Neural surface roughness models of CNC machined Glass Fibre Reinforced Composites

被引：2
|
作者：
Alexandrakis, S. [1 ]
Benardos, P. [1 ]
Vosniakos, G-C. [1 ]
Tsouvalis, N. [2 ]
机构：
[1] Natl Tech Univ Athens, Sch Mech Engn, Athens 15780, Greece
[2] Natl Tech Univ Athens, Sch Naval Architecture & Marine Engn, Athens 15780, Greece
来源：
INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY | 2008年 / 32卷 / 2-3期
关键词：
GRFC; CNC; surface roughness; ANN; image analysis; Taguchi;
D O I：
10.1504/IJMPT.2008.018986
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
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(2) and 800 gr/m(2)) were CNC machined. Microscopy and image analysis studies enrich the ANN models with machined material macro-structural characteristics.
引用
收藏
页码：276 / 294
页数：19
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