Media release

From: Queensland University of Technology (QUT)

First author Dr Pawel Mieszczanek, who did his PhD in the ARC Training Centre in Additive Biomanufacturing at QUT, said the researchers’ method would enable faster advancement of melt electrowriting (MEW) technology.

“MEW is a multifaceted 3D printing technology that also has applications in bioengineering, biomaterials science, and soft robotics,” Dr Mieszczanek said.

“However, it has faced many challenges from its early stages more than 10 years ago to its current stage, hampered by long experimentation times, low printing speeds, poor consistency in results, and dependence on the user for printer operation.

“To address these problems, we used machine learning (ML) to create a closed-loop process control system for MEW.

“The novel MEW system design is effective because it monitors the fibre-flight pass, allowing us to use real-time imaging for continuous analysis.”

Distinguished Professor Dietmar W. Hutmacher, director of the Max Planck Queensland Centre (MPQC) for the Materials Science of Extracellular Matrices, based at QUT, said the new automated data collection system reduced the experimental time to hours instead of days and weeks.

“We use a feedforward neural network, optimization techniques, and feedback loop to ensure that printed parts are consistently reproducible.

“This work shows that machine learning can automate MEW operations and support the engineering of effective closed-loop control in complex 3D printing technology.”

The research team comprised: Dr Pawel Mieszczanek, Distinguished Emeritus Professor Peter Corke, Distinguished Professor W. Hutmacher, all from QUT; Professor Courosh Mehanian and Associate Professor Paul D. Dalton from the University of Oregon.

The study, Towards industry-ready additive manufacturing: AI-enabled closed-loop control for 3D melt electrowriting was published in Communications Engineering.