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Modeling of thrust force and torque in drilling aluminum 7050

  • Ebru Aslan

    Ebru Aslan, born in 1993, is a graduate student in Department of Manufacturing Engineering, University of Gazi, Turkey. Her research interests include: machining methods and finite element method.

         , Duygu Gürkan Kocataş

    Duygu Gürkan Kocataş, born in 1992, is a PhD student in the Department of Manufacturing Engineering, University of Gazi, Turkey. Also, she is Research Assistant in same department. Her research interests include: machining methods, finite element analysis, machine design and industrial measuring techniques.

     EMAIL logo     and Gültekin Uzun

    Gültekin Uzun, born in 1982, is an Associate Professor in Department of Manufacturing Engineering, University of Gazi, Turkey. His research interests include: machine elements, machining methods, machine design, additive manufacturing, and finite element analysis.

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Published/Copyright: February 27, 2024
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Materials Testing
From the journal Materials Testing Volume 66 Issue 4

Abstract

The aluminum AA7050 alloy has high toughness and high strength. Despite the high machinability of the AA7050 alloy, hole quality can vary according to tool geometry and drilling parameters. This study investigated the effects of different cutting parameters and three different drill point angles on thrust force and torque. Numerical analyses for thrust force and torque were performed using the finite element method. The lowest thrust force and the highest torque were obtained with the drill at 130° drill point angle, while the highest cutting force and lowest torque were obtained with the drill at 118° drill point angle. There is an average difference of 5.37 and 6.9 % between the experimental and analysis values for thrust forces and torque, respectively, and the applicability of the finite element model has been proven. In the last part of the study, thrust force and torque are modeled with artificial neural networks. The statistical accuracy (R2) values for the learning and testing values in the thrust force of the equation are 0.997797 and 0.995739, respectively. Torque’s learning and testing accuracy values are 0.987247 and 0.937909, respectively. The obtained equations have a high accuracy rate.

Keywords: drilling; artificial neural network; finite element analysis; torque; thrust force
Corresponding author: Duygu Gürkan Kocataş, Department of Manufacturing Engineering, Gazi University, Ankara 06560, Türkiye, E-mail:

About the authors

Ebru Aslan

Ebru Aslan, born in 1993, is a graduate student in Department of Manufacturing Engineering, University of Gazi, Turkey. Her research interests include: machining methods and finite element method.

Duygu Gürkan Kocataş

Duygu Gürkan Kocataş, born in 1992, is a PhD student in the Department of Manufacturing Engineering, University of Gazi, Turkey. Also, she is Research Assistant in same department. Her research interests include: machining methods, finite element analysis, machine design and industrial measuring techniques.

Gültekin Uzun

Gültekin Uzun, born in 1982, is an Associate Professor in Department of Manufacturing Engineering, University of Gazi, Turkey. His research interests include: machine elements, machining methods, machine design, additive manufacturing, and finite element analysis.

  1. Research ethics: Not applicable.

  2. Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission. Ebru ASLAN made the analysis and experiments. Duygu GURKAN KOCATAS made the measurements and did the general writing of the manuscript. Gültekin UZUN evaluated the results and wrote the manuscript.

  3. Competing interests: The authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: Not applicable.

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Published Online: 2024-02-27
Published in Print: 2024-04-25

© 2024 Walter de Gruyter GmbH, Berlin/Boston

Articles in the same Issue

  1. Frontmatter
  2. Strain-life behavior of thick-walled nodular cast iron
  3. A novel bearing fault detection approach using a convolutional neural network
  4. Improved Gx40CrNi25-20 grade austenitic stainless steel
  5. Enhanced strength of (CoFeNiMn)100−xCrx (x = 5, 20, 35 at.%) high entropy alloys via formation of carbide phases produced from industrial-grade raw materials
  6. Modeling of thrust force and torque in drilling aluminum 7050
  7. Construction of amidinothiourea crosslinked graphene oxide membrane by multilayer self-assembly for efficient removal of heavy metal ions
  8. Effect of tool rotational speed on friction stir spot welds of AZ31B Mg alloy to AISI 304 stainless steel
  9. A new enhanced mountain gazelle optimizer and artificial neural network for global optimization of mechanical design problems
  10. Effect of particle volume fraction on wear behavior in Al–SiC MMC coated on DIN AlZnMgCu1.5 alloy
  11. Processing, microstructural characterization, and mechanical properties of deep cryogenically treated steels and alloys – overview
  12. Experimental and numerical investigation of patch effect on the bending behavior for hat-shaped carbon fiber composite beams
  13. Influence of water on microstructure and mechanical properties of a friction stir spot welded 7075-T651 Al alloy
  14. Effect of copper powder addition on the product quality of sintered stainless steels
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https://doi.org/10.1515/mt-2023-0335
Keywords for this article
drilling; artificial neural network; finite element analysis; torque; thrust force

Articles in the same Issue

  1. Frontmatter
  2. Strain-life behavior of thick-walled nodular cast iron
  3. A novel bearing fault detection approach using a convolutional neural network
  4. Improved Gx40CrNi25-20 grade austenitic stainless steel
  5. Enhanced strength of (CoFeNiMn)100−xCrx (x = 5, 20, 35 at.%) high entropy alloys via formation of carbide phases produced from industrial-grade raw materials
  6. Modeling of thrust force and torque in drilling aluminum 7050
  7. Construction of amidinothiourea crosslinked graphene oxide membrane by multilayer self-assembly for efficient removal of heavy metal ions
  8. Effect of tool rotational speed on friction stir spot welds of AZ31B Mg alloy to AISI 304 stainless steel
  9. A new enhanced mountain gazelle optimizer and artificial neural network for global optimization of mechanical design problems
  10. Effect of particle volume fraction on wear behavior in Al–SiC MMC coated on DIN AlZnMgCu1.5 alloy
  11. Processing, microstructural characterization, and mechanical properties of deep cryogenically treated steels and alloys – overview
  12. Experimental and numerical investigation of patch effect on the bending behavior for hat-shaped carbon fiber composite beams
  13. Influence of water on microstructure and mechanical properties of a friction stir spot welded 7075-T651 Al alloy
  14. Effect of copper powder addition on the product quality of sintered stainless steels
  15. Mechanical and thermal properties of short banana fiber reinforced polyoxymethylene composite materials dependent on alkali treatment
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