Integrated deep learning-based online layer-wise surface prediction of additive manufacturing

Abstract

Additive manufacturing (AM) is growing tremendously through its introduction into various industries over recent years, because of its capability of fabricating objects with complex designs. Despite its great successes, AM still has to deal with its main challenge, which is the quality assurance of the printed object. Although extensive studies have been conducted, there are still several critical gaps facing this technology. Due to layer-wise fabrication, one important aspect of quality assurance is to capture the surface morphology of the layer being printed online, which can help to better understand the process dynamics and greatly benefit the promotion of in-process quality control. Thus, this study developed an integrated deep learning model to achieve online layer-wise surface prediction of AM. The proposed deep learning model integrates the convolution autoencoder for surface representation learning and a long short-term memory (LSTM) network for layer-wise prediction. The proposed method is validated by a real-world case study in a common AM process, fused filament fabrication (FFF). The comparison with the benchmarks demonstrates that the developed method has great potential for online layer-wise prediction in AM.