Modeling and optimization of CNC milling of AISI 1050 steel by a regression based differential evolution algorithm (DEA)

Ugur Esme; Mustafa Kemal Kulekci; Deniz Ustun; Barış Buldum; Yigit Kazançoğlu; Seref Ocalır

doi:10.3139/120.110907

Published by De Gruyter October 10, 2018

Modeling and optimization of CNC milling of AISI 1050 steel by a regression based differential evolution algorithm (DEA)

Modellierung und Optimierung des CNC-Fräsens eines AISI 1050 Stahls mittels eines Differentialevolutionsalgorithmus

Ugur Esme , Mustafa Kemal Kulekci , Deniz Ustun , Barış Buldum , Yigit Kazançoğlu and Seref Ocalır

From the journal Materials Testing

https://doi.org/10.3139/120.110907

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Abstract

The present study is aimed at finding an optimization strategy for the CNC pocket milling process based on regression analysis including differential evolution algorithm (DEA). Milling parameters such as cutting speed, feed rate and depth of cut have been designed using rotatable central composite design (CCD). The AISI 1050 medium carbon steel has been machined by a high speed steel (HSS) flat end cutter tool with 8 mm diameter using the zig-zag cutting path strategy under air flow condition. The influence of milling parameters has been examined. The model for the surface roughness, as a function of milling parameters, has been obtained using the response surface methodology (RSM). Also, the power and adequacy of the quadratic mathematical model have been proved by analysis of variance (ANOVA) method. Finally, the process design parameters have been optimized based on surface roughness using bio-inspired optimization algorithm, called differential evolution algorithm (DEA). The enhanced method proposed in this study can be readily applied to different metal cutting processes with greater and faster reliability.

Kurzfassung

Die dem vorliegenden Beitrag zugrunde liegende Studie zielt darauf hin, eine Optimierungsstrategie für den Taschen-CNC-Fräsprozess basierend auf einer Regressionsanalyse einschließlich eines Differentialevolutionsalgorithmus (DEA) zu entwickeln. Hierzu wurden die Fräsparameter, wie beispielswweise die Schnittgeschwindigkeit, die Vorschubrate und die Schnitttiefe mittels des rotierbaren Zentralkompositdesigns (Central Composite Design (CCD)) entworfen. Der Stahl AISI 1050 mit einem mittleren Kohlenstoffgehalt wurde mittels eines Flachendschneidwerkzeuges mit einem Durchmesser von 8 mm aus einem Hochgeschwindigkeitsschnellarbeitsstahl (HSS) in einer Zick-Zack-Schneidpfadstrategie unter Luftzufuhr bearbeitet. Es wurde der Einfluss der Fräsparameter untersucht. Das Modell für die Oberflächenrauheit wurde als Funktion der Fräsparameter mittels des Oberflächenantwortverfahrens ermittelt. Außerdem wurde die Leistungsfähigkeit und die Genauigkeit des quadratischen mathematischen Modells mittels Varianzanalyse (ANOVA) geprüft. Schließlich wurden die Prozessparameter basierend auf der Oberflächenrauheit optimiert, indem ein bio-inspirierter Optimierungsalgorithmus angewendet wurde, der Differentialevolutionsalgorithmus (DEA) genannt wird. Das fortschrittliche Verfahren, wie es in dem vorliegenden Beitrag propagiert wird, kann sofort auf verschiedene Metallschneidprozesse mit größerer und schnellerer Zuverlässigkeit angewendet werden.

*Correspondence Address, Associate Prof. Dr. Ugur Esme, Institute of Natural and Applied Sciences, Department of Manufacturing Engineering, Mersin University, 33400 Tarsus-Mersin, Turkey, E-mail: uguresme@gmail.com

Assoc. Prof. Dr. Ugur Esme is Associate Professor and a lecturer in the Department of Automotive Engineering, Technology Faculty at Mersin University Tarsus, Turkey. He obtained his PhD degree from the Department of Mechanical Engineering of Cukurova University in Adana, Turkey, in 2006. His research areas include CAD/CAM technology, welding, modeling, designing as well as water jet cutting applications.

Prof. Mustafa Kemal Kulekci is Professor in the Department of Automotive Engineering, Technology Faculty, Mersin University Tarsus, Turkey. He obtained his PhD degree from Gazi University, in Ankara, Turkey in 2000. His research interests include CAD/CAM, friction stir welding, machinability of material as well as water-jet cutting applications.

Deniz Ustun has completed his BS degree in the Department of Computer Science Engineering at Istanbul University, Turkey in 2001. He received his MSc degree in Electrical-Electronics Engineering from Mersin University, Turkey in 2009. Since 2010, he has been studying towards his PhD degree in the same department. His current research interests are artificial neural networks and computer modeling of heuristic optimization algorithms.

Assist. Prof. Barış Buldum is Assistant Professsor at the Department of Mechanical Engineering of Mersin University, Turkey. His research areas include machinability, metals and composites, design and construction. He has been working in Advanced Technology Education, Research and Application Center at Mersin University since 1997.

Assoc. Prof. Yigit Kazançoğlu is Associate Professsor in the Department of Business Administration of Izmir University of Economics, Turkey. He received his BS degree from the Industrial Engineering Department of Eastern Mediterranean University, Izmir, Turkey, his MBA degree from Coventry Univertsity, UK, and Izmir University of Economics, Turkey, and his PhD degree in Operations Management from Ege University in Izmir, Turkey. His work at the university involves giving courses and conducting research in the areas of production planning, operations management and operations research.

Seref Ocalır, born in 1982, is working as a research assistant at Mersin University Graduate School of Natural and Applied Sciences, Turkey. He obtained his BSc degree and MSc degree from Mersin University, Turkey in 2006 and 2009, respectively. His research interests include friction stir welding, machinability of materials, design and corrosion.

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Published Online: 2018-10-10

Published in Print: 2016-07-15

Modeling and optimization of CNC milling of AISI 1050 steel by a regression based differential evolution algorithm (DEA)

Abstract

Kurzfassung

References

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