LAPSE:2024.0828
Published Article
LAPSE:2024.0828
An Experimental Study on Nodular Iron Machined Surfaces Utilizing a Capable 2D Finite Element Model for Precise Surface Roughness Estimation
Ibrahim T. Teke, Ahmet H. Ertas
June 7, 2024
Abstract
Nodular iron plays a crucial role in various industries, especially in large-scale applications such as gearboxes. Ensuring that nodular iron remains free from oil leakage and that contact surfaces are properly aligned is essential, given its operational requirements. Achieving flat contact faces through precise machining is therefore of utmost importance. As surface roughness and flatness are closely linked, it is vital to investigate the machining process parameters involved. This study focuses on addressing surface quality issues with EN-GJS-600-3 cast iron by optimizing machining parameters. CMM measurements were utilized to analyze the relationship between surface roughness and flatness, with a surface profile used to assess flatness. Furthermore, a new 2D surface roughness estimation method (2D-SRET) was created and tested with experimental data in order to improve the precision of assessing the discrete flat surface machining procedure.
Keywords
2D surface roughness estimation technique (2D-SRET), cast iron, machining, microstructure, nodular iron, surface roughness
Subject
Suggested Citation
Teke IT, Ertas AH. An Experimental Study on Nodular Iron Machined Surfaces Utilizing a Capable 2D Finite Element Model for Precise Surface Roughness Estimation. (2024). LAPSE:2024.0828
Author Affiliations
Teke IT: Department of Mechanical Engineering, Faculty of Engineering, HaliƧ University, Istanbul 34060, Turkey; Department of Mechanical Engineering, Faculty of Engineering & Natural Sciences, Bursa Technical University, Bursa 16330, Turkey [ORCID]
Ertas AH: Department of Mechanical Engineering, Faculty of Engineering & Natural Sciences, Bursa Technical University, Bursa 16330, Turkey
Journal Name
Processes
Volume
12
Issue
3
First Page
549
Year
2024
Publication Date
2024-03-11
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr12030549, Publication Type: Journal Article
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LAPSE:2024.0828
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https://doi.org/10.3390/pr12030549
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Jun 7, 2024
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CC BY 4.0
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