Spider webs are incredible biological structures, comprising thin but strong silk filament and arranged into complex hierarchical architectures with striking mechanical properties (e.g., lightweight but high strength, achieving diverse mechanical responses). While simple 2D orb webs can easily be mimicked, the modeling and synthesis of 3D-based web structures remain challenging, partly due to the rich set of design features. Here we provide a detailed analysis of the heterogenous graph structures of spider webs, and use deep learning as a way to model and then synthesize artificial, bio-inspired 3D web structures. The generative AI models are conditioned based on key geometric parameters (including average edge length, number of nodes, average node degree, and others). To identify graph construction principles, we use inductive representation sampling of large experimentally determined spider web graphs, to yield a dataset that is used to train three conditional generative models: 1) An analog diffusion model inspired by nonequilibrium thermodynamics, with sparse neighbor representation, 2) a discrete diffusion model with full neighbor representation, and 3) an autoregressive transformer architecture with full neighbor representation. All three models are scalable, produce complex, de novo bio-inspired spider web mimics, and successfully construct graphs that meet the design objectives. We further propose algorithm that assembles web samples produced by the generative models into larger-scale structures based on a series of geometric design targets, including helical and parametric shapes, mimicking, and extending natural design principles towards integration with diverging engineering objectives. Several webs are manufactured using 3D printing and tested to assess mechanical properties.

Generative AI models are all the rage these days thanks in large part to OpenAI and their latest gpt-3 and gpt-4 models. Seemingly everyone has heard of their now famous and shockingly human-like chatgpt interface. Even my grandmother has tried it out and she still has a corded home phone and sends me emails from her @hotmail.com The rapid pace of development around these large language models (LLMs) has been nothing short of incredible. ChatGPT recently broke the record as the fastest-growing consumer application in history, hitting 100 million users in its first two months.

With RunwayML, you can create and experiment with generative models in a matter of minutes, without having to write a single line of code" (RunwayML Website). Demand for innovation: Generative AI has opened up new possibilities and opportunities for innovation in various fields and industries. Generative AI can help to generate new ideas, designs, products, services, etc. that can solve problems or meet needs. In manufacturing, Autodesk and Creo use generative AI to design physical objects. In some cases, they also create those objects through 3D printing or computer-controlled machining and additive manufacturing. NVIDIA's set an example through GauGAN, an AI-powered tool that can transform rough sketches into photorealistic images in real-time. "GauGAN represents a major breakthrough in AI-powered image creation, opening up new possibilities for artists, designers, and creatives.

Recently renamed Meta, Facebook is one of the undisputed giants of emerging technologies, whether in the fields of virtual reality, augmented reality, artificial intelligence or even additive manufacturing. Indeed, the group is progressively advancing in this market, slowly but surely. Notably, the American giant already announced the acquisition of Luxexcel a few months ago. This time, Meta has decided to combine all this expertise to announce a new and somewhat… surprising project. Mark Zuckerberg, founder and CEO of the former Facebook, declared in an official press release that the company has 3D printed a robot equipped with OpenAI's GPT-5 to sit on the board of directors and support it in its strategic decisions.

An additive manufacturing (AM) process, like laser powder bed fusion, allows for the fabrication of objects by spreading and melting powder in layers until a freeform part shape is created. In order to improve the properties of the material involved in the AM process, it is important to predict the material characterization property as a function of the processing conditions. In thermoelectric materials, the power factor is a measure of how efficiently the material can convert heat to electricity. While earlier works have predicted the material characterization properties of different thermoelectric materials using various techniques, implementation of machine learning models to predict the power factor of bismuth telluride (Bi2Te3) during the AM process has not been explored. This is important as Bi2Te3 is a standard material for low temperature applications. Thus, we used data about manufacturing processing parameters involved and in-situ sensor monitoring data collected during AM of Bi2Te3, to train different machine learning models in order to predict its thermoelectric power factor. We implemented supervised machine learning techniques using 80% training and 20% test data and further used the permutation feature importance method to identify important processing parameters and in-situ sensor features which were best at predicting power factor of the material. Ensemble-based methods like random forest, AdaBoost classifier, and bagging classifier performed the best in predicting power factor with the highest accuracy of 90% achieved by the bagging classifier model. Additionally, we found the top 15 processing parameters and in-situ sensor features to characterize the material manufacturing property like power factor. These features could further be optimized to maximize power factor of the thermoelectric material and improve the quality of the products built using this material.

