Iterative learning for efficient additive mass production

Material extrusion could enable on-demand production of complex and personalized parts but is limited by low reliability, particularly in higher-volume production. Machine learning-based methods may enhance reliability, but are often themselves insufficiently reliable for use in production. Foundation artificial intelligence models have enabled significant improvements in performance across many tasks. Here, a vision-based control system is reported, coupling active learning and uncertainty awareness with a foundation model to continually learn to build a specific part better. The resulting framework is called Iterative Learning, as it improves performance by learning from its own errors during repeated build cycles of the same part. The iterative learning approach is shown to enable robust error detection and correction while being more space, time and computationally efficient compared to a naive fine-tuning approach. This provides a path showing how foundation models may be adapted to enhance reliability across a wider range of additive manufacturing processes. [Display omitted] •Iterative learning exhibits robust error correction in mass production settings.•Iterative learning does not require a reward function making it simple to deploy.•May be useful for a wider range of manufacturing processes and other robotic tasks.•Investigated why neural networks might underperform on out of distribution data.