Neural network-based prediction for surface characteristics in CO2 laser micro-milling of glass fiber reinforced plastic composite

A novel approach to predict the surface characteristics, namely depth and surface roughness, of glass fiber reinforced plastic composite after CO 2 laser milling by using artificial neural networks is developed and optimized. The experimental data are produced using a 60 W CO 2 laser machine to perf...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Neural computing & applications 2021-09, Vol.33 (18), p.11517-11529
Hauptverfasser: Prakash, Shashi, Suman, Siddharth
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:A novel approach to predict the surface characteristics, namely depth and surface roughness, of glass fiber reinforced plastic composite after CO 2 laser milling by using artificial neural networks is developed and optimized. The experimental data are produced using a 60 W CO 2 laser machine to perform milling of unidirectional glass fiber reinforced plastic composite. The CO 2 laser milling is performed in both parallel and perpendicular to fiber direction at five different values of energy deposition (0.066, 0.176, 0.22, 0.264, 0.308) J/mm and three different beam diameters (225, 277.398, 463.869) μm. The artificial neural network model having the architecture of 3-6-6-3, that is two hidden layers with six neurons in each layer, is found to have the best performance based on mean error value. The mean, maximum, and minimum prediction errors for this ANN model are 0.82%, 2.26%, and 0.0004%, respectively. A semiempirical model is also developed to predict the milling depth, and its response is compared with predicted depth from neural network model. The milled depth predicted using the optimized neural network model is far superior compared to the output of the semiempirical model.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-021-05818-w