According to a study by Washington State University, an artificial intelligence algorithm can allow researchers to use 3D printing more efficiently to manufacture intricate structures. 

The study, published in the journal Advanced Materials Technologies, could allow for more seamless use of 3D printing for complex designs in everything from artificial organs to flexible electronics and wearable biosensors.  

As part of the study, the algorithm learned to identify and then print the best versions of kidney and prostate organ models, printing 60 continually improving versions. 

Save your time, cost and labour 

“You can optimize the results, saving time, cost and labour,” said Kaiyan Qiu, co-corresponding author on the paper and Berry Assistant Professor in the WSU School of Mechanical and Materials Engineering. 

The use of 3D printing has been growing in recent years. It allows industrial engineers to quickly convert customized designs on a computer into a wide range of products, including wearable devices, batteries, and aerospace parts. 

However, developing the correct settings for their printing projects is cumbersome and inefficient for engineers. They have to decide on materials, the printer configuration, and the dispensing pressure of the nozzle, for instance—all of which affect the final product. 

“The sheer number of potential combinations is overwhelming, and each trial costs time and money,” said Jana Doppa, co-corresponding author and Huie-Rogers Endowed Chair Associate Professor of Computer Science at WSU. 

Balancing Complexity in 3D Organ Printing using Bayesian technique 

Qiu has researched for several years in developing complex, lifelike 3D-printed models of human organs. They can be used, for instance, in training surgeons or evaluating implant devices. Still, the models must include the mechanical and physical properties of the real-life organ, including veins, arteries, channels and other detailed structures. 

Qiu, Doppa, and their students used an AI technique called Bayesian Optimization to train and find the optimized 3D printing settings. Once it was trained, the researchers were able to optimize three different objectives for their organ models—the geometry precision of the model, its weight or how porous it is, and the printing time. The organ model’s porosity is important for surgery practice, for instance, because the model’s mechanical properties can change depending on its density. 

“It’s hard to balance all the objectives, but we were able to strike a favourable balance and achieve the best possible printing of a quality object, regardless of the printing type or material shape,” said co-first author Eric Chen, a WSU visiting student working in Qiu’s group in the School of Mechanical and Materials Engineering.  

Balancing Objectives for Real-World Impact 

Alaleh Ahmadian, co-first author and WSU graduate student in the School of Electrical Engineering and Computer Science, added that the researchers balanced all the objectives for favourable results and that the project benefited from its interdisciplinary perspective. 

“It is very rewarding to work on interdisciplinary research by performing physical lab experiments to create real-world impact,” she said. 

The researchers first trained the computer program to print out a surgical rehearsal model of a prostate. Because the algorithm is broadly generalizable, they could easily change it with minor tunings to print out a kidney model. 

“That means this method can be used to manufacture other more complicated biomedical devices and even other fields,” said Qiu.

Source: Washington State University