The monitoring of the turning tool wear process using an artificial neural network. Part 1: The experimental set-up and experimental results

Author:

Balan G C1,Epureanu A

Affiliation:

1. Universitatea Dunarea de Jos din Galati, Galati, Romania

Abstract

The study of machine tool dynamics is performed here as ‘monitoring’, which involves the checking and improving of machine functioning. A computer processes signals collected from certain sensors. These data then lead to the monitoring decision, which is to associate the current state of operation with one of the classes from a set of known classes. For monitoring in turning, the classes (tool conditions) are shown. The paper by Du, Elbestawi, and Wu (1995) represents the main source of inspiration for this paper, the present contribution being the improvement of the classes and of the monitoring indices. It is shown that the strains produced by Fy can be neglected and only the main cutting force is measured by means of the tensometer system. To validate the recordings, couples of recordings having working parameters quite similar in value were selected; nonetheless, these couples belonged to different classes. It is observable that for the recordings whose cutting working conditions are similar, the average value of force Fz increases as the cutting depth and wear increase. The experimental set-up and experimental results are presented.

Publisher

SAGE Publications

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Specific cutting energy: a physical measurement for representing tool wear;The International Journal of Advanced Manufacturing Technology;2019-03-15

2. Neural network approach for automatic image analysis of cutting edge wear;Mechanical Systems and Signal Processing;2017-05

3. A support vector machine-based online tool condition monitoring for milling using sensor fusion and a genetic algorithm;Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture;2012-08-28

4. The monitoring of the turning tool wear process using an artificial neural network. Part 2: The data processing and the use of artificial neural network on monitoring of the tool wear;Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture;2008-10-01

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