What is it about?
As the main quality criterion, delaminations at the entry and exit of the drill from the hole were taken. In the experimental study, a conventional drill and two modified geometry drills, a double-point angle drill and a dagger drill, were used. Preliminary experiments showed the best hole quality when using modified drills, which allowed further detailed study only with both modified drills at different drilling speeds and feed rates. Its results in the form of training sets were used to build artificial neural networks (ANNs) to predict delamination at the entry and exit of the drilled holes.
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Why is it important?
An analysis of the fitted response functions presented as 3D surface plots and contour plots led to the selection of the best tool, a double-point angle drill, which demonstrated the lowest achievable delamination both at the entry and at the exit of the holes approximately 1.5 times less (0.45/0.48 mm) compared to dagger drills (0.68/0.7 mm) and determined the ~5 times larger optimal area for the drilling speed and feed rate.
Perspectives
The results obtained confirm the possibility of effective prediction of the quality and productivity of mechanically processed composites of complex reinforcement using ANN to quantify the quality criteria and search for the optimal modes of such technologies.
Prof. Sergey N. Shevtsov
Southern Federal University
Read the Original
This page is a summary of: ANN-Based Estimation of the Defect Severity in the Drilling of GFRP/Ti Multilayered Composite Structure, Journal of Composites Science, December 2022, MDPI AG,
DOI: 10.3390/jcs6120370.
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