Washington State University researchers have pioneered an AI-driven method to enhance 3D printing efficiency. This breakthrough can transform manufacturing processes for artificial organs, flexible electronics and wearable biosensors, marking a significant stride in technology.
A team of researchers at Washington State University has developed an innovative artificial intelligence algorithm that significantly enhances the efficiency of 3D printing, potentially revolutionizing the way intricate structures such as artificial organs, flexible electronics and wearable biosensors are produced.
The study, published recently in the journal Advanced Materials Technologies, highlights how this AI method can optimize 3D-printing settings, making the production of complex designs more seamless and cost-effective.
Kaiyan Qiu, co-corresponding author and Berry Assistant Professor in the WSU School of Mechanical and Materials Engineering, emphasized the value of this development.
“You can optimize the results, saving time, cost and labor,” he said in a news release.
3D printing has experienced rapid growth, enabling industrial engineers to swiftly translate digital designs into physical products, spanning from wearable devices to aerospace components. However, the challenge has been the labor-intensive process of determining optimal settings for each unique printing task, involving choices related to materials, printer configuration and nozzle dispensing pressure.
“The sheer number of potential combinations is overwhelming, and each trial costs time and money,” Jana Doppa, co-corresponding author and Huie-Rogers Endowed Chair Associate Professor of Computer Science at WSU, said in the news release.
For years, Qiu’s research has focused on creating lifelike 3D-printed models of human organs to aid in surgical training and implant device evaluation. These models must replicate the intricate anatomical features and mechanical properties of real organs, such as veins and arteries.
In the new study, Qiu, Doppa and their team used an AI technique known as Bayesian Optimization. This allowed their algorithm to learn and identify the best printing parameters, ultimately producing 60 progressively improved versions of kidney and prostate organ models.
“That means that this method can be used to manufacture other more complicated biomedical devices, and even to other fields,” added Qiu.