Mid-spatial frequency removal on aluminum free-form mirror

Area of Science:

• Optical engineering
• Manufacturing science
• Materials science

Background:

• Mid-spatial frequency (MSF) errors are a significant challenge in freeform optics manufacturing, often stemming from tool-misfit and impacting functional surface performance.
• Previous work established hard-tool grolishing as effective for MSF removal on aluminum, but challenges remain for complex freeform shapes.
• Power spectral density (PSD) is a key diagnostic for quantifying MSF errors.

Purpose of the Study:

• To develop and validate a novel process-chain for removing MSF errors on saddle-like freeform aluminum mirrors.
• To optimize CNC bonnet polishing and subsequent non-Newtonian (n-N) tool polishing for MSF attenuation.
• To explore the dynamic behavior of n-N materials and implement a new 'hyper-crossing' tool-path for robotic polishing.

Main Methods:

• Optimization of CNC Precessions bonnet polishing to minimize initial MSFs.
• Application of a non-Newtonian (n-N) tool for residual MSF attenuation on a saddle-shaped aluminum mirror.
• Modeling and experimental investigation of n-N material dynamics to define optimal rotation speeds.
• Deployment of an industrial robot with a novel 'hyper-crossing' tool-path for efficient polishing.

Main Results:

• Successful reduction of MSF errors on the saddle-like freeform aluminum mirror.
• Demonstration of effective MSF attenuation using the optimized n-N tool and 'hyper-crossing' path.
• Validation of the process-chain's capability to address tool-misfit challenges in freeform manufacturing.
• Detailed analysis of results using power spectral density (PSD) measurements.

Conclusions:

• The developed process-chain, combining optimized CNC polishing with a novel robotic n-N tool-path, effectively removes MSF errors from freeform surfaces.
• The 'hyper-crossing' tool-path is well-suited for robotic polishing of freeform optics, offering an alternative to previous methods.
• This approach advances the manufacturing of high-performance functional surfaces by addressing critical MSF error challenges.