Zhilyaev, I.; Chigrinets, E.; Shevtsov, S.; Chotchaeva, S.; Snezhina, N. ANN-Based Estimation of the Defect Severity in the Drilling of GFRP/Ti Multilayered Composite Structure. J. Compos. Sci.2022, 6, 370.
Zhilyaev, I.; Chigrinets, E.; Shevtsov, S.; Chotchaeva, S.; Snezhina, N. ANN-Based Estimation of the Defect Severity in the Drilling of GFRP/Ti Multilayered Composite Structure. J. Compos. Sci. 2022, 6, 370.
Zhilyaev, I.; Chigrinets, E.; Shevtsov, S.; Chotchaeva, S.; Snezhina, N. ANN-Based Estimation of the Defect Severity in the Drilling of GFRP/Ti Multilayered Composite Structure. J. Compos. Sci.2022, 6, 370.
Zhilyaev, I.; Chigrinets, E.; Shevtsov, S.; Chotchaeva, S.; Snezhina, N. ANN-Based Estimation of the Defect Severity in the Drilling of GFRP/Ti Multilayered Composite Structure. J. Compos. Sci. 2022, 6, 370.
Abstract
The main purpose of this study was to develop a model for predicting the quality of holes drilled in the root part of a spar of helicopter main rotor blades made of the Glass Fiber Reinforced Plastic (GFRP)-Ti multilayer polymer composite. 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 the Artificial Neural Networks (ANNs) to predict delamination at the entry and exit of drilled holes. The analysis of the fitted response functions, presented as 3D surfaces plots and superimposed contour plots, made it possible to choose the better tool - a double-point angle drill and determine the optimal area for drilling speed and feed rates, confirming that the prediction of the quality and productivity of machining composites based on ANN is an effective tool to search and quantify the quality criteria of such technologies.
Keywords
polymeric composite drilling; GFRP reinforced with Ti interlayers; hole drilling quality; delaminations; defect severity prediction; ANN based prognosis of the quality; tool geometry and machining conditions.
Subject
Chemistry and Materials Science, Polymers and Plastics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
