A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring

Milling is a main processing mode of the modern manufacturing industry, which seriously affects the quality and precision of the machined workpiece. However, it is difficult to monitor the tool wear condition in the continuous cutting process, especially under a variable speed condition. The existing tool wear condition monitoring methods only carry out analysis with a constant engine speed. Different from the general monitoring methods, this paper put forward a milling cutter wear condition monitoring method based on order analysis (OA) and stacked sparse autoencoder (SSAE). The methodology in the research include signals feature extraction and tool wear state monitoring and were designed to analyze the three-phase spindle current signals instead of the traditional force signals and vibration signals. The variable speed signals were transformed into angle domain stationary signals by order analysis, and the SSAE neural network was used to monitor the tool wear state. The proposed method was verified on the laboratory signals and the results showed a better performance than the other methods and a better applicability in actual industrial manufacturing.

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State Monitoring Method for Tool Wear in Aerospace Manufacturing Processes Based on a Convolutional Neural Network (CNN)

Aerospace ◽

10.3390/aerospace8110335 ◽

2021 ◽

Vol 8 (11) ◽

pp. 335

Author(s):

Wei Dai ◽

Kui Liang ◽

Bin Wang

Keyword(s):

Neural Network ◽

Tool Wear ◽

Convolutional Neural Network ◽

Condition Monitoring ◽

Tool Condition Monitoring ◽

Manufacturing Processes ◽

State Monitoring ◽

Monitoring Method ◽

Tool Condition ◽

Aerospace Manufacturing

In the aerospace manufacturing field, tool conditions are essential to ensure the production quality for aerospace parts and reduce processing failures. Therefore, it is extremely necessary to develop a suitable tool condition monitoring method. Thus, we propose a tool wear process state monitoring method for aerospace manufacturing processes based on convolutional neural networks to recognize intermediate abnormal states in multi-stage processes. There are two innovations and advantages of the proposed approach: one is that the criteria for judging abnormal conditions are extended, which is more useful for practical application. The other is that the proposed approach solved the influence of feature-to-recognition stability. Firstly, the tool wear level was divided into different state modes according to the probability density interval based on the kernel density estimation (KDE), and the corresponding state modes were connected to obtain the point-to-point control limit. Then, the state recognition model based on a convolutional neural network (CNN) was developed, and the sensitivity of the monitoring window was considered in the model. Finally, open-source datasets were used to verify the feasibility of the proposed method, and the results demonstrated the applicability of the proposed method in practice for tool condition monitoring.

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A New Tool Wear Condition Monitoring Method Based on Deep Learning under Small Samples

Measurement ◽

10.1016/j.measurement.2021.110622 ◽

2021 ◽

pp. 110622

Author(s):

Yuqing Zhou ◽

Gaofeng Zhi ◽

Wei Chen ◽

Qijia Qian ◽

Dedao He ◽

...

Keyword(s):

Deep Learning ◽

Tool Wear ◽

Condition Monitoring ◽

Small Samples ◽

Monitoring Method ◽

Wear Condition

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Two Current-Based Methods for the Detection of Bearing and Impeller Faults in Variable Speed Pumps

Energies ◽

10.3390/en14154514 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4514

Author(s):

Vincent Becker ◽

Thilo Schwamm ◽

Sven Urschel ◽

Jose Alfonso Antonino-Daviu

Keyword(s):

Condition Monitoring ◽

False Alarms ◽

Signature Analysis ◽

Variable Speed ◽

Monitoring Methods ◽

Time Frequency ◽

Motor Current ◽

Load Point ◽

Bearing Defects ◽

Current Signature

The growing number of variable speed drives (VSDs) in industry has an impact on the future development of condition monitoring methods. In research, more and more attention is being paid to condition monitoring based on motor current evaluation. However, there are currently only a few contributions to current-based pump diagnosis. In this paper, two current-based methods for the detection of bearing defects, impeller clogging, and cracked impellers are presented. The first approach, load point-dependent fault indicator analysis (LoPoFIA), is an approach that was derived from motor current signature analysis (MCSA). Compared to MCSA, the novelty of LoPoFIA is that only amplitudes at typical fault frequencies in the current spectrum are considered as a function of the hydraulic load point. The second approach is advanced transient current signature analysis (ATCSA), which represents a time-frequency analysis of a current signal during start-up. According to the literature, ATCSA is mainly used for motor diagnosis. As a test item, a VSD-driven circulation pump was measured in a pump test bench. Compared to MCSA, both LoPoFIA and ATCSA showed improvements in terms of minimizing false alarms. However, LoPoFIA simplifies the separation of bearing defects and impeller defects, as impeller defects especially influence higher flow ranges. Compared to LoPoFIA, ATCSA represents a more efficient method in terms of minimizing measurement effort. In summary, both LoPoFIA and ATCSA provide important insights into the behavior of faulty pumps and can be advantageous compared to MCSA in terms of false alarms and fault separation.

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Real Time Tool Wear Condition Monitoring in Hard Turning of Inconel 718 Using Sensor Fusion System

Materials Today Proceedings ◽

10.1016/j.matpr.2017.07.208 ◽

2017 ◽

Vol 4 (8) ◽

pp. 8605-8612 ◽

Cited By ~ 5

Author(s):

Rahul Mali ◽

M.T. Telsang ◽

T.V.K. Gupta

Keyword(s):

Tool Wear ◽

Real Time ◽

Sensor Fusion ◽

Condition Monitoring ◽

Inconel 718 ◽

Hard Turning ◽

Fusion System ◽

Wear Condition

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Face Milling Tool Wear Condition Monitoring Based on Wavelet Transform and Shannon Entropy

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.541-542.1419 ◽

2014 ◽

Vol 541-542 ◽

pp. 1419-1423 ◽

Cited By ~ 3

Author(s):

Min Zhang ◽

Hong Qi Liu ◽

Bin Li

Keyword(s):

Wavelet Transform ◽

Tool Wear ◽

Condition Monitoring ◽

Shannon Entropy ◽

Spindle Motor ◽

Face Milling ◽

Motor Current ◽

Milling Tool ◽

Spindle Motor Current ◽

Wear Condition

Tool condition monitoring is an important issue in the advanced machining process. Existing methods of tool wear monitoring is hardly suitable for mass production of cutting parameters fluctuation. In this paper, a new method for milling tool wear condition monitoring base on tunable Q-factor wavelet transform and Shannon entropy is presented. Spindle motor current signals were recorded during the face milling process. The wavelet energy entropy of the current signals carries information about the change of energy distribution associated with different tool wear conditions. Experiment results showed that the new method could successfully extract significant signature from the spindle-motor current signals to effectively estimate tool wear condition during face milling.

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