Cutting tool wear monitoring based on a smart toolholder with embedded force and vibration sensors and an improved residual network. (August 2022)

• Record Type: Journal Article
• Authors: Zhang, Pengfei; Gao, Dong; Lu, Yong; Ma, Zhifu; Wang, Xiaoran; Song, Xin
• Is Part Of: Measurement. Volume 199(2022)
• Publication Date: 2022-08
• Subjects: Smart toolholder -- Cutting force -- Vibration -- Tool condition monitoring -- ResNet; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.111520
• Languages: English
• ISSNs: 0263-2241

Abstract

Graphical abstract: Highlights: An integrated multi-sensor smart toolholder including triaxial cutting force, torque and triaxial vibration is designed for the fist time. Smart toolholder are not only convenient to use, but also more sensitive to vibration signal, with less attenuation and richer frequency domain signals. A one-dimensional ResNet with multi-scale wide convolution kernels model (MSW-1D ResNet) is proposed. The TCM model is established based on the smart toolholder, which provide potential solution for practical industrial application. Abstract: Current milling tool condition monitoring (TCM) methods mainly rely on commercially available sensors with cutting force and vibration being the most widely used. However, they suffer from inconvenient installation or invasive machining when the installation position is close to the cutting area. Aiming at the problem of TCM, this paper proposes a TCM system based on a self-developed smart toolholder which is capable of sensing triaxial cutting force, torque and triaxial vibration simultaneously for the first time. The results of modal test and circuit system test demonstrate that the smart toolholder has a dynamic natural frequency of up to 1 kHz. The milling test results show that vibration sensed by the smart toolholder are much larger than the accelerometers installed on the spindle, workpiece and workbench in terms of maximum value, root mean square and kurtosis characteristics. Subsequently, a milling cutter with two physical vapor deposition (PVD) coated inserts was subjected to a full life cycle milling test on a 304 stainless steel workpiece. To build the TCM model, an improved residual network is proposed, which adopts the structure of the first layer of multi-scale large convolution kernels and the subsequent residual network, which can take the raw cutting force and vibration signals as input and automatically extract features. The results show that the established TCM model can identify the degree of tool wear up to 97.5% with lower fluctuations, which provides a practical solution for factory automated milling tool change.