Regression Based Neural Network Modeling for Forecasting of the Metal Volume Removal Rate in Turning Operations

Funda Kahraman; Ugur Esme; Mustafa Kemal Kulekci; Yigit Kazancoglu

doi:10.3139/120.110328

Published by De Gruyter May 26, 2013

Regression Based Neural Network Modeling for Forecasting of the Metal Volume Removal Rate in Turning Operations

Regressionsbasierte Neural Network Modellierungen zur Vorhersage der Metallvolumenabtragsrate bei Drehoperationen

Funda Kahraman , Ugur Esme , Mustafa Kemal Kulekci and Yigit Kazancoglu

From the journal Materials Testing

https://doi.org/10.3139/120.110328

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Abstract

The present paper focuses on two techniques, namely regression and neural network, for predicting tool wear. Predicted values of tool wear by both techniques were compared with experimental values. Also, the effects of the main machining variables on tool wear have been determined. The metal volume removed (MVR) was taken as response (output) variable and cutting speed, feed rate, depth of cut and hardness were taken as input parameters, respectively. The relationship between tool wear and machining parameters was found out by direct measurement of the tool wear by MVR. The results showed the ability of regression and neural network models to predict the tool wear, accurately.

Kurzfassung

Die diesem Beitrag zugrunde liegende Studie fokussiert sich auf zwei Technologien, und zwar der Regressionsanalyse und Neural Network Modellierungen, um den Werkzeugabtrag vorherzusagen. Die mit beiden Technologien vorhergesagten Abtragswerte wurden mit experimentellen Ergebnissen verglichen. Außerdem wurden die Effekte der Haupt-Maschinenvariablen auf den Werkzeugabtrag bestimmt. Der Metallvolumenabtrag (metal volume removed – MVR) wurde dabei als Antwort bzw. Output-Variable und die Schnittgeschwindigkeit, Vorschubrate, Schnitttiefe und Härte wurden dabei als Input-Parameter herangezogen. Es wurde ein Bezug zwischen dem Werkzeugabtrag und den Maschinenparametern hergestellt, in dem der Werkzeugabtrag direkt mittels MVR gemessen wurde. Die Ergebnisse zeigen die Möglichkeit der Regression und der Neural Network Modelle zur direkten Vorhersage des Werkzeugabtrages.

Funda Kahraman is assistant professor Dr. in Mersin University Tarsus Technical Education Faculty. She received her BS and MBA degree from İstanbul Technical University Engineering Faculty Department of Metallurgical Engineering, PhD degree from Cukurova University Engineering and Architecture Faculty Department of Mechanical Engineering. Her research areas include metallurgy, welding and design. She has number of publications on these subjects.

Ugur Esme is assistant professor Dr. in Mersin University Tarsus Technical Education Faculty. He obtained his PhD degree from Cukurova University Department of Mechanical Engineering in 2006. His research areas include CAD/CAM technology, welding, modelling, designing and water jet cutting applications.

Mustafa Kemal Kulekci is professor of the Faculty of Tarsus Technical Education, Department of Machine Education, Mersin University, Mersin, Turkey. He obtained his PhD degree from Gazi University in 2000. His research interests include CAD/CAM, friction stir welding, machinability of materials, and water-jet cutting applications.

Yigit Kazancoglu is assistant professor Dr. in Izmir University of Economics, Dept. of business Administration. He received his BS degree from Industrial Engineering Dept. of Eastern Mediterranean University, MBA degree from Coventry Univertsity and Izmir University of Economics and PhD degree in Ege University in operations management. His work at the university involves giving courses and conducting research in the areas of production planning, operations management and operations research. He is the author of a number of international publications on these subjects.

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Published Online: 2013-05-26

Published in Print: 2012-04-01

Regression Based Neural Network Modeling for Forecasting of the Metal Volume Removal Rate in Turning Operations

Abstract

Kurzfassung

References

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