A deep transfer learning model based on pockets clustering and feature reconstruction for dimensional accuracy forecast in aerospace skin parts manufacturing.

Skin parts are widely used in the modern aerospace industry. The remaining wall thickness of pockets on a skin part must be precisely controlled to reduce overall weight and ensure strength. The in-process dimensional accuracy forecast of remaining wall thickness is urgently required for process efficiency improvement and compensation. In this paper, the spindle power signal of pocket milling is used for the in-process dimensional accuracy forecast. Based on deep learning mothed, the correlation between dimensional accuracy and features extracted from power signal is established. To improve the applicability of the forecast model, a deep transfer learning model based on pockets clustering and feature reconstruction is proposed. The pockets of a skin part are divided into different types through clustering. Then, the trained deep learning network for feature extraction of one type of pocket is transferred to a new type by feature reconstruction. The results indicate that the deep transfer learning model can achieve in-process dimensional accuracy forecast for remaining wall thickness with high precision. [ABSTRACT FROM AUTHOR]