scholarly journals A Method for Mill Monitoring Based on Inter-insert Periodic Correlation Using Singular Value Decomposition (SVD)

2021 ◽  
Author(s):  
Xiaowen ZHU ◽  
François Girardin ◽  
Jérôme Antoni
Keyword(s):  
End Milling ◽  
Abnormal Behavior ◽  
False Alarms ◽  
Efficient Tool ◽  
Tool Condition ◽  
Condition Monitoring System ◽  
Value Decomposition ◽  
Milling Tools ◽  
Current Operating ◽  
Periodic Correlation

Abstract This paper introduces a method to monitor the wear of end milling tools in real-time production based on inter-insert periodic correlation. The aim is to detect abnormal behavior of the cutter as early as possible to prevent severe tool failure and subsequent losses. The approach takes advantage of the angular domain to segment the signal in periodic cycles of the same angular duration, which are then amenable to correlation analysis. An ordered separability index with latent correlation characteristics is proposed to assess the current operating state of the tool. A series of simulations with existing experimental data are run to test the feasibility of the proposed index and to calculate the corresponding confidence interval. This approach has a high potential to form an efficient tool condition monitoring system. Compared to the traditional teach-in method, this method is more independent of the cutting conditions (changes of velocity or direction) and has no requirement for a trial cut, making the method useful for small batch production and can reduce the rate of false alarms.

scholarly journals A Novel Mutual Information and Partial Least Squares Approach for Quality-Related and Quality-Unrelated Fault Detection

Processes ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 166
Author(s):  
Majed Aljunaid ◽  
Yang Tao ◽  
Hongbo Shi
Keyword(s):  
Fault Detection ◽  
Mutual Information ◽  
Least Squares ◽  
Partial Least Squares ◽  
Principal Component ◽  
False Alarms ◽  
Total Quality ◽  
Process Variables ◽  
Tennessee Eastman Process ◽  
Value Decomposition

Partial least squares (PLS) and linear regression methods are widely utilized for quality-related fault detection in industrial processes. Standard PLS decomposes the process variables into principal and residual parts. However, as the principal part still contains many components unrelated to quality, if these components were not removed it could cause many false alarms. Besides, although these components do not affect product quality, they have a great impact on process safety and information about other faults. Removing and discarding these components will lead to a reduction in the detection rate of faults, unrelated to quality. To overcome the drawbacks of Standard PLS, a novel method, MI-PLS (mutual information PLS), is proposed in this paper. The proposed MI-PLS algorithm utilizes mutual information to divide the process variables into selected and residual components, and then uses singular value decomposition (SVD) to further decompose the selected part into quality-related and quality-unrelated components, subsequently constructing quality-related monitoring statistics. To ensure that there is no information loss and that the proposed MI-PLS can be used in quality-related and quality-unrelated fault detection, a principal component analysis (PCA) model is performed on the residual component to obtain its score matrix, which is combined with the quality-unrelated part to obtain the total quality-unrelated monitoring statistics. Finally, the proposed method is applied on a numerical example and Tennessee Eastman process. The proposed MI-PLS has a lower computational load and more robust performance compared with T-PLS and PCR.

TOOL CONDITION MONITORING SYSTEM IN SERIAL PRODUCTION CONDITIONS

2020 ◽  
Vol 4 (2) ◽  
pp. 161-168
Author(s):  
V.I. GOLOVIN ◽  
S.Yu. RADCHENKO
Keyword(s):  
Monitoring System ◽  
Condition Monitoring ◽  
Cutting Tool ◽  
Tool Condition Monitoring ◽  
Serial Production ◽  
Tool Condition ◽  
Decision Making System ◽  
Software And Hardware ◽  
Condition Monitoring System ◽  
The Cost

One of the most important tasks of serial and mass production is to maintain the continuity of the technological process in order to reduce equipment downtime and, as a result, the cost of production. One of the systems is the tool condition monitoring system. However, the solutions used today are complex software and hardware systems that are not available for most medium and small productions. The article proposes a system based on a comparative analysis of the applied tool with reference instances. The results of the analysis are sent to the decision-making system, which determines the feasibility of further use of the cutting tool for subsequent machining. An example of an experimental study of milling processing is given. The results obtained show the possibility and rationality of using this model to predict the state of the instrument.

Sign in / Sign up

Export Citation Format

Share Document