Dynamic evaluation method for milling tool wear state using multi-source data hybrid drive

doi:10.3785/j.issn.1008-973X.2025.10.006

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (10): 2056-2066 DOI: 10.3785/j.issn.1008-973X.2025.10.006

Dynamic evaluation method for milling tool wear state using multi-source data hybrid drive

Jianwei MAO(

),Di ZHOU,Xiao ZHUANG,Weifang SUN*(

)

College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China

Download:

HTML

PDF(4007KB) HTML
Export: BibTeX | EndNote (RIS)

Abstract

In order to address the problem that dynamic environmental changes affect the evaluation of cutting tools’ service state during milling, a tool wear evaluation method integrating direct and indirect measurements was proposed. Periodic tool end images were fused with real-time sound data, and the high precision of direct measurement and continuity of indirect measurement were combined to realize robust evaluation of the tool wear state during machining. To improve the evaluation effect, a multi-scale network model was constructed. Wear features at different scales were captured, and multi-source information was deeply fused to enhance the model’s feature extraction capability. Results of milling experiments under different machining parameters show that the proposed method can effectively track feature changes in key features and achieves higher accuracy than comparative models (SPPNet, ASPP, U-Net). In four groups of milling experiments, the proposed method exhibited mean values of 0.05124 for mean absolute error, 0.06273 for root mean square error, and 0.94508 for coefficient of determination. The method showed low evaluation error for the tool service condition and strong dynamic tracking capability.

Key words： tool image acoustic signal tool wear dynamic evaluation milling

Received: 28 September 2024 Published: 27 October 2025

CLC:

TH 165

Fund: 国家自然科学基金资助项目（52205122）.

Corresponding Authors: Weifang SUN E-mail: 1171367413@qq.com;swf@wzu.edu.cn

	Service
	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Jianwei MAO
	Di ZHOU
	Xiao ZHUANG
	Weifang SUN

Cite this article:

Jianwei MAO,Di ZHOU,Xiao ZHUANG,Weifang SUN. Dynamic evaluation method for milling tool wear state using multi-source data hybrid drive. Journal of ZheJiang University (Engineering Science), 2025, 59(10): 2056-2066.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.10.006 OR https://www.zjujournals.com/eng/Y2025/V59/I10/2056

多源数据混合驱动的铣削刀具磨损状态动态评价方法

动态环境变化会影响铣削过程中加工刀具服役状态的评估，为此提出融合直接与间接测量的刀具磨损评价方法. 将周期性刀具端部图像与实时声音数据融合，结合直接测量的高精度与间接测量的连续性特点，实现所提方法对加工过程中刀具磨损状态的鲁棒评价. 为了提升评价效果，构建多尺度网络模型，通过在不同尺度上捕获磨损特征并深度融合多源信息来增强模型的特征提取能力. 不同加工参数下的铣削实验结果表明，较之对比模型（SPPNet、ASPP、U-Net），所提方法能够有效追踪关键特征变化且评估准确性高. 在4组铣削实验中，所提方法的平均绝对误差均值为0.05124、均方根误差均值为0.06273、决定系数均值为0.94508；该方法的刀具服役状态评价误差低，动态追踪能力强.

关键词： 刀具图像, 声音信号, 刀具磨损, 动态评价, 铣削

Fig.1 Network structure of multi-scale deep integration model

Fig.2 Network structure of receptive field block

Fig.3 Convolutional block attention module

Fig.4 Network structure of improved receptive field-convolutional block attention module

Fig.5 Vertical machining center

Fig.6 Experimental measurement apparatus

Fig.7 Schematic diagram of milling path

Tab.1 Milling parameter settings

Fig.8 Schematic diagram of tool wear measurement

Fig.9 Test results of proposed tool wear state evaluation model in four milling experiments

Fig.10 Acoustic signal images of tool under different signal-to-noise ratios

Fig.11 Markov transition field transformed images of acoustic signals

Fig.12 Test results of proposed tool wear state evaluation model in four milling experiments (under different signal-to-noise ratios)

Fig.13 Test results of proposed tool wear state evaluation model in four milling experiments (under different inputs)

Tab.2 Evaluation metrics of proposed tool wear state evaluation model in four milling experiments (under different inputs)

Fig.14 Test results of different evaluation models in four milling experiments

Tab.3 Evaluation metrics of different evaluation models in four milling experiments

Fig.15 Acoustic signal images and images transformed by three signal conversion methods

Fig.16 Test results of models composed of different signal conversion methods in four milling experiments

Tab.4 Evaluation metrics of models composed of different signal conversion methods in four milling experiments


[1]	王秋莲, 欧桂雄, 徐雪娇, 等基于VMD-SSA-LSTM考虑刀具磨损的数控铣床切削功率预测模型研究[J]. 中国机械工程, 2024, 35 (6): 1052- 1063 WANG Qiulian, OU Guixiong, XU Xuejiao, et al Research on CNC milling machine cutting power prediction model considering tool wear based on VMD-SSA-LSTM[J]. China Mechanical Engineering, 2024, 35 (6): 1052- 1063

[2]	PIMENOV D Y, BUSTILLO A, WOJCIECHOWSKI S, et al Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review[J]. Journal of Intelligent Manufacturing, 2023, 34 (5): 2079- 2121 doi: 10.1007/s10845-022-01923-2

[3]	刘献礼, 秦怡源, 岳彩旭, 等递归特征消除与极端随机树在铣刀磨损监测中的研究[J]. 机械科学与技术, 2023, 42 (6): 821- 828 LIU Xianli, QIN Yiyuan, YUE Caixu, et al Application research on recursive feature elimination and extra trees in milling cutter wear monitoring[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42 (6): 821- 828

