Optimizing 5-axis sculptured surface finish machining through design of experiments and neural networks

Fountas, Nikolaos A., Kechagias, John, Benhadj-Djilali, Redha, Stergiou, Constantinos I. and Vaxevanidis, Nikolaos M. (2014) Optimizing 5-axis sculptured surface finish machining through design of experiments and neural networks. In: 12th Biennial Conference on Engineering Systems Design and Analysis; 5-27 Jun 2014, Copenhagen, Denmark. (ASME 2014 12th Biennial Conference on Engineering Systems Design and Analysis Volume 1) ISBN 9780791845837

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Abstract

Five axis machining and CAM software play key role to new manufacturing trends. Towards this direction, a series of 5 axis machining experiments were conducted in CAM environment to simulate operations and collect results for quality objectives. The experiments were designed using an L27 orthogonal array addressing four machining parameters namely tool type, stepover, lead angle and tilt angle (tool inclination angles). Resulting outputs from the experiments were used for the training and testing of a feed-forward, back-propagation neural network (FFBP-NN) towards the effort of optimizing surface deviation and machining time as quality objectives. The selected ANN inputs were the aforementioned machining parameters. The outputs were the surface deviation (SD) and machining time (tm). Experimental results were utilized to train, validate and test the ANN. Major goal is to provide results robust enough to predict optimal values for quality objectives, thus; support decision making and accurate machining modelling.

Item Type: Conference or Workshop Item (Paper)
Event Title: 12th Biennial Conference on Engineering Systems Design and Analysis
Organising Body: American Society of Mechanical Engineers
Uncontrolled Keywords: machining , finishes , artificial neural networks , experimental design
Research Area: Mechanical, aeronautical and manufacturing engineering
Faculty, School or Research Centre: Faculty of Science, Engineering and Computing > School of Mechanical and Automotive Engineering
Depositing User: Redha Benhadj-Djilali
Date Deposited: 03 Nov 2014 15:08
Last Modified: 03 Nov 2014 15:08
URI: http://eprints.kingston.ac.uk/id/eprint/29066

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