When it comes to 3D printing materials, from metals and self-folding plastics to biomaterials like soft tissue, the researchers at Carnegie Mellon University (CMU) know their stuff. Now, a research team from the university’s College of Engineering has created a novel approach to optimizing soft material 3D printing, which can be tricky due to the fact that many parameters can affect the final product, such as the consistency of the gel bath an object is printed in, how fast the 3D print head moves, and the concentrations of each material.
Most experimental designs or optimization models will focus on the few parameters deemed most important to the particular print, but it can be tough to adapt these models for experimental materials, as the 3D printing characteristics are often not well-known.
“When 3-D printing thermoplastics, if you have just five or 10 main print parameters and want to explore, say, five levels of each, a factorial design can result in millions of possible combinations of settings to print. The combinations become even more daunting when exploring an experimental material whose print characteristics are unknown,” said Sara Abdollahi, a CMU biomedical engineering PhD student. “For example, if the experimental material has 20 print parameters with five levels, the experimenter can have trillions of combinations of print settings to explore.”
The research team, which consists of Abdollahi, assistant professor of engineering and public policy Alexander Davis, CMU’s Dietrich College of Humanities and Social Sciences Professor John H. Miller, and Adam Feinberg, associate professor of biomedical engineering and materials science and engineering, published a paper on their work, titled “Expert-guided optimization for 3D printing of soft and liquid materials,” in PLOS One. The paper demonstrates their new Expert-Guided Optimization (EGO) method, which was designed to optimize high quality, soft material 3D prints.
The abstract reads, “Here, we developed an expert-guided optimization (EGO) strategy to provide structure in exploring and improving the 3D printing of liquid polydimethylsiloxane (PDMS) elastomer resin. EGO uses three steps, starting first with expert screening to select the parameter space, factors, and factor levels. Second is a hill-climbing algorithm to search the parameter space defined by the expert for the best set of parameters. Third is expert decision making to try new factors or a new parameter space to improve on the best current solution. We applied the algorithm to two calibration objects, a hollow cylinder and a five-sided hollow cube that were evaluated based on a multi-factor scoring system. The optimum print settings were then used to print complex PDMS and epoxy 3D objects, including a twisted vase, water drop, toe, and ear, at a level of detail and fidelity previously not obtained.”
The researchers use a 3D printing method known as freeform reversible embedding (FRE), where soft materials are deposited in a gel support bath. As previously mentioned, while typical models and designs only focus on a few select 3D printing parameters, the EGO method can quickly and efficiently rule out any ineffective combinations. It pairs expert judgment with an efficient optimization algorithm to find combinations that will result in optimal, high-fidelity 3D prints for experimental, soft materials.
For the purposes of the paper, the researchers demonstrated their EGO method using liquid polydimethylsiloxane (PDMS) elastomer resin. PDMS is also known as silicone rubber, and is often used in medical devices and wearable sensors.
The team’s innovative EGO method could even extend beyond 3D printing soft materials to multiple engineering processes, and has the potential to be used as a systematic tool for discovering important parameters that lead to high-quality, reproducible, novel materials.
Davis explained, “The purpose of EGO is to create an effective search algorithm that explicitly combines both expert knowledge and traditional search algorithms. Typically we think of machine learning being useful for big data, but EGO works in situations when we have little or no data and need to rely on expert judgment, then through a combination of search algorithms and the expert’s knowledge, effectively transition from small to big data.”
The EGO model is made up of three steps, starting with a human expert choosing the initial set of parameters – this gives the algorithm its search boundaries. A hill-climbing algorithm then searches within these boundaries for any positive combinations of the selected parameters, which will result in a local optimum.
Then, the expert will evaluate this local optimum, and determine whether or not to change the search process by adding additional parameters, or to keep searching within the original boundaries. This three-step process will repeat until the algorithm finds an ideal solution.
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