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.

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 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.

Development of an Adequate Online Tool Wear Monitoring System in Turning Process Using Low Cost Sensor

2012 ◽  
Vol 13 (1) ◽  
pp. 702-706 ◽  
Author(s):  
Jaharah A. Ghani ◽  
Muhammad Rizal ◽  
Mohd Zaki Nuawi ◽  
Che Hassan Che Haron
Keyword(s):  
Tool Wear ◽  
Monitoring System ◽  
Low Cost ◽  
Tool Wear Monitoring ◽  
Turning Process ◽  
Online Tool ◽  
Wear Monitoring

Toward development of a new online tool wear compensation strategy in micro-electro-discharge machining drilling

2016 ◽  
Vol 231 (4) ◽  
pp. 588-599 ◽  
Author(s):  
C K Nirala ◽  
P Saha
Keyword(s):  
Tool Wear ◽  
Empirical Relationship ◽  
Average Energy ◽  
Compensation System ◽  
Tool Wear Monitoring ◽  
Compensation Strategy ◽  
Online Tool ◽  
Electro Discharge Machining ◽  
Wide Range ◽  
Wear Monitoring

In micro-electro-discharge machining drilling, the problem of tool wear is a well-known fact. In order to minimize the effect of tool wear on the accuracy of fabricated product, an online tool wear monitoring and compensation system needs to be integrated with the micro-electro-discharge machining machine. The existing monitoring and compensation system very much relies on the pulse discrimination. The available systems assume that pulses are isoenergetic and are applicable to a single parametric setting only. In order to make the system more robust, a new pulse discrimination and tool wear compensation strategy which is suitable for a wide range of parametric settings is proposed. In this context, an empirical relationship between “average energy” (AE) and “volume removal per discharge” (VRD) is established and verified with experimental results.

Sign in / Sign up

Export Citation Format

Share Document