Talks and Poster Presentations (with Proceedings-Entry):

G. Wang, R. Grosu:
"Milling-Tool Wear-Condition Prediction with Statistic Analysis and Echo-State Networks";
Talk: S2M'16: the International Conference on Sustaniable Smart Manufacturing, Lisbon, Portugal; 2016-10-20 - 2016-10-22; in: "Proceedings of S2M'16, the International Conference on Sustaniable Smart Manufacturing", Taylor & Francis, (2016).

English abstract:
Tool wear is the most commonly observed and unavoidable issue in metal milling.
The worn or damaged cutting tools will cause materials loss and machines shut-down.
To tackle this problem, we propose a new method for predicting the wear condition of end-milling tool.
First, we adopt statistic-analysis techniques to analyze the collected data.
Second, we select interesting features based on Pearson-correlation coefficient (PCC).
Finally, those features are applied as inputs to the so called echo-state network to predict subsequent tool wear condition.
The experimental results and theoretical analysis both demonstrate that the proposed method performs better than naive feed-forward neural networks (FFNN) and time-series neural networks (TSNN).

Created from the Publication Database of the Vienna University of Technology.