. 24/7 Space News .
Combining experts and automation in 3D printing
by Staff Writers
Pittsburgh PA (SPX) Jun 18, 2018

Images of the PDMS 3D prints made using the S3D CAD slicer to determine toolpath.

Researchers in Carnegie Mellon University's College of Engineering have developed a novel approach to optimizing soft material 3-D printing. The researchers' Expert-Guided Optimization (EGO) method combines expert judgment with an optimization algorithm that efficiently searches combinations of parameters relevant for 3-D printing, enabling high-fidelity soft material products to be printed.

The researchers - which include lead author Sara Abdollahi, a Ph.D. student in biomedical engineering; Adam Feinberg, associate professor of biomedical engineering and materials science and engineering; Alex Davis, assistant professor of engineering and public policy; and Dietrich College of Humanities and Social Sciences Professor John Miller - designed the EGO method to optimize high quality 3-D prints of soft materials.

In their paper, "Expert-guided optimization for 3D printing of soft and liquid materials," which was recently published in PLOS One, the researchers demonstrate the EGO method using liquid polydimethylsiloxane (PDMS) elastomer resin, a material often used in wearable sensors and medical devices. The researchers used a printing method called freeform reversible embedding (FRE), in which soft materials are deposited within a gel support bath.

When it comes to 3-D printing soft materials, many parameters can affect the final product. How fast the head of the 3-D printer moves, the consistency of the gel bath in which the product is printed, and the concentrations of each material in the print are just a few of the variables that can affect the final product. In each print, there can be dozens of parameters to take into account, and many more possible combinations of them.

A typical optimization model or experimental design will focus on a few parameters that are considered most important to the print. However, adapting these optimization models for experimental materials, whose 3-D printing characteristics aren't well known, can be extremely challenging.

"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," says Abdollahi.

"The combinations become even more daunting when exploring an experimental material whose print characteristics are unknown. 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."

However, with the EGO model, this challenge can be made less of an obstacle because experts are able to rule out many combinations as being ineffective. By combining an expert's scientific judgment with efficient search algorithms, EGO significantly reduces the time and energy required to find combinations that yield optimal 3-D prints for experimental materials.

"The purpose of EGO is to create an effective search algorithm that explicitly combines both expert knowledge and traditional search algorithms," says Davis. "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 comprised of three steps. First, a human expert selects the initial set of parameters, giving the algorithm the boundaries for search. Then, a hill climbing algorithm searches within those boundaries for promising combinations of those parameters, resulting in a "local optimum."

Finally, the expert evaluates the local optimum and decides whether to alter the search process by adding new parameters, or continue to search within the existing boundaries. The process iterates until an ideal solution is found.

The EGO method, which can extend beyond the 3-D printing of soft materials for a variety of engineering processes, has great potential as a systematic tool to discover the key parameters that yield reproducible, high-quality, novel materials.

Research Report: "Expert-guided optimization for 3D printing of soft and liquid materials"

Related Links
College of Engineering, Carnegie Mellon University
Space Technology News - Applications and Research

Thanks for being there;
We need your help. The SpaceDaily news network continues to grow but revenues have never been harder to maintain.

With the rise of Ad Blockers, and Facebook - our traditional revenue sources via quality network advertising continues to decline. And unlike so many other news sites, we don't have a paywall - with those annoying usernames and passwords.

Our news coverage takes time and effort to publish 365 days a year.

If you find our news sites informative and useful then please consider becoming a regular supporter or for now make a one off contribution.
SpaceDaily Monthly Supporter
$5+ Billed Monthly

paypal only
SpaceDaily Contributor
$5 Billed Once

credit card or paypal

Shapeshifting minibots printed with 3-D 'ink'
Paris (AFP) June 13, 2018
Engineers have created a soft, malleable 3-D "ink" to print devices that can roll, jump, even grasp objects at the wave of a magnet, they said on Wednesday. The shape-shifting material holds promise for flexible robotics and medicine, said the researchers, mooting tiny devices that can envelop a drug, transport it through the body, and unfold to release it where needed. A team of US-based researchers made the new type of 3-D printing ink by mixing magnetic iron particles with soft, silicone rubb ... read more

Comment using your Disqus, Facebook, Google or Twitter login.

Share this article via these popular social media networks
del.icio.usdel.icio.us DiggDigg RedditReddit GoogleGoogle

Astronaut Sally Ride's legacy of encouraging young women to embrace science and engineering

Space tourism not far off, rocket maker says

Space Station Roulette

Peggy Whitson, NASA's most experienced astronaut, retires

S7 space mulls restoring production of heavy rocket engines in Russia

Russia to deliver US new rocket engines

Arianegroup tests innovative technology for next generation upper stage rocket engine

ESA Council commits to Ariane 6 and transition from Ariane 5

Explosive volcanoes spawned mysterious Martian rock formation

Unique microbe could thrive on Mars, help future manned missions

NASA spacecraft studying massive Martian dust storm

Opportunity rover sends transmission amid Martian dust storm

China confirms reception of data from Gaofen-6 satellite

Experts Explain How China Is Opening International Space Cooperation

Beijing welcomes use of Chinese space station by all UN Nations

China upgrades spacecraft reentry and descent technology

GomSpace and Aerial Maritime Ltd enter MOU for delivery and operation of a global constellation

Forget Galileo - UK space sector should look to young stars instead

US FCC expands market access for SES O3b MEO constellation

Liftoff as Alexander Gerst returns to space

Physicists discover how to create the thinnest liquid films ever

Combining experts and automation in 3D printing

Reaktor Space Lab and VTT investigate a new frequency band for telecommunications satellites

The right chemistry, fast: employing AI and Automation to map out and make molecules

Astronomers identify 121 giant planets likely to host habitable moons

Hawking plea 'to save planet' beamed to black hole

Study could help humans colonise Mars and hunt for alien life

Chandra Scouts Nearest Star System for Possible Hazards

A dark and stormy Jupiter

NASA shares more Pluto images from New Horizons

Juno Solves 39-Year Old Mystery of Jupiter Lightning

NASA Re-plans Juno's Jupiter Mission

The content herein, unless otherwise known to be public domain, are Copyright 1995-2024 - Space Media Network. All websites are published in Australia and are solely subject to Australian law and governed by Fair Use principals for news reporting and research purposes. AFP, UPI and IANS news wire stories are copyright Agence France-Presse, United Press International and Indo-Asia News Service. ESA news reports are copyright European Space Agency. All NASA sourced material is public domain. Additional copyrights may apply in whole or part to other bona fide parties. All articles labeled "by Staff Writers" include reports supplied to Space Media Network by industry news wires, PR agencies, corporate press officers and the like. Such articles are individually curated and edited by Space Media Network staff on the basis of the report's information value to our industry and professional readership. Advertising does not imply endorsement, agreement or approval of any opinions, statements or information provided by Space Media Network on any Web page published or hosted by Space Media Network. General Data Protection Regulation (GDPR) Statement Our advertisers use various cookies and the like to deliver the best ad banner available at one time. All network advertising suppliers have GDPR policies (Legitimate Interest) that conform with EU regulations for data collection. By using our websites you consent to cookie based advertising. If you do not agree with this then you must stop using the websites from May 25, 2018. Privacy Statement. Additional information can be found here at About Us.