Optimization of drilling parameters to minimize delamination in CNT-filled GFRP composites using machine learning
Composites are most commonly fastened in assemblies by drilling. The current investigation examines the effect of the drilling factors on the quality of the drilled holes. The holes were drilled on epoxy resin polymer composites reinforced using glass fibers with Carbon nano tube (CNT) as fillers. H...
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Elsevier
2025-09-01
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| Series: | Applications in Engineering Science |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266649682500055X |
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| author | Aveen K P Ullal Vignesh Nayak K M Pranesh Rao Shivaramu H T V Londhe Neelakantha Shashikumar C M |
| author_facet | Aveen K P Ullal Vignesh Nayak K M Pranesh Rao Shivaramu H T V Londhe Neelakantha Shashikumar C M |
| author_sort | Aveen K P |
| collection | DOAJ |
| description | Composites are most commonly fastened in assemblies by drilling. The current investigation examines the effect of the drilling factors on the quality of the drilled holes. The holes were drilled on epoxy resin polymer composites reinforced using glass fibers with Carbon nano tube (CNT) as fillers. Hand-layup was done to fabricate the composites. The laminated composites were produced with 0 %, 1 %, and 1.5 % of CNT fillers. Operating parameters such as spindle speeds-1000 rpm, 2000 rpm, and 3000 rpm, feed rates- 50 mm/min, 100 mm/min and 150 mm/min were used during the experiments. Torque (T) and thrust force (F) were measured using a digital drilling machine with a dynamometer. A machine learning based multi-output random forest regression model with hyper parameter tuning was used to predict the T, F, and delamination factor (Fd). The algorithm showed that the most important parameter that influenced delamination was speed (s) followed by the feed rate (f) and filler content respectively. Further, it predicted the thrust force and Fd with ±5% accuracy and T with ±10% accuracy. The best combination of speed, feed, filler which would result in a minimized Fd was arrived at with the help of a Bayesian optimization. |
| format | Article |
| id | doaj-art-2645abacb4fb4f31bb4bdf83a82a6b10 |
| institution | Kabale University |
| issn | 2666-4968 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
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| series | Applications in Engineering Science |
| spelling | doaj-art-2645abacb4fb4f31bb4bdf83a82a6b102025-08-20T04:01:01ZengElsevierApplications in Engineering Science2666-49682025-09-012310025710.1016/j.apples.2025.100257Optimization of drilling parameters to minimize delamination in CNT-filled GFRP composites using machine learningAveen K P0Ullal Vignesh Nayak1K M Pranesh Rao2Shivaramu H T3V Londhe Neelakantha4Shashikumar C M5Department of Mechanical Engineering, Mangalore Institute of Technology & Engineering, Autonomous Institute, Affiliated to Visvesvaraya Technological University, Moodbidri, D.K - 574225, Belagavi, Karnataka, IndiaDepartment of Mechanical Engineering, Mangalore Institute of Technology & Engineering, Autonomous Institute, Affiliated to Visvesvaraya Technological University, Moodbidri, D.K - 574225, Belagavi, Karnataka, IndiaRenault Nissan Technology & Business Centre, IndiaDepartment of Mechanical Engineering, Mangalore Institute of Technology & Engineering, Autonomous Institute, Affiliated to Visvesvaraya Technological University, Moodbidri, D.K - 574225, Belagavi, Karnataka, IndiaDepartment of Mechanical Engineering, Mangalore Institute of Technology & Engineering, Autonomous Institute, Affiliated to Visvesvaraya Technological University, Moodbidri, D.K - 574225, Belagavi, Karnataka, IndiaDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; Corresponding author.Composites are most commonly fastened in assemblies by drilling. The current investigation examines the effect of the drilling factors on the quality of the drilled holes. The holes were drilled on epoxy resin polymer composites reinforced using glass fibers with Carbon nano tube (CNT) as fillers. Hand-layup was done to fabricate the composites. The laminated composites were produced with 0 %, 1 %, and 1.5 % of CNT fillers. Operating parameters such as spindle speeds-1000 rpm, 2000 rpm, and 3000 rpm, feed rates- 50 mm/min, 100 mm/min and 150 mm/min were used during the experiments. Torque (T) and thrust force (F) were measured using a digital drilling machine with a dynamometer. A machine learning based multi-output random forest regression model with hyper parameter tuning was used to predict the T, F, and delamination factor (Fd). The algorithm showed that the most important parameter that influenced delamination was speed (s) followed by the feed rate (f) and filler content respectively. Further, it predicted the thrust force and Fd with ±5% accuracy and T with ±10% accuracy. The best combination of speed, feed, filler which would result in a minimized Fd was arrived at with the help of a Bayesian optimization.http://www.sciencedirect.com/science/article/pii/S266649682500055XFRP compositesDelamination factorCNT fillerMachine learningMulti-output random forest regressorBayesian optimization |
| spellingShingle | Aveen K P Ullal Vignesh Nayak K M Pranesh Rao Shivaramu H T V Londhe Neelakantha Shashikumar C M Optimization of drilling parameters to minimize delamination in CNT-filled GFRP composites using machine learning Applications in Engineering Science FRP composites Delamination factor CNT filler Machine learning Multi-output random forest regressor Bayesian optimization |
| title | Optimization of drilling parameters to minimize delamination in CNT-filled GFRP composites using machine learning |
| title_full | Optimization of drilling parameters to minimize delamination in CNT-filled GFRP composites using machine learning |
| title_fullStr | Optimization of drilling parameters to minimize delamination in CNT-filled GFRP composites using machine learning |
| title_full_unstemmed | Optimization of drilling parameters to minimize delamination in CNT-filled GFRP composites using machine learning |
| title_short | Optimization of drilling parameters to minimize delamination in CNT-filled GFRP composites using machine learning |
| title_sort | optimization of drilling parameters to minimize delamination in cnt filled gfrp composites using machine learning |
| topic | FRP composites Delamination factor CNT filler Machine learning Multi-output random forest regressor Bayesian optimization |
| url | http://www.sciencedirect.com/science/article/pii/S266649682500055X |
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