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.
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ASME 2014 12th Biennial Conference on Engineering Systems Design and Analysis
July 25–27, 2014
Copenhagen, Denmark
Conference Sponsors:
- International
ISBN:
978-0-7918-4583-7
PROCEEDINGS PAPER
Optimizing 5-Axis Sculptured Surface Finish Machining Through Design of Experiments and Neural Networks Available to Purchase
Nikolaos A. Fountas,
Nikolaos A. Fountas
School of Pedagogical and Technological Education (ASPETE), Athens, Greece
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John Kechagias,
John Kechagias
Technological Educational Institute (TEI) of Thessalia, Larissa, Greece
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Redha Benhadj-Djilali,
Redha Benhadj-Djilali
Kingston University, London, UK
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Constantinos I. Stergiou,
Constantinos I. Stergiou
Technological Educational Institute (TEI) of Piraeus, Egaleo, Greece
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Nikolaos M. Vaxevanidis
Nikolaos M. Vaxevanidis
School of Pedagogical and Technological Education (ASPETE), Athens, Greece
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Nikolaos A. Fountas
School of Pedagogical and Technological Education (ASPETE), Athens, Greece
John Kechagias
Technological Educational Institute (TEI) of Thessalia, Larissa, Greece
Redha Benhadj-Djilali
Kingston University, London, UK
Constantinos I. Stergiou
Technological Educational Institute (TEI) of Piraeus, Egaleo, Greece
Nikolaos M. Vaxevanidis
School of Pedagogical and Technological Education (ASPETE), Athens, Greece
Paper No:
ESDA2014-20210, V001T06A002; 8 pages
Published Online:
October 23, 2014
Citation
Fountas, NA, Kechagias, J, Benhadj-Djilali, R, Stergiou, CI, & Vaxevanidis, NM. "Optimizing 5-Axis Sculptured Surface Finish Machining Through Design of Experiments and Neural Networks." Proceedings of the ASME 2014 12th Biennial Conference on Engineering Systems Design and Analysis. Volume 1: Applied Mechanics; Automotive Systems; Biomedical Biotechnology Engineering; Computational Mechanics; Design; Digital Manufacturing; Education; Marine and Aerospace Applications. Copenhagen, Denmark. July 25–27, 2014. V001T06A002. ASME. https://doi.org/10.1115/ESDA2014-20210
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