Modelling and optimization of cutting strategy and surface generation in ultra-precision raster milling

Abstract

Ultra-precision raster milling (UPRM) is an advanced manufacturing technology for the fabrication of non-symmetric freeform surfaces. Due to the different cutting mechanics, the surface generation is very dependent on cutting strategies and more factors affect surface quality in UPRM, as compared with ultra-precision diamond turning and conventional milling. In this research, theoretical and experimental investigations are conducted to study the effect of cutting strategy on surface generation in UPRM and a three-dimensional (3D) holistic kinematic model for surface roughness prediction is developed. The influences of shift length and tool-interference are firstly introduced into surface generation in UPRM. The developed surface roughness prediction model makes more precise predictions than the existing models. The machining of ultra-precision freeform surfaces needs a cost-effective machining process. According to the investigation on influences of cutting strategy and machine characteristics on surface quality and machining efficiency in freeform machining, two methodologies of cutting strategy optimization are proposed to fulfill different objective functions: quality-optimal and time-optimal strategies. The newly developed optimal cutting strategies consider not only the geometry of freeform surfaces, but also the cutting mechanics and surface generation mechanism of raster milling. The effect of material properties on surface roughness cannot be ignored in UPRM. The influences of material swelling and elastic recovery on chip formation and surface generation are investigated in this study. A surface roughness prediction model is built by introducing the material elastic recovery into surface generation and provides more precise predictions than the kinematic model. Moreover, a new method is proposed to characterize the effect of material properties on surface finish by decomposing surface roughness profile into two components: low-frequency component with high amplitude and high-frequency component with lower amplitude. The new method successfully separates the effect of cutting strategy on surface roughness from the material effect. The originality and significance of the thesis include: (i) the successful development of a surface roughness prediction model provides a quantitative relationship between cutting strategy and surface roughness in UPRM without the need for costly trial and error cutting tests; (ii) the cutting strategy optimization methodologies are developed to optimize cutting parameters and TPG to achieve ultra-precision freeform surfaces in an efficient way; (iii) it is the first time that a deterministic roughness prediction model that accounts for material elastic recovery on surface roughness in ultra-precision multi-axis milling has been developed and (iv) a new method is proposed to characterize the extent of the distortion of the cutting profile induced by the material being cut. This study provides an important means for a better understanding of the surface generation mechanism in ultra-precision multi-axis milling and contributes significantly to the further improvement of the performance of ultra-precision machines.