A machine learning workflow for 4D printing: understand and predict morphing behaviors of printed active structures

Abstract Understanding and predicting morphing response of printed active structures remain a challenge in 4D printing. To tackle it, in this paper, we present a consolidated data-driven approach enabled by an ensemble of machine learning (ML) algorithms. First, three ML algorithms were employed to quantitatively correlate a geometrical feature (thickness) with the final morphing shapes indicated by curvatures and curving angles. Among them, the gradient boosting algorithm achieved correlation factors ( R 2 ) of 0.96 and 0.94 when predicting the curvatures and curving angles by using the data collected from 150 experiments. The random forest model enabled to rank the importance of fabrication parameters in determining the shape morphing behaviors. To forecast the dynamic response of printed structures, three time series forecast algorithms were implemented based on the time-dependent image data during morphing processes of the printed active structures. Among them, the exponential smoothing method achieved an average mean absolute percentage error of 0.0139. This work offers a proof-of-concept on how the ensemble ML algorithms can be employed to delineate and predict morphing mechanism of printed active structures, thus providing a new paradigm for advancing the state-of-the-art research in 4D printing.