An Approach to the Classification of Cutting Vibration on Machine Tools
Abstract
:1. Introduction
2. Literature Review
2.1. Skills of Monitoring Machining Processes
2.2. Vibrations of Machine Tools
- (1)
- Out-of-balance rotating or reciprocating machine components. This is due to the fact that the mechanisms transfer energy in uniformly timed impulses to faulty gears, belts, balls, and roller bearings.
- (2)
- Vibrations transmitted from other machines through foundations.
- (3)
- Vibrations caused by chip formation. When a discontinuous type of chip is formed, the recurring fractures of the metal in the shear plane ahead of the tool produce periodic variations. Similarly, in the case of machining operations that produce a continuous chip with a built-up edge, there is a variation in the force on the cutting tool. Yet another source of forced variation may be caused by the formation of chips of varying thickness obtained. The frequency of these periodic variations depends upon the frequency of discontinuity in the chip of the built-up edge, or the number of teeth in the milling cutting.
2.3. Past Research Related to the Vibrations of Machine Tools
3. Previous Classification Approaches
3.1. Decision Tree
3.2. Support Vector Machine (SVM)
3.3. Naive Bayes Classifier
4. Artificial Neural Network (ANN) Architecture
4.1. The Feed-Forward Neural Network
4.2. Back-PropagationAlgorithm
- Step 1:
- Design the structure of FNN and set input parameters of the network.
- Step 2:
- Set learning rate and momentum rate .
- Step 3:
- Initialize the connection weights Wji, Wki and bias weights , to random values.
- Step 4:
- Set stopping criteria.
- Step 5:
- Start training by applying input patterns one at a time and propagate through the layers to calculate total error.
- Step 6:
- Back-propagate error through output and hidden layers and update biases and weights.
- Step 7:
- Back-propagate error through hidden and input layers and update biases and weights.
- Step 8:
- Repeat step 5 to step 8 until stopping criteria are reached.
5. An Application of the Presented ANN Classifier
5.1. CNC MillingMachine
5.2. The Dynamic Behavior of Machine Tools
5.3. Identifying Input and Output Variables
6. Results
7. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Chen, J.-F.; Lo, S.-K.; Do, Q.H. An Approach to the Classification of Cutting Vibration on Machine Tools. Information 2016, 7, 7. https://doi.org/10.3390/info7010007
Chen J-F, Lo S-K, Do QH. An Approach to the Classification of Cutting Vibration on Machine Tools. Information. 2016; 7(1):7. https://doi.org/10.3390/info7010007
Chicago/Turabian StyleChen, Jeng-Fung, Shih-Kuei Lo, and Quang Hung Do. 2016. "An Approach to the Classification of Cutting Vibration on Machine Tools" Information 7, no. 1: 7. https://doi.org/10.3390/info7010007