To manufacture parts with nano-or microscale geometry using laser machining, it is essential to have a thorough understanding of the material removal process in order to control the system behaviour. At present, the operator must use trial-and-error methods to set the process control parameters related to the laser beam, motion system, and work piece material. In addition, dynamic characteristics of the process that cannot be controlled by the operator such as power density fluctuations, intensity distribution within the laser beam, and thermal effects can significantly influence the machining process and the quality of part geometry. This paper describes how a multi-layered neural network can be used to model the nonlinear laser micro-machining process in an effort to predict the level of pulse energy needed to create a dent or crater with the desired depth and diameter. Laser pulses of different energy levels are impinged on the surface of several test materials in order to investigate the effect of pulse energy on the resulting crater geometry and the volume of material removed. The experimentally acquired data is used to train and test the neural network's performance. The key system inputs for the process model are mean depth and mean diameter of the crater, and the system outputs are pulse energy, variance of depth and variance of diameter. This study demonstrates that the proposed neural network approach can predict the behaviour of the material removal process during laser machining to a high degree of accuracy.

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A compact mechanism is designed to enable manipulation about a pivot point, different kinds of surgical tools which are commonly used in minimally invasive surgery such as therapy laser delivery tools, biopsy and bracytherapy needles. The robot's special configuration will enable it to reorient a surgical tool about a pivot point conveniently; achieve and control small-scale movement for precision manipulation in two independent degrees of freedom, and allow for miniaturization so it can overcome problems associated with the limited surgical workspaces. The manipulator can be used in manual, autonomous or remote-control modes. Performance analysis showed that the robot can operate with an average angular accuracy of 1.4 and 1.1 degrees for the joints. The features of the proposed mechanism make it well suited for use in a broad range of medical interventions.

Bioartificial liver support provides a bridge to transplantation which is at present the only proven specific treatment for acute liver failure. In this paper, a novel multi-coaxial hollow fiber bioreactor so-called "Fibre-in-Fibre FIF Bioartificial liver device" with three compartments is experimentally and mathematically studied. The mathematical model in this paper is an extension of Krogh cylinder model for hollow fibre devices by including one more zone for oxygen transfer, i.e. oxygenation compartment. Three simultaneous linear differential equations were derived for pressure in plasma and cell compartments and flow rate in cell compartment. To validate the model, Oxygen Transfer Rate and hydrostatic pressure experimental measurements for different flow rates, 17-400 ml/min, and different number of hollow fibres pairs are used. Several important parameters of the Michaelis-Menten was investigated, namely, constant Vmax (the maximum oxygen consumption per unit volume of the cell mass), the oxygen partial pressure, the flow rate of the perfusate at device inlet. The results showed that the oxygenation compartment should easily secure Oxygen to the cells in compartment B.