Vibration assisted machining: Modelling, simulation, optimization, control and applications

Abstract

Increasing demand for precision components made of hard and brittle materials such as glasses, steel alloys and advanced ceramics, is such that conventional grinding and polishing techniques can no longer meet the requirements of today's precision manufacturing engineering. Particularly, in order to undertake micro-milling of optical glasses or other hard-machining materials, vibration assisted machining techniques have been adopted. However, it is essential and much needed to undertake such processes based on a scientific approach, i.e. the process to be quantitatively controlled and optimized rather than carried out with a trial-and-error manner. In this research, theoretical modelling and instrumental implementation issues for vibration assisted micro-milling are presented and explored in depth. The modelling is focused on establishing the scientific relationship between the process variables such as vibration frequency, vibration amplitude, feedrate and spindle speed while taking into account machine dynamics effect and the outcomes such as surface roughness generated, tool wear and material removal rate in the process. The machine dynamics has been investigated including a static analysis, machine tool-loop stiffness, modal analysis, frequency response function, etc, carried out for both the machine structure and the piezo-actuator device. The instrumentation implementation mainly includes the design of the desktop vibration assisted machining system and its control system. The machining system consists of a piezo-driven XY stage, air bearing spindle, jig, workpiece holder, PI slideway, manual slideway and solid metal table to improve the system stability. The control system is developed using LabVIEW 7.1 programming. The control algorithms are developed based on theoretical models developed by the author. The process optimisation of vibration assisted micro-milling has been studied by using design and analysis of experiment (DOE) approach. Regression analysis, analysis of variance (ANOVA), Taguchi method and Response Surface Methodology (RSM) have been chosen to perform this study. The effects of cutting parameters are evaluated and the optimal cutting conditions are determined. The interaction of cutting parameters is established to illustrate the intrinsic relationship between cutting parameters and surface roughness, tool wear and material removal rate. The predicted results are confirmed by validation experimental cutting trials. This research project has led to the following contribution to knowledge: (1) Development of a prototype desktop vibration assisted micro-milling machine. (2) Development of theoretical models that can predict the surface finish, tool wear and material removal rate quantitatively. (3) Establishing in depth knowledge on the use of vibration assisted machining principles. (4) Optimisation of cutting process parameters and conditions through simulations and machining trials for through investigation of vibration assisted machining.