Micro mechanical machining is conceived by many as the most versatile method of fabricating miniaturized components in complex three-dimensional shapes from a variety of engineering materials. Although this method has several advantages, such as simplicity and flexibility, compared to photolithographic based methods of micro fabrications, it still faces several challenges. The mechanism of material removal in the micro scale is different from that of conventional macro machining, due to various aspects of material behaviour, such as the presence of a critical chip thickness, elastic recovery, ploughing and size effects, resulting in increased cutting forces and limited productivity. Furthermore, micro tools are small and fragile and can be easily worn and subject to catastrophic failures due to excessive forces and vibrations. Therefore, the prediction and measurement of the cutting forces, as well as monitoring of the micro tool and machining conditions, are imperative.

This research is aimed at the modelling of the material removal in micro mechanical machining, especially for micro milling operations. Micro scratch and orthogonal cutting tests are employed to obtain fundamental material behaviour, investigate size effect and identify parameters, such as the elastic recovery, flow stress and friction coefficient. A novel formulation for the critical chip thickness is suggested and experimentally verified. The identified parameters are used in a new mechanistic cutting force model developed for micro milling operations that considers the effects of critical chip thickness, elastic recovery, ploughing forces, tool dynamics and tool run-out. The tool tip dynamics are indirectly obtained using the receptance coupling method. The cutting and ploughing constants for the mechanistic force model are identified from the experimental cutting data, which are measured using a table dynamometer. The expanded Kalman Filter method is employed to compensate for unwanted dynamics of the table dynamometer for accurate measurement of high-speed micro milling forces.

A tool wear monitoring scheme that fuses the signals from various sensors, such as a force sensor, accelerometers and an acoustic emission sensor, through a neuro-fuzzy algorithm is utilized to increase the frequency bandwidth of measurement and effectively monitor the tool conditions. The effectiveness of fusing different sensors for monitoring is investigated.

This research is important for various micro machining operations in the selection of the optimal machining parameters, in order to obtain the desired productivity and surface quality while maintaining the longevity of micro tools.