Surrogate-model-based dwell time optimization for atmospheric pressure plasma jet finishing

Abstract

With the rapidly increasing requirements for high-precision optical elements in modern optical systems, atmospheric pressure plasma jet (APPJ) finishing has attracted wide attention due to its high material removal efficiency and superior surface quality. However, the temperature fluctuation in the localized processing footprint, induced by the heated plasma and varying dwell time, emerges as a critical factor that oscillates the etching rate and thus degrades the deterministic removal control. Existing dwell time solution algorithms only apply under the assumption that the material removal rate exhibits temporal and spatial constancy, which is not the case in APPJ finishing. To address this challenge, a surrogate-model-based dwell time optimization approach for the temperature-dependent tool influence function (TIF) is proposed in this study. Firstly, a temperature-dependent TIF is proposed to quantitatively describe the thermal effect, and the corresponding calibration method is established. Subsequently, a thermodynamic simulation model is established to simulate the temperature of the localized processing footprint. By integrating the temperature-dependent TIF with the temperature prediction model, a novel dwell time optimization algorithm is proposed to alleviate the error caused by the temperature fluctuation. Given the prohibitive long computation time of the thermodynamic simulation model, a data-driven surrogate model based on Long Short Term Memory (LSTM), capable of rapid temperature prediction, is trained through the customized simulation dataset. The effectiveness of the proposed surrogate-model-based dwell time optimization algorithm is verified through simulations and experiments. Compared to traditional dwell time solution algorithms, the proposed approach yields a 47.12% improvement in machining precision.