Enhancement of optical surface quality based on real-time compensation of temperature-driven thermal errors in diamond turning

In diamond turning, thermal error compensation is critical for improving machining accuracy. In the ultraprecision diamond turning of optical devices, fluctuations in room temperature cause periodic waviness on the machined surface, which degrades the optical performance. Surface finishing processes, such as polishing, are generally required to remove surface waviness. However, the materials used in the polishing process can pollute the environment. Moreover, the polishing process produces a large amount of waste, including contaminated alcohol and tissue. This study presents a new method for compensating temperature-driven thermal errors during single-point diamond turning (SPDT) using machine learning. Long short-term memory (LSTM), an artificial recurrent neural network architecture, was used to estimate the thermal error from the temperatures observed at several locations around the SPDT machine. The performance of the proposed method was evaluated by comparing its estimation accuracy with those of other machine learning methods (multiple linear regression and support vector machine). The thermal error was compensated for by controlling the tool position according to the estimated thermal error. To evaluate the results of the thermal error compensation, the intensities of the laser beam profiles reflected from two flat aluminum mirrors with diameters of 100 mm were observed at several propagation distances. The waviness formed on the uncompensated surface resulted in a dramatic increase in intensity owing to the constructive interference caused by Bragg scattering, whereas the intensity of the reflection from the compensated surface did not vary as the waviness was removed.