This paper presents an ongoing project that explores the usability of computer vision and deep learning to improve the quality of 3D clay printing (3DCP). One of the challenges in 3DCP is related to the nonstandard nature of the clay mixture and the environmental conditions in which the printing happens, which can result in printing failures. Manual interventions are required to adjust the printing parameters to ensure a good result. In this project, we aimed to develop an automated solution to this challenge by using computer vision and the Attention-56 deep learning network (DLN) method presented by Wang et al. (2017) and the real-time material flow control method presented by Brion and Pattinson (2022a, 2022b). Our work adapts these methods for 3DCP to adjust layer height and extrusion amount to automatically respond to changing clay mixture properties and achieve better results.

This paper presents ongoing research that aims to develop a closed-loop and real-time error
detection and correction system in 3D clay printing (3DCP) using computer vision and machine
learning. [...]