Research on the Technique of Tool Wear Monitoring in Plunge Milling

2010 ◽  
Vol 426-427 ◽  
pp. 468-471
Author(s):  
Xu Da Qin ◽  
X.L. Ji ◽  
X. Yu ◽  
S. Hua ◽  
Wei Cheng Liu ◽  
...  
Keyword(s):  
Tool Wear ◽  
Cutting Condition ◽  
Mean Square ◽  
Tool Wear Monitoring ◽  
Plunge Milling ◽  
Vibration Signals ◽  
Bp Artificial Neural Network ◽  
Milling Tool ◽  
Wear Monitoring ◽  
The Mean

The technique of tool wear monitoring in plunge milling is studied. The mean of cutting force signals and the root mean square (RMS) of vibration signals are selected as characteristic quantities. The model between tool wear and the characteristic quantities is built using BP artificial neural network. The result of experiment shows that the module is fit for plunge milling wear’s testing under cutting condition, and it is helpful to monitoring plunge milling tool strong wear.

scholarly journals Indirect online tool wear monitoring and model-based identification of process-related signal

2020 ◽  
Vol 12 (5) ◽  
pp. 168781402091920 ◽  
Author(s):  
Panagiotis Stavropoulos ◽  
Alexios Papacharalampopoulos ◽  
Thanassis Souflas
Keyword(s):  
Tool Wear ◽  
Complex Task ◽  
Tool Wear Monitoring ◽  
Vibration Signals ◽  
Online Tool ◽  
Process Simulations ◽  
Wear Monitoring ◽  
Intrinsic Vibration ◽  
Cutting Speeds

Tool wear monitoring using vibrations is a complex task, due to various simultaneously occurring vibration sources and due to distortion of the signals acquired. This work investigates the mechanism by which tool wear information is concealed within acquired process-intrinsic vibration signals. Excluding other sources of vibration, such as machine-related, is attempted utilizing process simulations. As a case study, face milling is performed for three different cutting speeds. At first, the resulted simulated wear curves have been compared with experimental ones resulted under the same cutting conditions. Then, a quantification of the effect of tool wear on the acquired signals is presented.

scholarly journals Tool Wear Monitoring with Vibration Signals Based on Short-Time Fourier Transform and Deep Convolutional Neural Network in Milling

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhiwen Huang ◽  
Jianmin Zhu ◽  
Jingtao Lei ◽  
Xiaoru Li ◽  
Fengqing Tian
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Tool Wear ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Tool Wear Monitoring ◽  
Short Time Fourier Transform ◽  
Vibration Signals ◽  
Wear Monitoring ◽  
Short Time

Tool wear monitoring is essential in precision manufacturing to improve surface quality, increase machining efficiency, and reduce manufacturing cost. Although tool wear can be reflected by measurable signals in automatic machining operations, with the increase of collected data, features are manually extracted and optimized, which lowers monitoring efficiency and increases prediction error. For addressing the aforementioned problems, this paper proposes a tool wear monitoring method using vibration signal based on short-time Fourier transform (STFT) and deep convolutional neural network (DCNN) in milling operations. First, the image representation of acquired vibration signals is obtained based on STFT, and then the DCNN model is designed to establish the relationship between obtained time-frequency maps and tool wear, which performs adaptive feature extraction and automatic tool wear prediction. Moreover, this method is demonstrated by employing three tool wear experimental datasets collected from three-flute ball nose tungsten carbide cutter of a high-speed CNC machine under dry milling. Finally, the experimental results prove that the proposed method is more accurate and relatively reliable than other compared methods.

scholarly journals Tool Wear Monitoring for Complex Part Milling Based on Deep Learning

2020 ◽  
Vol 10 (19) ◽  
pp. 6916 ◽  
Author(s):  
Xiaodong Zhang ◽  
Ce Han ◽  
Ming Luo ◽  
Dinghua Zhang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Tool Wear ◽  
Learning Model ◽  
Cutting Condition ◽  
Force Model ◽  
Tool Wear Monitoring ◽  
Monitoring Method ◽  
Complex Part ◽  
Wear Monitoring

Tool wear monitoring is necessary for cost reduction and productivity improvement in the machining industry. Machine learning has been proven to be an effective means of tool wear monitoring. Feature engineering is the core of the machining learning model. In complex parts milling, cutting conditions are time-varying due to the variable engagement between cutting tool and the complex geometric features of the workpiece. In such cases, the features for accurate tool wear monitoring are tricky to select. Besides, usually few sensors are available in an actual machining situation. This causes a high correlation between the hand-designed features, leading to the low accuracy and weak generalization ability of the machine learning model. This paper presents a tool wear monitoring method for complex part milling based on deep learning. The features are pre-selected based on cutting force model and wavelet packet decomposition. The pre-selected cutting forces, cutting vibration and cutting condition features are input to a deep autoencoder for dimension reduction. Then, a deep multi-layer perceptron is developed to estimate the tool wear. The dataset is obtained with a carefully designed varying cutting depth milling experiment. The proposed method works well, with an error of 8.2% on testing samples, which shows an obvious advantage over the classic machine learning method.

Sign in / Sign up

Export Citation Format

Share Document