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Positron Annihilation Spectroscopy Study of Metallic Materials after High-Speed Cutting
Jinquan Li1, Roman Laptev2, Iurii Bordulev2
1School of Mechanical Engineering, Shenyang Ligong University, No.6 Nanping Center Road, Hunnan New District, Shenyang 110159, China.
Materials (Basel, Switzerland)
|February 15, 2022
Summary
High-speed cutting creates atomic-scale defects in GCr15 steel
Area of Science:
- Materials Science
- Mechanical Engineering
- Surface Engineering
Background:
- High-speed cutting often generates a white layer on machined surfaces, negatively impacting mechanical properties.
- The white layer's properties are linked to its material structure and cutting-induced defects.
- Research on atomic-scale defects within the white layer is limited.
Purpose of the Study:
- To investigate the influence of cutting parameters (feed rate, cutting speed, cutting depth) on atomic-scale defects in GCr15 steel during high-speed cutting.
- To characterize the types and distribution of defects in the white layer and subsurface regions.
Main Methods:
- Utilized positron annihilation spectroscopy (PAS) to study atomic-scale defects.
- Analyzed GCr15 steel samples subjected to varying high-speed cutting parameters.
Main Results:
- Positron annihilation studies revealed defects characteristic of plastically deformed or tempered carbon steel, along with vacancy cluster components.
- The quantity of vacancy clusters varied with changes in cutting parameters.
- Significant microstructural alterations, including phase transformations, deformation, and thermal impacts, were observed in the subsurface region up to 1 µm.
- No single type of atomic-scale defect dominated the subsurface region.
Conclusions:
- Cutting parameters significantly influence the type and quantity of atomic-scale defects in the white layer of GCr15 steel.
- The white layer and its subsurface region exhibit complex microstructural changes due to combined mechanical and thermal effects during high-speed cutting.
- Understanding these defects is crucial for predicting and improving the performance of machined components.

