106-110.
[9]	汤爱民;周志雄;曾滔. 不等齿距三刃高速立铣刀的动平衡及设计[J]. , 2011, 47(13): 180-185.
[10]	张大卫;冯志勇;黄田;曾子平. 圆锥螺旋铣刀非线性铣削力模型[J]. , 1997, 33(6): 14-19.