Transfer learning-based thermal error prediction and control with deep residual LSTM network

The thermal error is a dominant factor that seriously hinders the high-accuracy machining of complex parts. The weak robustness and low predictive accuracy have always been barriers to the wide use of data-based models. To improve the robustness, the transfer learning-based error control method is proposed in this study. The error mechanism modeling is conducted to demonstrate the memory behavior of thermal errors, and the applicability of a long short-term memory network (LSTMN) for the error prediction is proven. Then an improved least mean square (ILMS) is proposed to filter the high-frequency noises and remove singular values. A pre-activated residual block is designed, and is embedded into the deep residual LSTMN (DRLSTMN). The differential spotted hyenas optimization algorithm (DSHOA) is proposed based on the chaos initialization strategy, differential mutation operator, and nonlinear control factor to optimize the hyper-parameters of DRLSTMN. Then the ILMS-DSHOA-DRLSTMN error prediction model is proposed for machine tool #1. The transfer learning model is established for machine tool #2 based on ILMS-DSHOA-DRLSTMN to enhance the robustness. The predictive abilities of the transfer learning models of ILMS-DSHOA-DRLSTMN, ILMS-DRLSTMN, ILMS-DSHOA-LSTMN, ILMS-back propagation network (ILMS-BP), ILMS-multiple linear regression analysis (ILMS-MLRA), ILMS-least squared support vector machine (ILMS-LSSVM), ILMS-CNNs-LSTM (ILMS-CL), and ILMS-deep calibration (ILMS-DC) are 98.37%, 97.95%, 97.60%, 94.51%, 95.41%, 96.02%, 96.43%, and 96.06%, respectively. Finally, the actual machining experiments were performed. When the thermal error is controlled with the transfer learning model, the fluctuation ranges for the geometric errors for D1 and D2 are [−4μm, 4μm] and [−3μm, 3μm], respectively.