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Neural surface roughness models of CNC machined Glass Fibre Reinforced Composites

Published Online:pp 276-294

CNC machining of parts from pre-made Glass Fibre Reinforced Composites (GFRCs) blocks started gaining ground. However, wrong cutting conditions result in poor surface quality, delaminations or other damaging effects. In this work, a computational tool is developed to help improve machinability of these parts by accounting for surface quality. Artificial Neural Network models trained with data obtained through Taguchi-style designed experiments predict surface roughness obtained. GFRC blocks made from D.E.R.321 epoxy resin, CHEM.93-1-74, PC12 stabiliser and Woven Roving (500 gr/m? and 800 gr/m?) were CNC machined. Microscopy and image analysis studies enrich the ANN models with machined material macro-structural characteristics.


GRFC, glass fibre reinforced composites, CNC machining, surface roughness, ANN, neural networks, image analysis, Taguchi mathods, machining composites, machinability, surface quality, microstructure