Application of Mesh-free and Finite Element Methods in Modelling Nano-Scale Material Removal from Copper Substrates: A Computational Approach - data
This is the complete dataset for a study which aimed to investigate the application of mesh-free and finite element methods in modelling nano-scale material removal from copper substrates. The dataset comprises a number of related data sub-sets as follows:
1) Average values of simulated groove widths and vertical dimensions for different values of cutting tip radii (i.e., 20 nm, 50 nm and 100 nm), cutting depths (i.e., 20 nm, 50 nm, 100 nm and 150nm), and rake angles (i.e., -15°, -30° and -60°)
2) Cutting and normal forces simulated with the Finite Element and Smooth Particle Hydrodynamics methods along the groove length for a depth of cut of 100 nm with a rake angle of -60°.
3) Average values of simulated cutting and normal forces for different values of cutting tip radii (i.e., 20 nm, 50 nm and 100 nm), cutting depths (i.e., 20 nm, 50 nm, 100 nm and 150nm), and rake angles (i.e., -15°, -30° and -60°)
4) Comparison of experimental data (taken from Applied Mathematical Modelling, 2012, 36(11), 5589-5602) and simulated forces for 100 nm and 150 nm scratching depths with a tip exhibiting 100 nm radius and -60° rake angle.
5) Variation of the simulated force ratio as a function of the ratio of the scratching depth and tip radius for different rake angles (i.e., -15° and -60°) and variation of simulated normal and cutting forces with a tip exhibiting 100 nm radius and -15° rake angle.(a)
6) Simulated deformed thickness of nano-grooves with different cutting depths (i.e., 20 nm, 50 nm, 100 nm and 150nm), tip radii (i.e., 20 nm, 50 nm and 100 nm) and rake angles (i.e., -15°, -30° and -60°)
Research results based upon these data are published at https://doi.org/10.1016/j.ijsolstr.2024.112891
Funding
AFM-based nano-machinery, developing and validating a novel modelling approach for the effective process implementation in nanotechnology applications (2020-09-01 - 2024-08-30); Brousseau, Emmanuel. Funder: Engineering and Physical Sciences Research Council
History
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Microsoft ExcelLanguage(s) in dataset
- English-Great Britain (EN-GB)