Assessment and investigation of SPH modelling for nano-scratching

Shen, Hao, Brousseau, Emmanuel ORCID: https://orcid.org/0000-0003-2728-3189 and Kulasegaram, Sivakumar ORCID: https://orcid.org/0000-0002-9841-1339 2022. Assessment and investigation of SPH modelling for nano-scratching. Presented at: 2022 8th International Conference on Nanomanufacturing & 4th AET Symposium on ACSM and Digital Manufacturing (Nanoman-AETS), 30 August - 01 September 2022. 2022 8th International Conference on Nanomanufacturing & 4th AET Symposium on ACSM and Digital Manufacturing (Nanoman-AETS). IEEE, 10.1109/Nanoman-AETS56035.2022.10119520

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Abstract

The computational modelling of nano-machining using Smooth Particles Hydrodynamics (SPH) method is a research area of recent interest. Among the papers which focus on SPH simulation of nano-machining on copper workpieces, Johnson-Cook model parameters obtained at macro-scale have been used previously [1]–[3] for describing constitutive properties of copper. This paper establishes a SPH model for nano-scratching on copper workpiece with the aim of evaluating the feasibility of using these macro-scale Johnson-Cook parameters in SPH simulation of nano-scale machining. The geometrical parameters for tool tip and workpiece for SPH modelling of nano-scratching is adopted from Islam et al. [4] and simulation results are compared with the experimental work of these authors which was reported in [4] and [5]. The analysis of machining forces shows that the cutting and normal forces slightly increase as the cutting speed increases, and that these are close to experimental results. Simulation results also show that ploughing is the main mode of surface deformation and that the cross-sectional profile of a machined nano-groove matches well with experimental results. This paper contributes towards further elaborating the suitability of employing macro-scale Johnson-Cook parameters in the SPH simulation of nano-machining to predict material responses.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Professional Services > Advanced Research Computing @ Cardiff (ARCCA) Schools > Engineering
Publisher:	IEEE
ISBN:	978-1-6654-7604-1
Last Modified:	30 Aug 2024 07:45
URI:	https://orca.cardiff.ac.uk/id/eprint/160286

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