While 3D printing soft materials, such as silicone or proteins, offers many advantages, it also introduces many new and complicated variables to consider when creating a new part, per Vanesa Listek for 3D Print in her report featuring the work of McGowan Institute for Regenerative Medicine affiliated faculty member Newell Washburn, PhD, professor of biomedical engineering and chemistry at Carnegie Mellon University (CMU). The existing soft materials that can be 3D printed commercially are somewhat limited since they don’t have all the properties that researchers need to fully advance their developments and they end up working within the constraints of the current technology.
One of the main problems with 3D printing a soft material is that it tends to deform under the forces that normally occur, sometimes even during the build, so they require support materials. According to researchers from the College of Engineering at CMU, that means that additive manufacturing of soft materials requires optimization of printable inks, formulations of these feedstocks, and complex printing processes that must balance a large number of disparate but highly correlated variables (such as metal powder particle size, melt pool shape and size or filament feeding rate, extrusion width, linear plotting speed and layer thickness or suspension viscosity). Due to the critical need for integrated methodologies, the researchers have come up with a hierarchical machine learning (HML) algorithm that optimizes parameters of these type of materials for 3D printing, using Freeform Reversible Embedding (FRE)–a recently developed method for 3D printing of liquid polymer precursors that involves controlled deposition of a fluid precursor into a supporting aqueous bath.
The team, led by Dr.Washburn, who along with colleagues from CMU including Adam Feinberg, PhD, associate professor of biomedical engineering and materials science and engineering (and an affiliated faculty member of the McGowan Institute); Barnabas Poczos, professor of machine learning at the School of Computer Science; and materials science and engineering doctoral student Aditya Menon, recently published a paper on their work, titled “Optimization of Silicone 3D Printing with Hierarchical Machine Learning,” in 3D Printing and Additive Manufacturing. The paper demonstrates how their new algorithm was designed to optimize high quality, soft material 3D prints.
“Increasingly, we’re starting to make more soft material-based components and devices for low-tech areas such as sensors and medical applications, and for high tech areas like soft robotics,” said Dr. Washburn. “But every time you add another component you exponentially raise the complexity of the manufacturing process. That’s where a machine learning algorithm could really help with optimization.”
Machine learning algorithms, which are a type of artificial intelligence algorithm, are designed to develop a relationship between the input variables and the outputs of a complex system based on training data. According to researchers at CMU, traditional algorithms in machine learning tools generally treat systems as black boxes and then attempt to define a response surface based on statistical analysis. This then requires extensive data sets to train these models, which can be difficult to generate, especially given the number of variables and time required to probe experimental processing parameters. But for the complex problem that the research team is working on, there isn’t a lot of data. Therefore, to account for this, Dr. Washburn and his team custom-built an HML algorithm that incorporates expert knowledge about how the physical systems and parameters operate with limited data available. Dr. Washburn assures that HML uses knowledge of the underlying physical system—in this case, the physics of the FRE 3D printing variables—to draw connections between the right variables, significantly cutting time and the amount of data needed.
“This new machine-learning algorithm is a powerful tool that we have designed to use in our research work for complex physical systems, it uses very small data sets allowing us to optimize their performance. For the 3D printing projects, the material and the technologies we are developing will allow us to optimize the 3D printing of entire organs where we are applying a complex mixture of components, materials, and cells. The overall goal of our research is to design materials for function and not just composition, leveraging the understanding of how these materials work,” Dr. Washburn claims.
The researchers were able to print 2.5 times faster, print with ink that previously didn’t work well, and the algorithm also identified a unique silicone formulation and printing parameters that had not been found previously through trial-and-error approaches. With these results, the researchers—some of whom are affiliated with both the Manufacturing Futures Initiative and the Bioengineered Organs Initiative—are optimistic that HML will have a broad application for planning and optimizing in the AM of many types of soft materials, such as for biomedical applications via the FRE method.
Abstract (Optimization of silicone 3d printing with hierarchical machine learning. Aditya Menon, Barnabás Póczos, Adam W. Feinberg, and Newell R. Washburn. 3D Printing and Additive Manufacturing, Vol. 6, No. 4, Published Online:14 Aug 2019.)