DSpace Repository

Prediction of drilling induced delamination and circularity deviation in GFRP nanocomposites using deep neural network

Show simple item record

dc.contributor.author Sharma, Panchagnula Jayaprakash
dc.contributor.author Jasti, Naga Vamsi Krishna
dc.date.accessioned 2024-08-16T10:50:01Z
dc.date.available 2024-08-16T10:50:01Z
dc.date.issued 2022
dc.identifier.uri https://www.sciencedirect.com/science/article/pii/S2214785322006071
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/xmlui/handle/123456789/15265
dc.description.abstract Drilling of Glass Fiber Reinforced Polymer (GFRP) nanocomposites is most prevailing topic to understand the composite behaviour under different cutting conditions. The present study is mainly focused on prediction of drilling output responses such as delamination factor and circularity error randomly with the help of deep neural network (DNN) model. L9 orthogonal array is used for experimentation. Drilling operation is performed on 0.3 wt% multi-walled carbon nano tubes reinforced GFRPs with solid carbide, TiCN and TiAlN coated (6 mm- diameter) twist drills. Based on experimental results, two different deep neural network models are prepared with single and double hidden layers by varying node numbers such as 8, 16, 32, 64, and 128. Thrust force, Acoustic Emission RMS voltage, and drill type (coating) are considered as input to the neural network and delamination factor at exit, circularity error are treated as predicted output responses for the given network model. The revealed predicted results recommended that two hidden layers with 32 nodes network model give the lowest absolute error of 0.08% and 3.13% in delamination factor and circularity errors respectively. Similarly, the highest absolute error is identified as 4.19% in delamination factor and 13.14% in circularity error by single hidden layer with 128 nodes. Therefore, it is urged that DNN is the most suitable modelling technique for prediction of drilling responses on GFRP nano composites. en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.subject Mechanical Engineering en_US
dc.subject Nanocomposites en_US
dc.subject Acoustic emission en_US
dc.subject Neural networks en_US
dc.title Prediction of drilling induced delamination and circularity deviation in GFRP nanocomposites using deep neural network en_US
dc.type Article en_US


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account