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Recently, material removal utilising electrokinetic phenomenon was proposed as an alternative to create material removal at the nanometric level [1]. The concept of the introduced material removal process is to impinge particles contained in the slurry, under the influence of hydrodynamic and electrokinetic effects, onto the workpiece with a predetermined velocity to create material removal on the surface. The material removal process proved to be feasible where the material removal rate was reported to be in the range of a few hundred nm/hr with a surface roughness of a few nm (RMS). This paper aims to look into the effect of the electrochemical dissolution on the material removal process since high voltages are involved during the material removal process. During the experimental study, electrochemical dissolution was observed and it contributed a certain proportion of the material removal process. However, the main material removal mechanism still relies on the mechanical action of the abrasive particles on the surface of the workpiece to create material removal during the process.

With the demand for precise nanometric material removal with minimal defects, several non-contact ultraprecision machining techniques were developed over recent decades. The electrokinetic material removal technique [1] is one such method that allows material to be removed without any physical contact between the tool and the workpiece. In this work, the influence of the slurry mixture on the material removal rate for the electrokinetic material removal process is studied. During the process, it was observed experimentally that the mixture of the slurry affected the material removal rate. The parameters varied in the slurry mixture experiments were the size and concentration of the particles. Explanations for the behaviour of the material removal rate were also suggested during the study to further understand the electrokinetic material removal technique.

The goal of image segmentation is to identify and differentiate one object or region from another. The problems associated with image segmentation or groupings have always been a challenge to the image processing community especially in the case of medical imaging where segmentation can play a key role to 3D model re-construction for medical diagnosis and planning. Though it can be said that there is no ‘one-size-fits-all’ algorithm available and that manual segmentation remains the best available technique, current on-going works have focused on hastening the computational intensive process and to achieve a high level of accuracy with as little human intervention as possible. This is especially true when fast segmentation is required for tomography images such as MRI or CT images.