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Prediction of Surface Roughness in Longitudinal Turning Process by a Genetic Learning Algorithm

Vorhersage der Oberflächenrauheit beim Längsdrehen mit einem genetischen Lernalgorithmus
  • Kemal Aldaş , İskender Özkul and Murat Eskil
From the journal Materials Testing

Abstract

The surface roughness is one of the major parameters for determining the level of machining quality. The cutting parameters and conditions have great importance to achieve the desired values during the turning process. In the present work, a new approach was considered for modelling the effect of various turning process parameters and conditions on surface roughness. The experimental studies about the surface roughness after the turning process documented in the literature were collected and compiled into a model based on a genetic learning algorithm. As input parameters for modeling the work piece alloy type, tool type, tool tip radius, tool coating type, cooling conditions, cutting speed, feed rate, and cut depth were used in the study and were comprehensivly compiled.

Kurzfassung

Die Oberflächenrauheit ist einer der wichtigsten Parameter für die Bestimmung der Bearbeitungsqualität. Die Schnittparameter und –bedingungen haben große Bedeutung, um die hierfür geforderten Werte während des Drehprozesses zu erreichen. In der vorliegenden Arbeit wurde ein neuer Ansatz berücksichtigt, um die Auswirkung der verschiedenen Drehprozessparameter und –bedingungen auf die Oberflächenrauheit zu modellieren. Die in der Literatur dokumentierten experimentellen Studien zur Oberflächenrauheit bei Drehprozessen wurden hierzu gesammelt und darauf wurde basierend auf einem genetischen Algorithmus ein Modell erstellt. Als Eingabeparameter für die Modellierungen wurden der Werkstücklegierungstyp, der Werkzeugtyp, der Werkzeugspitzenradius, der Werkzeugbeschichtungstyp, die Abkühlbedingungen, die Schnittgeschwindigkeit, die Zufuhrrate, und die Schnitttiefe herangezogen und umfassend zusammengetragen.


* Correspondence Address, Iskender Özkul, Department of Mechanical Engineering, Faculty of Engineering, Aksaray University, 68100, Aksaray, Turkey, E-mail:

Kemal Aldas is associate professor in Aksaray University at the Engineering Faculty, Turkey. He received his PhD degree from Selçuk University, Turkey, from Department of Mechanical Engineering in1998. His research areas include hydrogen and materials science.

İskender Özkul is a specialist at the University Engineering Faculty. He received his MSc degree from Gazi University, Turkey, from Department of Indsutrial Technology Education Faculty in 2012. His research areas include materials science.

Murat Eskil is assistant professor in Aksaray University, Turkey, at the faculty of Arts and Sciences. He received his PhD degree from Fırat University, Turkey, from Department of Physics in 2006. His research areas include materials science and shape memory.


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Published Online: 2014-10-01
Published in Print: 2014-05-01

© 2014, Carl Hanser Verlag, München

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