Controlling Morphing Behaviour in PLA-Based 4D Printing: From Process Parameters to Machine Learning Prediction

On 19 March, Associate Professor Victor Neto delivered a seminar titled ‘Controlling Morphing Behavior in PLA-Based 4D Printing: From Process Parameters to Machine Learning Prediction’. The presentation explored how 4D printed structures change shape under external stimuli and the challenges in controlling this behaviour due to processing-design interactions. It highlighted the role of printing parameters in governing key properties and morphing response in PLA-based parts. Raster angle and geometry were identified as key factors influencing bending behaviour. The use of  machine learning models used to predict performance with high accuracy was also discussed, demonstrating the effectiveness of combining process optimisation, design, and data-driven methods.