Pullman, United States - August 21, 2024

Researchers at Washington State University (WSU) have developed a groundbreaking artificial intelligence (AI) method that significantly enhances the efficiency of 3D printing, particularly for intricate structures like artificial organs and flexible electronics. This advancement, detailed in the journal Advanced Materials Technologies, promises to streamline the production process, reducing both time and cost.

The study, led by Kaiyan Qiu, Berry Assistant Professor in the WSU School of Mechanical and Materials Engineering, and Jana Doppa, Huie-Rogers Endowed Chair Associate Professor of Computer Science, utilized an AI technique known as Bayesian Optimization. This method allowed the AI to learn and adapt, optimizing the 3D printing process by adjusting various parameters such as material choice, printer settings, and nozzle pressure.

"Each trial in 3D printing can be expensive and time-consuming due to the vast number of variable combinations," explained Doppa. "Our AI model cuts through this complexity, optimizing the process for the best outcomes."

The AI was trained to print models of human kidneys and prostates, iteratively improving the design through 60 versions. The algorithm focused on three key objectives: the precision of the model's geometry, its weight or porosity, and the printing time. Porosity is crucial for models used in surgical training, as it affects the model's mechanical properties, mimicking real human tissues.

Eric Chen, a visiting student at WSU and co-first author of the study, highlighted the AI's ability to balance multiple objectives. "We achieved the best possible printing of quality objects, regardless of the type or shape of the material," he said.

Alaleh Ahmadian, another co-first author and a graduate student in the School of Electrical Engineering and Computer Science, praised the interdisciplinary approach of the project. "It's very rewarding to see how our experiments in the lab can create real-world impact," she added.

The versatility of this AI method was demonstrated when researchers adapted the algorithm from printing a prostate model to a kidney model with minimal adjustments, showcasing its potential for broader applications in biomedical devices and beyond.

This research was supported by grants from the National Science Foundation, WSU Startup, and Cougar Cage Funds, underscoring the project's significance in advancing both academic research and practical applications in manufacturing.

The development signifies a leap forward in 3D printing technology, potentially opening new avenues in medical training, personalized medicine, and the creation of complex electronic devices, all with greater efficiency and precision.