Effect of burnishing parameters on surface roughness and hardness

Hudayim Basak; Murat Yücel

doi:10.3139/120.110963

Article

Effect of burnishing parameters on surface roughness and hardness

Hudayim Basak and Murat Yücel

Published/Copyright: December 22, 2016

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From the journal Materials Testing Volume 59 Issue 1

Abstract

Burnishing is used as the final operation in machining, and brings along advantages such as increase of hardness, resistance against material fatigue and abrasion resistance on the workpiece on which it is applied. Improving the surface roughness of workpiece, improvements on the surface hardness, increase of the abrasion resistance of the material, decreasing depths on the material surface which may cause cracks, etc., directly depend on parameters such as progression, number of passes, press amount and ball diameter used in burnishing operation. For this study, an apparatus was designed which has different heads for a burnishing operation of rotary pieces. The surface of an Al 6061-T6 alloy was subjected to a respective burnishing operation and the effect of the parameters (i. e., pressure force, progression, number of passes and ball diameter) used in the burnishing operation on the surface roughness and surface hardness was determined. In addition, an artificial neural network (ANN) and an adaptive neuro-fuzzy interference system (ANFIS) model are developed for these parameters. Both models provide accurate results that are comparable to the experimental results. However, the ANFIS based model appears more accurate, compared to the ANN based model with respect to the same conditions for the surface roughness and surface hardness.

Kurzfassung

Das Kugelpolieren wird als letzter Schritt in der maschinellen Bearbeitung eingesetzt und hat einige Vorteile, wie zum Beispiel einen Anstieg der Härte, des Widerstandes gegen mechanische Ermüdung und des Abrasionswiderstandes des Werkstücks, auf dem es angewandt wird. Die Verbesserung der Oberflächenrauheit, Verbesserungen der Oberflächenhärte, der Anstieg des Abrasionswiderstandes des Werkstoffes, abnehmende Tiefen in der Werkstoffoberfläche, wo Risse beginnen können, usw. hängen direkt von Parametern ab, wie zum Beispiel dem Vorschub, der Anzahl von Bearbeitungsgängen, der Höhe des Anpressdruckes und dem Kugeldurchmesser, der beim Kugelpolieren angewandt wird. In der diesem Beitrag zugrunde liegenden Studie wurde ein Apparat entwickelt und eingesetzt, auf dem verschiedene Köpfe angebracht wurden zur Kugelpolierbehandlung von Rundteilen. Die Oberfläche einer Aluminiumlegierung Al 6061-T6 wurde dem entsprechenden Polierprozess unterzogen und es wurden die Auswrikungen der Parameter (Anpresskraft, Vorschub, Zahl der Bearbeitungsgänge und Kugeldurchmesser) des Polierprozesses auf die Oberflächenrauheit und die Oberflächenhärte bestimmt. Zusätzlich wurde jeweils ein Modell basierend auf einem Artificial Neural Network (ANN) und einem Adaptive Neuro-Fuzzy Interference System (ANFIS) für diese Parameter entwickelt. Beide Modelle zeigen korrekte Ergebnisse, die mit den experimentellen Resultaten übereinstimmen. Das ANFIS-basierte Modell erscheint allerdings exakter im Vergleich zum ANN-basierten Modell in Bezug auf dieselben Bedingungen für die Oberflächenrauheit und die Oberflächenhärte.

*Correspondence Address, Assoc. Prof. Dr. Hudayim Basak, Industrial Design Engineering, Gazi University Technology Faculty, 06500 Teknikokullar, Ankara, Turkey, E-mail: hbasak@gazi.edu.tr

Associated Prof. Dr. Hüdayim Basak is working in the Department of Industrial Design Engineering, Technology Faculty at Gazi University, Ankara, Turkey. He obtained his PhD from Gazi University in 2000. His research interests include CAD/CAM, geometric modeling, computer graphics and software in CAD/CAM systems and manufacturing.

Associated Prof. Dr. Murat Yücel received his BSc, MSc and PhD degrees from Gazi University, Ankara, Turkey, in 2000, 2003 and 2008, respectively, and is currently working in the Department of Electical and Electronic Engineering. His main research interests include photonic devices, fiber optic sensing, artificial neural network, fuzzy logic and neuro fuzzy control.

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Published Online: 2016-12-22

Published in Print: 2017-01-05

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https://doi.org/10.3139/120.110963