3D Printing News Briefs, June 29, 2024: AI Machine Learning, Sensory Garden, Hard Hats, & More

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In 3D Printing News Briefs today, we’re starting with Desktop Metal’s new PureSinter furnace. Then it’s on to research about a variable binder amount algorithm and adaptive slicing, a 3D printing robot that uses AI machine learning, and a 3D bioprinted liver model for evaluating toxicity effects. Finally, WASP 3D printed ceramic structures for a flower show’s sensory garden, and NUMO showcased its 3D printed hard hats and helmets in patriotic colors.

Desktop Metal Debuts PureSinter Furnace at RAPID+TCT

The PureSinter Furnace horizontal shelving system is made of high-purity graphite and features seven shelves in a 15.8L retort.

This week at RAPID+TCT 2024 in Los Angeles, Desktop Metal had the global debut of its new PureSinter, a high-purity vacuum furnace that offers one-run debinding and sintering of powder metal parts. The company has been developing this machine for five years to take on the challenge of sintering furnaces that are easily contaminated by hydrocarbons and other waste emitted by powdered metal parts. The PureSinter was designed for reliability, ease of use, high performance, and low cost of operation. Its patent-pending design features more than 17 fans and a pop-out ceiling vent, so it’s able to cool from 1,420°C to 200°C in less than four hours without the use of costly water-cooled walls. The PureSinter also has an efficient footprint, toughscreen controls, an automated thermal hood lift, and excellent visibility inside its 15.8L retort. The system delivers parts-per-billion levels of purity, is compatible with both traditional and additive manufacturing methods, and has already been installed at FreeFORM Technologies.

“Rather than trying to simply mitigate the factors that lead to poor performance in an all-in-one debinding and sintering furnace, we have eliminated them with an innovative all-new design. This is the first product from Desktop Metal aimed at manufacturers using both Additive Manufacturing and traditional manufacturing methods,” said Ric Fulop, Founder and CEO of Desktop Metal.

“We have put the PureSinter through a prolonged period of testing to rigorously verify our new design, and it has exceeded all expectations. PureSinter is an exemplary demonstration of the innovation for which Desktop Metal and our engineers are known. We believe this furnace will revolutionize sinter-based AM and the traditional furnace industry.”

Standard shipments of the PureSinter Furnace, now validated with 14 metal powder and binder combinations, should begin in Q3 of 2024.

Turkish Researchers Optimizing Surface Roughness in Binder Jetting

Last year, a team of researchers from Ondokuz Mayıs University in Turkey published a study about increasing the production speed in binder jet 3D printing through the use of adaptive slicing methods, as well as a variable binder amount algorithm. As PhD researcher Hasan Baş explained in an email, their findings demonstrated definite improvements in both surface quality and optimization of production time. Baş reached out again recently to inform us that the team advanced their initial research, focusing on using the Taguchi method and image processing techniques to optimize surface roughness. Also, adaptive slicing was again used to increase production speed, and the team “demonstrated the applicability of these methods to industrial binder jetting printers.”

“Adaptive slicing and variable binder amount algorithm (VBAA) were used to increase manufacturing speed in binder jetting. Taguchi method was used to optimize the layer thickness and saturation ratio in VBAA. According to the Taguchi experimental design, 27 samples were produced in nine different conditions, three replicates each. The width of the samples in their raw form was measured. Afterward, the samples were sintered at 1,500 °C for 2 h. After sintering, surface roughness and density tests were performed. Therefore, the methods used have been proven to be successful. In addition, measurement possibilities with image processing were investigated to make surface roughness measurements more accessible and more economical,” the researchers wrote in their new study.

Autonomous 3D Printing Robot Using AI Machine Learning

Figure 2: a) Each maximum energy absorbing efficiency Ks* measured over the first ~21,500 experiments performed. Pictures highlight noteworthy components (black stars) and the highest-performing structure (red star). Larger versions of images are included as Figure S11. The color of the pictured components is indicative of the material used, with Green indicating PLA, Blue indicating PETG, and Red/Gray indicating different types of TPU. The solid blue line denotes the running best Ks* observed. b) Effective medium stress σ vs. effective medium strain ε for experiment 21,285, named Palm, which resulted in Ks* = 75.2%. Inset photographs show the state of the component at various points indicated on the curve (images enhanced to improve clarity – originals given as Figure S12). Shading denotes regions used to compute Ks* as described in Fig. 1a.

An automated experiment has been going on at the KABlab at Boston University since 2021, run by Keith Brown, an ENG associate professor of mechanical engineering, and his team. An autonomous robot arm, called MAMA BEAR (Mechanics of Additively Manufactured Architectures Bayesian Experimental Autonomous Researcher), continuously 3D prints small, plastic parts, and then records their size and shape, moves them to a flat metal plate, and crushes them. It measures and records how much energy the structure absorbed, and how its shape changed, before dropping it into a box on the floor, cleaning the plate, and printing another piece, which will be just a little different than the last. MAMA BEAR does all of this through AI machine learning, and each time, the structure gets better at absorbing its crushing impact. Recently, the team watched the robot reach 75% efficiency—breaking the record of 71%—with a 3D printed structure featuring four points, shaped like flower petals, that’s taller and narrower than early designs.

