A New Method for Real-Time Tool Condition Monitoring in Transfer Machining Stations1

2000 ◽  
Vol 123 (2) ◽  
pp. 339-347 ◽  
Author(s):  
Ya Wu ◽  
Philippe Escande ◽  
R. Du
Keyword(s):  
Decision Making ◽  
Condition Monitoring ◽  
Wavelet Packet ◽  
Signal Reconstruction ◽  
Spindle Motor ◽  
Tool Condition Monitoring ◽  
New Method ◽  
Wavelet Packets ◽  
Tool Condition ◽  
Threshold Crossing

This paper introduces a new method for tool condition monitoring in transfer machining stations. The new method is developed based on a combination of wavelet transform, signal reconstruction, and the probability of threshold crossing. It consists of two parts: training and decision making. Training is aimed at determining the alarm threshold and it is done in six steps: (1) Calculate the wavelet packet transform of the sensor signals (spindle motor current) obtained from normal tool conditions. (2) Select feature wavelet packets that represent the principal components of the signals. (3) Reconstruct the signals from the feature wavelet packets (this removes the unwanted noises). (4) Calculate the statistics of the reconstructed signals. (5) Calculate the alarm thresholds based on the statistics of the reconstructed signals, and (6) Calculate the probability of the threshold crossing (the number of threshold crossing conforms a Poisson distribution). The decision making is done in two steps: (1) Check the threshold crossing, and (2) Calculate the number of threshold crossing to determine whether an alarm shall be given. As demonstrated using a practical example from a drilling transfer station, the new method is effective with a success rate over 90 percent. Also, it is fast (the monitoring decision can be done in milliseconds) and cost-effective (the implementation cost shall be less than $500).

A Novel Generalized Approach for Real-Time Tool Condition Monitoring

2017 ◽  
Vol 140 (2) ◽  
Author(s):  
Mahmoud Hassan ◽  
Ahmad Sadek ◽  
M. H. Attia ◽  
Vincent Thomson
Keyword(s):  
Statistical Analysis ◽  
Condition Monitoring ◽  
High Speed ◽  
Threshold Level ◽  
Processing Technique ◽  
Spindle Motor ◽  
Tool Condition Monitoring ◽  
Important Principle ◽  
Tool Condition ◽  
Cutting Processes

In high-speed cutting processes, late replacement of defective tools may lead to machine breakdowns and badly affect the product quality, which subsequently lead to scrap parts and high process costs. Accurate tool condition detection is essential to achieve high level of competitiveness via increasing process productivity and standardizing the quality of the produced parts. Therefore, tool condition monitoring (TCM) systems have been widely emphasized as an important principle to achieve these industrial demands. Several studies for TCM were carried out to capture tool failure using complex conventional and artificial intelligence (AI) techniques. However, these studies suffer from the absence of standardization and generalization. Hence, this paper presents a robust and reliable processing technique for the cutting process signals to extract generalized features in time and frequency domains. The proposed technique masks the effects of the cutting conditions on the extracted features and accentuates the tool condition effect. Characterization and statistical analysis of the processed features were performed to examine their sensitivity to the tool condition. The results revealed the processing technique capability to separate the features extracted from the spindle motor current signals into two mutually exclusive clusters according to their tool condition. The statistical analysis results were employed to optimize the tool condition detection approach using linear discrimination analysis (LDA) model. The results indicate the capability of the processing technique to minimize the system learning effort and to detect tool wear above the threshold level with accuracy above 90%.

scholarly journals Transfer of Process References between Machine Tools for Online Tool Condition Monitoring

Machines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 282
Author(s):  
Berend Denkena ◽  
Benjamin Bergmann ◽  
Tobias H. Stiehl
Keyword(s):  
Condition Monitoring ◽  
Machine Tools ◽  
Tool Condition Monitoring ◽  
New Method ◽  
False Alarms ◽  
Transfer Of Knowledge ◽  
Monitoring Method ◽  
Detection Rates ◽  
General Process ◽  
Tool Condition

Process and tool condition monitoring systems are a prerequisite for autonomous production. One approach to monitoring individual parts without complex cutting simulations is the transfer of knowledge among similar monitoring scenarios. This paper introduces a novel monitoring method which transfers monitoring limits for process signals between different machine tools. The method calculates monitoring limits statistically from cutting processes carried out on one or more similar machines. The monitoring algorithm aims to detect general process anomalies online. Experiments comprise face-turning operations at five different lathes, four of which were of the same model. Results include the successful transfer of monitoring limits between machines of the same model for the detection of material anomalies. In comparison to an approach based on dynamic time warping (DTW) and density-based spatial clustering of applications with noise (DBSCAN), the new method showed fewer false alarms and higher detection rates. However, for the transfer between different models of machines, the successful application of the new method is limited. This is predominantly due to limitations of the employed process component isolation and differences between machine models in terms of signal properties as well as execution speed.

scholarly journals Artificial Intelligence-Based Hole Quality Prediction in Micro-Drilling Using Multiple Sensors

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 885 ◽  
Author(s):  
Jitesh Ranjan ◽  
Karali Patra ◽  
Tibor Szalay ◽  
Mozammel Mia ◽  
Munish Kumar Gupta ◽  
...  
Keyword(s):  
Condition Monitoring ◽  
Fuzzy Inference ◽  
Wavelet Packet ◽  
Vibration Signal ◽  
Tool Condition Monitoring ◽  
Hole Quality ◽  
Inference System ◽  
Tool Condition ◽  
Micro Drilling ◽  
Sensor Signals

The prevalence of micro-holes is widespread in mechanical, electronic, optical, ornaments, micro-fluidic devices, etc. However, monitoring and detection tool wear and tool breakage are imperative to achieve improved hole quality and high productivity in micro-drilling. The various multi-sensor signals are used to monitor the condition of the tool. In this work, the vibration signals and cutting force signals have been applied individually as well as in combination to determine their effectiveness for tool-condition monitoring applications. Moreover, they have been used to determine the best strategies for tool-condition monitoring by prediction of hole quality during micro-drilling operations with 0.4 mm micro-drills. Furthermore, this work also developed an adaptive neuro fuzzy inference system (ANFIS) model using different time domains and wavelet packet features of these sensor signals for the prediction of the hole quality. The best prediction of hole quality was obtained by a combination of different sensor features in wavelet domain of vibration signal. The model’s predicted results were found to exert a good agreement with the experimental results.

scholarly journals Remote acoustic analysis for tool condition monitoring

2019 ◽  
Vol 38 ◽  
pp. 840-847
Author(s):  
James Coady ◽  
Daniel Toal ◽  
Thomas Newe ◽  
Gerard Dooly
Keyword(s):  
Condition Monitoring ◽  
Acoustic Analysis ◽  
Tool Condition Monitoring ◽  
Tool Condition
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