[4]	程训, 余建波基于机器视觉的加工刀具磨损监测方法[J]. 浙江大学学报: 工学版, 2021, 55 (5): 896- 904 CHENG Xun, YU Jianbo Monitoring method for machining tool wear based on machine vision[J]. Journal of Zhejiang University: Engineering Science, 2021, 55 (5): 896- 904

[5]	LI S, LING Z, ZHU K Image super resolution by double dictionary learning and its application to tool wear monitoring in micro milling[J]. Mechanical Systems and Signal Processing, 2024, 206: 110917 doi: 10.1016/j.ymssp.2023.110917

[6]	YU J, CHENG X, LU L, et al A machine vision method for measurement of machining tool wear[J]. Measurement, 2021, 182: 109683 doi: 10.1016/j.measurement.2021.109683

[7]	ZHU K, GUO H, LI S, et al Online tool wear monitoring by super-resolution based machine vision[J]. Computers in Industry, 2023, 144: 103782 doi: 10.1016/j.compind.2022.103782

[8]	LI W, FU H, HAN Z, et al Intelligent tool wear prediction based on Informer encoder and stacked bidirectional gated recurrent unit[J]. Robotics and Computer-Integrated Manufacturing, 2022, 77: 102368 doi: 10.1016/j.rcim.2022.102368

[9]	HU K, JIN J, ZHENG F, et al Overview of behavior recognition based on deep learning[J]. Artificial Intelligence Review, 2023, 56 (3): 1833- 1865 doi: 10.1007/s10462-022-10210-8

[10]	刘会永, 张松, 李剑峰, 等采用改进CNN-BiLSTM模型的刀具磨损状态监测[J]. 中国机械工程, 2022, 33 (16): 1940- 1947 LIU Huiyong, ZHANG Song, LI Jianfeng, et al Tool wear detection based on improved CNN-BiLSTM model[J]. China Mechanical Engineering, 2022, 33 (16): 1940- 1947

[11]	WANG M, YANG L, ZHAO Z, et al Intelligent prediction of wear location and mechanism using image identification based on improved Faster R-CNN model[J]. Tribology International, 2022, 169: 107466 doi: 10.1016/j.triboint.2022.107466

[12]	ZHANG X, YU T, XU P, et al In-process stochastic tool wear identification and its application to the improved cutting force modeling of micro milling[J]. Mechanical Systems and Signal Processing, 2022, 164: 108233 doi: 10.1016/j.ymssp.2021.108233

[13]	史丽晨, 史炜椿, 王海涛, 等基于DRSN-BiLSTM模型的刀具磨损预测方法研究[J]. 机械工程学报, 2024, 60 (24): 66- 74 SHI Lichen, SHI Weichun, WANG Haitao, et al Tool wear prediction based on DRSN-BiLSTM model[J]. Journal of Mechanical Engineering, 2024, 60 (24): 66- 74

[14]	滕瑞, 黄海松, 杨凯, 等基于图像编码技术和卷积神经网络的刀具磨损值在线监测方法[J]. 计算机集成制造系统, 2022, 28 (4): 1042- 1051 TENG Rui, HUANG Haisong, YANG Kai, et al On-line monitoring method for tool wear based on image coding technology and convolutional neural network[J]. Computer Integrated Manufacturing Systems, 2022, 28 (4): 1042- 1051

[15]	朱锟鹏, 黄称意, 李俊刀具磨损状态的多步向前智能预测[J]. 计算机集成制造系统, 2024, 30 (9): 3038- 3049 ZHU Kunpeng, HUANG Chengyi, LI Jun Multi-step forward intelligent prediction of tool wear condition[J]. Computer Integrated Manufacturing Systems, 2024, 30 (9): 3038- 3049

[16]	李恒帅, 刘献礼, 岳彩旭, 等刀具磨损自动识别及检测系统[J]. 计算机应用, 2021, 41 (Suppl.1): 259- 263 LI Hengshuai, LIU Xianli, YUE Caixu, et al Automatic recognition and detection system for cutter wear[J]. Journal of Computer Applications, 2021, 41 (Suppl.1): 259- 263

[1]	Chao LIU,Zunpeng HUANG,Shaofu HUANG. Surface roughness modeling of thread workpieces in whirlwind milling considering material deformation[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(4): 761-771.

[2]	Tao LV,Aibing YU,Xuefeng XU,Minhai MA,Conglin ZHAO. Penetration and deposition characteristics and machining performance of magnetic field assisted nanofluid spray lubrication[J]. Journal of ZheJiang University (Engineering Science), 2024, 58(11): 2320-2329.

[3]	Qi-lin DENG,Juan LU,Yong-hui CHEN,Jian FENG,Xiao-ping LIAO,Jun-yan MA. Optimization method of CNC milling parameters based on deep reinforcement learning[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(11): 2145-2155.

[4]	Xun CHENG,Jian-bo YU. Monitoring method for machining tool wear based on machine vision[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(5): 896-904.

[5]	Hai-jin WANG,Zong-yu YIN,Zhen-zheng KE,Ying-jie GUO,Hui-yue DONG. Wear monitoring of helical milling tool based on one-dimensional convolutional neural network[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(5): 931-939.

[6]	Mao-xia RAN,Qin-yuan HUANG,Xin LIU,Hong SONG,Hao WU. Internal defect detection of arc magnets based on optimized variational mode decomposition[J]. Journal of ZheJiang University (Engineering Science), 2020, 54(11): 2158-2168.

[7]	DONG Hui yue, ZHU Ling sheng, ZHANG Ming, LI Shao bo, LUO Shui jun. Orbital milling method of aircraft skins trimming[J]. Journal of ZheJiang University (Engineering Science), 2015, 49(11): 2033-2039.

Viewed

Full text

Abstract

Cited

Shared

Discussed