The major trends in manufacturing are miniaturization, convergence of the traditional research fields and creation of interdisciplinary research areas. These trends have resulted in the development of multi-scale models and multi-scale surfaces to optimize the performance. Multi-scale surfaces that exhibit specific properties at different scales for a specific purpose require multi-scale measurement and characterization. Researchers and instrument developers have developed instruments that are able to perform measurements at multiple scales but lack the much required multi-scale characterization capability. The primary focus of this research was to explore possible multi-scale data fusion strategies and options for surface metrology domain and to develop enabling software tools in order to obtain effective multi-scale surface characterization, maximizing fidelity while minimizing measurement cost and time. This research effort explored the fusion strategies for surface metrology domain and narrowed the focus on Discrete Wavelet Frame (DWF) based multi-scale decomposition. An optimized multi-scale data fusion strategy `FWR method' was developed and was successfully demonstrated on both high aspect ratio surfaces and non-planar surfaces. It was demonstrated that the datum features can be effectively characterized at a lower resolution using one system (Vision CMM) and the actual features of interest could be characterized at a higher resolution using another system (Coherence Scanning Interferometer) with higher capability while minimizing the measurement time.
