Modern ultra-precision manufacturing increasingly focuses on fabricating complex geometries that deliver superior optical performance while also meeting sustainability goals. However, metrology of freeform surfaces has become the main bottleneck in their rapid production. This research seeks to advance on-machine surface measurement systems to address these challenges. In precision machinery, the key difference between on-machine measurement systems and commercial offline devices is the absence of an independent metrology frame in the former, which makes traceability of on-machine measurements a long-standing unresolved issue. Calibration is therefore central to improving on-machine measurement accuracy. Conventional offline calibration principles do not perform as expected when applied to on-machine systems. This study develops a reversal-principle-based on-machine calibration method to identify the critical geometric errors of an ultra-precision diamond-turning machine—errors that affect both machining and on-machine measurement. To meet the demands of higher measurement speeds and greater sag, dynamic errors are identified as the primary limitation on accuracy. Knowledge of on-machine measurement dynamics in optical fabrication is currently almost non-existent. This research determines that the key error sources arise from the structural compliance of the fixtures for the optical cage components assembled in the on-machine prototype. A further step traces the disturbance back to the machine itself. The error identification results encouragingly reveals a transition of error modes: when the fixtures are stiffened, the dynamic error does not disappear, it shifts from a lag to a lead type. A parasitic-motion model of the machine is established, the contribution of machine-axis errors to the measurement results is quantified and calibrated, and a comprehensive system-level uncertainty analysis is performed after fine calibration, taking into account thermal disturbances, spindle errors, and residual geometric errors of the machine axes. Measurement uncertainties are benchmarked against those of an offline system, the LUPHOScan from Taylor Hobson. Existing characterization tools encounter bottlenecks when processing the high-density, high-definition time-series data generated during high-speed surface evaluations. To overcome this challenge, a Surface Intrinsic Mode Decomposition (SIMD) method is developed, enabling accurate evaluation of spatial-frequency errors. The measurement results align with those obtained by white-light interferometry when evaluating diamond-turned ripples with amplitudes of tens of nanometres. To demonstrate closed-loop form-error control, a high-aspect-ratio surface is machined with a slow-tool servo, and on-machine form-error correction is applied. The study concludes that a well-calibrated on-machine surface-measurement system can accurately assess ultra-precise optical components directly at the manufacturing site. Nonetheless, the limited positional feedback provided by the existing metrology frame restricts further improvements in measurement accuracy and machining efficiency. Integrated ultra-compact metrology is therefore a promising solution for the next generation of ultra-precision machines.
| Date of Award | 27 Jan 2026 |
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| Original language | English |
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| Supervisor | Wenhan Zeng (Main Supervisor) & Wenbin Zhong (Co-Supervisor) |
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