Quality Analysis And Process Optimization Of Data-driven Optical Element Continuous Polishing

An inertial confinement fusion laser device is a complex and highly systematic super-large optical engineering system that uses strong lasers as a driving source to achieve thermonuclear fusion.The device includes tens of thousands of high-precision,large-diameter optical components,which represent the highest level of precision machining today.Full-aperture continuous polishing is a key technology for fabricating planar optical components in intense laser devices.It offers significant advantages in suppressing mid-frequency waviness error,converging low-frequency large scale surface error,and producing ultra-smooth surfaces.However,selectively removing different areas of the machining surface of optical components using large polishing discs is difficult.This makes control of the surface shape error of optical components mainly reliant on process experience,which makes the surface quality of the machining surface of optical components difficult to predict and the process parameters challenging to adjust.This can lead to unpredictable processing times for optical components and nonconvergent surface shape errors.This paper focuses on improving the processing efficiency of continuous polishing and will cover the following research topics:1)The data acquisition method of continuous polishing is studied and analyzed.The study and analysis of the data acquisition method led to the proposal of a corresponding scheme for data acquisition in the continuous polishing workshop.To complete the data acquisition and analysis,a data acquisition environment was established that utilized the Siemens numerical control system and OM240 acquisition card.2)The analysis focuses on the surface figure error that results from continuously polishing planar optical elements.This study identifies factors that influence surface errors of optical elements based on material removal theory,such as uneven velocity distribution and polishing pressure distribution.By utilizing the historical machining records of continuous polishing,a prediction model was developed using the XGBoost algorithm to establish the relationship between machining parameters and surface shape errors.This approach effectively addresses the challenge of predicting surface shape errors in the continuous polishing process.The accuracy and effectiveness of XGBoost were compared with other regression prediction algorithms to assess their performance.3)The intelligent adjustment of parameters during continuous polishing is studied.The processing process of continuous polishing was analyzed,revealing that it has the Markov property.To facilitate intensive learning and training of continuous polishing using the deep deterministic policy gradient algorithm,the environment,state,action,and reward of continuous polishing were defined.By comparing the adjustment effect of the agent with the historical record,it was found that the agent trained using reinforcement learning achieved better results in adjusting the process parameters of continuous polishing.This approach effectively addresses the challenging task of adjusting the process parameters of continuous polishing,leading to a reduction in the number of processing iterations required for optical elements.4)A quality analysis system for optical elements during continuous polishing is designed and developed.A quality analysis system for continuous polishing has been designed,comprising four functional modules: data acquisition and storage,real-time monitoring of acquisition variables,prediction of optical element surface errors,and intelligent adjustment of optical element process parameters.The system is built on a Browser /Server architecture,with data processing and storage performed on the server’s back end and data rendering and display carried out on the browser’s front end.This allows for easy data collection,storage,query,and visualization services for workshop workers.