In this study, our aim is to improve the efficiency of the optical manufacturing process employing magnetorheological finishing (MRF) by quantitatively analyzing the MRF response characteristics that vary according to the type and size of low-spatial frequency. Dimension-variable patterns were devised based on the dimension of the tool influence function (TIF), consisting of two types: a width-variable pattern and a height-variable pattern. These dimension-variable patterns were utilized as input data for the MRF corrective polishing system. The resulting residual figure error of the patterns generated through the MRF corrective polishing system was calculated and expressed as output data. Furthermore, to quantify the MRF response characteristics for low-spatial frequency, the relative error is presented by comparing the input data and output data. The results indicate that the MRF polishing performance for low-spatial frequency is influenced by both the type and size of the frequency, and these trends can assist in devising sophisticated and efficient MRF strategies for manufacturing ultra-precision optical surfaces.
In this paper, we propose a fabrication process based on Magneto-Rheological Finishing (MRF) for a reflective Spiral Phase Plate (SPP) with a continuous surface. The front surface of a nickel-plated aluminum disk is machined by diamond turning as a plane mirror, and spiral structure with low topological charge is generated by sub-aperture polishing tools, i.e., MRF. Interferometers are used to analyze the smoothness of the spiral structure, steepness of the center step and surface roughness of the random areas for the fabricated SPP. The results indicate that the direct-polishing approach can be a promising technique to fabricate high-precision SPPs.
In this paper, we propose a compensation method for the nanometer level of thermal drift by adopting long-short term memory (LSTM) algorithm. The precision of a machining process is highly affected by environmental factors. Especially in case of a single-point diamond turning (SPDT), the temperature fluctuation directly causes the unexpected displacement at nanometer scale between a diamond tool and a workpiece, even in the well-controlled environment. LSTM is one of the artificial recurrent neural network algorithms, and we figure out that it is quite suitable to predict the temperature variation based on the history of thermal fluctuation trends. We monitor the temperatures at 8 spots nearby a SPDT machine, and the neural network based on LSTM algorithm is trained to construct the thermal drift model from the time series data. Results of thermal drift prediction showed that the proposed method gives an effective model upon the well-controlled laboratory environment, and by which the thermal drift can be compensated to improve the precision of SPDT process.
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