Online monitoring model of micro-milling force incorporating tool wear prediction process

被引:20
作者
Ding, Pengfei [1 ]
Huang, Xianzhen [1 ,2 ]
Zhao, Chengying [1 ]
Liu, Huizhen [1 ]
Zhang, Xuewei [1 ]
机构
[1] Northeastern Univ, Sch Mech Engn & Automa, Shenyang 110819, Peoples R China
[2] Northeastern Univ, Key Lab Vibrat & Control Aeroprop Syst, Minist Educ China, Shenyang 110819, Peoples R China
基金
中国国家自然科学基金;
关键词
Micro-milling; Mechanical modeling; Tool wear; Neural network; Parameter optimization; DEFORMATION;
D O I
10.1016/j.eswa.2023.119886
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In modern manufacturing, micro-milling technology plays an essential role in manufacturing high-precision and complex micro-size parts. Exploring the changing rule of time-varying cutting is of great significance for un-derstanding the micro-milling mechanism and improving the machining efficiency. In addition, tool wear identification and updating in advance can enhance the accuracy and sustainability of micromachining. This paper presents a tool wear prediction framework for micro-milling by a temporal convolution network, bi-directional long short-term memory, and the multi-objective arithmetic optimization algorithm. Then, a new integrated model for real-time micro-milling cutting force monitoring is constructed, considering tool defor-mation, tool runout, time-varying cutting coefficient, chip separation state, and tool wear estimation results. Based on the micro-milling experiment with workpiece material Al6061, the accuracy of the proposed tool wear prediction and cutting force model is verified. The developed model can provide theoretical guidance for statics and dynamics analysis in the micro-milling.
引用
收藏
页数:20
相关论文
共 65 条
[1]   The Arithmetic Optimization Algorithm [J].
Abualigah, Laith ;
Diabat, Ali ;
Mirjalili, Seyedali ;
Elaziz, Mohamed Abd ;
Gandomi, Amir H. .
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2021, 376
[2]   A data-driven model for milling tool remaining useful life prediction with convolutional and stacked LSTM network [J].
An, Qinglong ;
Tao, Zhengrui ;
Xu, Xingwei ;
El Mansori, Mohamed ;
Chen, Ming .
MEASUREMENT, 2020, 154
[3]   Using artificial neural networks for the prediction of dimensional error on inclined surfaces manufactured by ball-end milling [J].
Arnaiz-Gonzalez, Alvar ;
Fernandez-Valdivielso, Asier ;
Bustillo, Andres ;
Norberto Lpez de Lacalle, Luis .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2016, 83 (5-8) :847-859
[4]   Investigation of micro-cutting operations [J].
Chae, J ;
Park, SS ;
Freiheit, T .
INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2006, 46 (3-4) :313-332
[5]   Advances in micro milling: From tool fabrication to process outcomes [J].
Chen, Ni ;
Li, Hao Nan ;
Wu, Jinming ;
Li, Zhenjun ;
Li, Liang ;
Liu, Gongyu ;
He, Ning .
INTERNATIONAL JOURNAL OF MACHINE TOOLS & MANUFACTURE, 2021, 160
[6]   Intelligent tool wear monitoring and multi-step prediction based on deep learning model [J].
Cheng, Minghui ;
Jiao, Li ;
Yan, Pei ;
Jiang, Hongsen ;
Wang, Ruibin ;
Qiu, Tianyang ;
Wang, Xibin .
JOURNAL OF MANUFACTURING SYSTEMS, 2022, 62 :286-300
[7]   A new method based on a WOA-optimized support vector machine to predict the tool wear [J].
Cheng, Yaonan ;
Gai, Xiaoyu ;
Jin, Yingbo ;
Guan, Rui ;
Lu, Mengda ;
Ding, Ya .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2022, 121 (9-10) :6439-6452
[8]   Mechanistic model and probability characteristics of micro-milling force with a new parameter identification method [J].
Ding, Pengfei ;
Huang, Xianzhen ;
Miao, Xinglin ;
Zhang, Xuewei ;
Li, YuXiong ;
Wang, Changli .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE, 2024, 238 (1-2) :199-213
[9]   Reliability optimization of micro-milling cutting parameters using slime mould sequence algorithm [J].
Ding, Pengfei ;
Huang, Xianzhen ;
Zhang, Xuewei ;
Li, Yuxiong ;
Wang, Changli .
SIMULATION MODELLING PRACTICE AND THEORY, 2022, 119
[10]   Multi-frequency-band deep CNN model for tool wear prediction [J].
Duan, Jian ;
Duan, Jie ;
Zhou, Hongdi ;
Zhan, Xiaobin ;
Li, Tianxiang ;
Shi, Tielin .
MEASUREMENT SCIENCE AND TECHNOLOGY, 2021, 32 (06)