“Here, we explore the energy absorbing efficiency of additively manufactured polymer structures by using a self-driving lab (SDL) to perform >25,000 physical experiments on generalized cylindrical shells. We use a human-SDL collaborative approach where experiments are selected from over trillions of candidates in an 11-dimensional parameter space using Bayesian optimization and then automatically performed while the human team monitors progress to periodically modify aspects of the system. The result of this human-SDL campaign is the discovery of a structure with a 75.2% energy absorbing efficiency and a library of experimental data that reveals transferable principles for designing tough structures,” the researchers wrote in the abstract of their published paper.

Some of the team members are now working with the U.S. Army to use their data to inform the design of new helmet padding for soldiers.

3D Bioprinted Cellular Liver Models for Measuring Toxicity Effects

Figure 1. BAB400 3D printing and dispensing workflow integrated with Imager for endpoint assays. Image Credit: Molecular Devices UK Ltd

Speaking of robots, Advanced Solutions and Molecular Devices worked together to develop a method for automated generation of 3D cellular liver models, in order to test and evaluate the toxicity effects of various compounds. This was accomplished using the BioAssemblyBot 400 (BAB400), a cGMP-certified multi-tool bioprinting robotic device with liquid handling. The BAB400 used tissue structure information modeling (TSIM) software to design 3D structures for bioprinting, and the robot also automated dispensing, imaging, and maintenance of the 3D models. The ImageXpress Micro Confocal HighContent Imaging System was used to gain transmitted light (TL) and fluorescent images. Using the system’s confocal mode, Z-stack images of liver cells within a collagen matrix were acquired with 4X or 10X objectives for the liver tissue models.

Ultimately, the BAB400 and TSIM software were able to develop 3D HepG2 human liver cancer cell models in 96 well formats, which were then cultured and monitored daily. After four days, several of the well plates were treated with multiple drugs for 48 hours and the cells were stained and analyzed. These were compared with 2D HepG2 liver cancer cell models. After comparing the 2D and 3D HepG2 assays, compound effects were found at lower concentrations in 2D systems than in 3D. The results demonstrate that it is valuable to use an automated method, like the BAB400, in forming liver 3D bioprinted models, and also found benefits to using imaging and data analysis methods and descriptors to learn more about the complex compound effects in both types of cell models.

3D Printed Sensory Ceramic Garden for World Child Cancer UK

RHS Chelsea Flower Show Giulio Giorgi WASP. Image provided by Telegraph, Guy Bell/Alamy.

Italian landscape designer and gardener Giulio Giorgi wanted to create something very special for the 2024 RHS Chelsea Flower Show. In collaboration with World Child Cancer UK and WASP, and with funding from Project Giving Back, they built “World Child Cancer’s Nurturing Garden,” a 3D printed ceramic sensory garden to support the emotional wellbeing of young cancer patients and their families. Giorgi wanted a garden that could be easily replicated, and was adaptable to a variety of environments and climates, so it could be reproduced in other hospitals. Giorgi and Professor Giuseppe Fallacara of the Polytechnic University of Bari designed a stackable ceramic module for ultimate customization, and WASP’s clay 3D printing farm—using the automated WASP 40100 Production system—printed a series of the modules with personalized designs. Once these were complete, Landesigns Ltd assembled the garden, which also ended up winning the gold medal in sustainability at the RHS Chelsea Flower Show.

Malcolm Anderson, the Royal Horticultural Society’s head of sustainability, said, “The garden has been made using products made entirely from soil and timber and in its construction no power tools have been used, only hand tools, so it is a fine example of how we can design and build gardens more sustainably in the future.”

NUMO’s 3D Printed Hard Hats and Urban Helmets

Finally, on Memorial Day in the U.S., Numorpho Cybernetic Systems (NUMO) showcased its 3D printed hard hats and urban helmets in red and blue in honor of “the uncountable lives of our front-line soldiers that have made our country free and safe.” The company, which got its start at mHUB in Chicago, builds smart, connected, and sustainable products that can enhance our current capabilities, enable living beyond our constraints, and even eventually evolving features that will make systems more aware, coordinated, and intelligent. They used ABS, which is stronger and impact resistant, to print the blue helmets, and PETG, more flexible and shatter-resistant, to print the red ones. In one of the comments on his LinkedIn post, NUMO’s Founder and CEO Nitin Uchil said the helmets are customizable, and that the company is working out how to take point cloud data from a head scan and “convert it to get the 42 control points that are needed to define the custom shape so that we can fit it for anyone.”

“When doing impact testing for helmets, it not just how strong the material is, but also how it muffles the shock of the blow and prevents or minimizes injury to the head,” Uchil wrote on LinkedIn. “It also depends on the foam/flexible padding, the fit of the retainers and suspensions, and the chin strap that would prevent the detachment of the helmet on impact that play key factors in safety. We will also be conducting digital simulations of the impact test to see if we can modify the infill lattice structure to have regions of variable density to match up with the needs to resist impact while optimizing the weight of the helmet.”

The company plans to move soon to the next phase of commercialization for its helmets: small batch manufacturing and test validation for safety standard certifications.

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