Utilizing a Malfunctioning 3D Printer by Modeling Its Dynamics with Machine Learning
Renzo Caballero, Piotr Piękos, Eric Feron, Jürgen Schmidhuber
Abstract
To create a self-repairing 3D printer, it must continue operating even after experiencing corruption. This work focuses on developing a method to effectively utilize a malfunctioning printer for reliable printing. This method can be applied by the printer itself for self-repair and enhance the reliability of commercial 3D printers. We achieve this by modeling the dynamics of the corrupted printer using a machine learning model that by observing one trajectory infers the corrupted printer dynamics to improve its accuracy. Our method is evaluated on a digital twin of the 3D printer, demonstrating its capability to enable the printer to operate reliably, even when encountering new corruptions not encountered during training. The scripts are public on https://github.com/piotrpiekos/adaptive-printer.
BibTeX
@inproceedings{icra2024_utilizingamalfun,
title = {Utilizing a Malfunctioning 3D Printer by Modeling Its Dynamics with Machine Learning},
author = {Renzo Caballero and Piotr Piękos and Eric Feron and Jürgen Schmidhuber},
booktitle = {ICRA 2024},
year = {2024}
}