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.

We analyze and model a belt transmission system with non-circular timing pulleys. Using a 3D printer as a proof-of-concept device, experiments consisting of tracking the pose data of a printer nozzle and its pulleys are conducted. A computational model from our previous work is validated with the experimental data and expanded to model more complex systems with multiple non-circular timing pulleys as well as slippage and non-ideal tensions. Finally, an example with two non-circular timing pulleys is presented and simulated utilizing the proposed method.

This work presents a novel amplifier architecture which is the input stage of an analog front end targeting the acquisition of biological signals with low voltage supply (1.2 V), low noise, high Common Mode Rejection Ratio (CMRR) and high current efficiency. A prototype, designed and fabricated in a 130 nm CMOS technology, was characterized by simulations and measurements. Our preamplifier presents one of the lowest noise levels reported up-to-date (over the considered bandwidth) while presenting a very competitive performance in other important features. Results from measurements show a bandwidth from 20 Hz to 11 kHz, a CMRR higher than 70 dB, an equivalent input-referred noise as low as 1.3 μVrms. The Noise Efficiency Factor (NEF) at 2.5 and Power Efficiency Factor (PEF) at 7.5 are remarkable results.