Neural surface roughness models of CNC machined Glass Fibre Reinforced Composites
Source: International Journal of Materials and Product Technology, Volume 32, Numbers 2-3, 27 June 2008 , pp. 276-294(19)
Publisher: Inderscience Publishers
Abstract: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/m² and 800 gr/m²) were CNC machined. Microscopy and image analysis studies enrich the ANN models with machined material macro‐structural characteristics.
Document Type: Research Article
Affiliations: 1: National Technical University of Athens, School of Mechanical Engineering, Iroon Polytexneiou 9, 15780 Zorgafou, Athens, Greece. 2: National Technical University of Athens, School of Naval Architecture & Marine Engineering, Iroon Polytexneiou 9, 15780 Zorgafou, Athens, Greece
Publication date: 2008-06-27
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