Biomedical engineers at Queensland University of Technology (QUT) have introduced an innovative approach to speed up progress in melt electrowriting (MEW), an advanced 3D printing technology essential for tissue engineering and regenerative medicine.
By integrating machine learning (ML) into the MEW process, researchers have created a system capable of overcoming longstanding challenges in this field, the university reported in a news release.
Dr Pawel Mieszczanek, a key contributor to the study and a graduate of the ARC Training Centre in Additive Biomanufacturing at QUT, emphasised the transformative potential of the new method.
“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.
Distinguished Professor Dietmar Hutmacher, director of the Max Planck Queensland Centre for the Materials Science of Extracellular Matrices at QUT, highlighted the time-saving capabilities of the automated system.
“We use a feedforward neural network, optimization techniques, and feedback loop to ensure that printed parts are consistently reproducible,” the professor noted.
“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 included Dr Mieszczanek, Distinguished Emeritus Professor Peter Corke, and Professor Hutmacher from QUT, along with Professor Courosh Mehanian and Associate Professor Paul Dalton from the University of Oregon.
The study, titled Towards industry-ready additive manufacturing: AI-enabled closed-loop control for 3D melt electrowriting, is published in Communications Engineering.
