Article detail · 2023
Optimization of Machining Parameters to Minimize Cutting Forces and Surface Roughness in Micro-Milling of Mg13Sn Alloy
- Year
- 2023
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue Micromachines
- Catalog match (ISSN) Micromachines
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
This comprehensive study investigates the micro-milling of a Mg13Sn alloy, a material of considerable interest in various high-precision applications, such as biomedical implants. The main objective of the study was to explore the optimizations of variable feed per tooth (fz), cutting speed (Vc), and depth of cut (ap) parameters on the key outcomes of the micro-milling process. A unique experimental setup was employed, employing a spindle capable of achieving up to 60,000 revolutions per minute. Additionally, the study leveraged linear slides backed by micro-step motors to facilitate precise axis movements, thereby maintaining a resolution accuracy of 0.1 μm. Cutting forces were accurately captured by a mini dynamometer and subsequently evaluated based on the peak to valley values for Fx (tangential force) and Fy (feed force). The study results revealed a clear and complex interplay between the varied cutting parameters and their subsequent impacts on the cutting forces and surface roughness. An increase in feed rate and depth of cut significantly increased the cutting forces. However, the cutting forces were found to decrease noticeably with the elevation of cutting speed. Intriguingly, the tangential force (Fx) was consistently higher than the feed force (Fy). Simultaneously, the study determined that the surface roughness, denoted by Sa values, increased in direct proportion to the feed rate. It was also found that the Sa surface roughness values decreased with the increase in cutting speed. This study recommends a parameter combination of fz = 5 µm/tooth feed rate, Vc = 62.8 m/min cutting speed, and ap = 400 µm depth of cut to maintain a Sa surface roughness value of less than 1 µm while ensuring an optimal material removal rate and machining time. The results derived from this study offer vital insights into the micro-milling of Mg13Sn alloys and contribute to the current body of knowledge on the topic.
Topics
Citations
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57 citations
OpenAlex cited_by_count (cache / database)
20 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- Effect of cutting conditions on tool wear and wear mechanism in micro-milling of additively manufactured titanium alloy 2024
- Effect of cutting conditions on tool wear and wear mechanism in micro-milling of additively manufactured titanium alloy 2024
- Effect of cutting conditions on tool wear and wear mechanism in micro-milling of additively manufactured titanium alloy 2024
- Review of Image Processing Methods for Surface and Tool Condition Assessments in Machining 2024
- Review of Image Processing Methods for Surface and Tool Condition Assessments in Machining 2024
- Review of Image Processing Methods for Surface and Tool Condition Assessments in Machining 2024
- Machine Learning-Driven Optimization of Machining Parameters Optimization for Cutting Forces and Surface Roughness in Micro-Milling of AlSi10Mg Produced by Powder Bed Fusion Additive Manufacturing 2025
- Machine Learning-Driven Optimization of Machining Parameters Optimization for Cutting Forces and Surface Roughness in Micro-Milling of AlSi10Mg Produced by Powder Bed Fusion Additive Manufacturing 2025
- Machine Learning-Driven Optimization of Machining Parameters Optimization for Cutting Forces and Surface Roughness in Micro-Milling of AlSi10Mg Produced by Powder Bed Fusion Additive Manufacturing 2025
- Magnesium alloys for next-generation engineering: Properties, applications, and machining challenges from macro to micro scales 2026