Researchers at the University of New South Wales, Sydney, have developed a flexible 3D bioprinter that can layer organic material directly onto organs or tissue. Unlike other bioprinting approaches, this system would only be minimally invasive, perhaps helping to avoid major surgeries or the removal of organs. It sounds like the future -- at least in theory -- but the research team warns it's still five to seven years away from human testing. The printer, dubbed F3DB, has a soft robotic arm that can assemble biomaterials with living cells onto damaged internal organs or tissues. Its snake-like flexible body would enter the body through the mouth or anus, with a pilot / surgeon guiding it toward the injured area using hand gestures.

Integrating sensors into rotational mechanisms could make it possible for engineers to build smart hinges that know when a door has been opened, or gears inside a motor that tell a mechanic how fast they are rotating. MIT engineers have now developed a way to easily integrate sensors into these types of mechanisms, with 3D printing. Even though advances in 3D printing enable rapid fabrication of rotational mechanisms, integrating sensors into the designs is still notoriously difficult. Due to the complexity of the rotating parts, sensors are typically embedded manually, after the device has already been produced. However, manually integrating sensors is no easy task.

Hall effect thrusters are one of the most versatile and popular electric propulsion systems for space use. Industry trends towards interplanetary missions arise advances in design development of such propulsion systems. It is understood that correct sizing of discharge channel in Hall effect thruster impact performance greatly. Since the complete physics model of such propulsion system is not yet optimized for fast computations and design iterations, most thrusters are being designed using so-called scaling laws. But this work focuses on rather novel approach, which is outlined less frequently than ordinary scaling design approach in literature. Using deep machine learning it is possible to create predictive performance model, which can be used to effortlessly get design of required hall thruster with required characteristics using way less computational power than design from scratch and way more flexible than usual scaling approach.

An almost entirely 3D-printed rocket is ready to blast off from Cape Canaveral, Florida, then head for low Earth orbit. Scheduled for a three-hour launch window that opens at 1 pm Eastern time tomorrow, the inaugural launch of Relativity Space's Terran 1 rocket will constitute a major milestone for the California-based startup, and for expanding the use of 3D printing in the space industry. Relativity and similar companies envision ultimately using the technology to construct tools, spacecraft, and infrastructure while in orbit, on the moon, or on Mars--in those cases, utilizing lunar and Martian dirt for building materials. But first, company engineers want to see how Terran 1 fares on this crucial test flight, an event the company has dubbed "Good Luck, Have Fun." "The number one goal for our rocket is to collect as much data as possible and learn as much as possible from the flight," says senior vice president Josh Brost. He and his colleagues will be closely watching its path through the stratosphere as it reaches a trajectory point called "max q" about a minute after launch, when intense dynamic pressure will put stresses on rocket.

A 3D-printed rocket built by California-based startup Relativity Space was due for blastoff on its first mission to orbit on Wednesday in a key test of the US company's novel strategy for cutting manufacturing costs. The 35-metre-tall (115-foot) Terran 1 rocket, 85 percent of which was fabricated from a 3D printer, was set to lift off from a United States Space Force base launch pad in Cape Canaveral, Florida at 1 pm Eastern time (18:00 GMT) on Wednesday. "The launch that we're preparing for is an opportunity to demonstrate a whole bunch of things all at once," said Josh Brost, Relativity Space's senior vice president of revenue. He called the Terran 1 "by far the largest 3D-printed structure that's ever been assembled". The rocket – nicknamed GLHF for "Good Luck, Have Fun" – will not carry a commercial payload, as it is an inaugural flight, but will instead carry a failed 3D-printed rocket part from a previous attempt to build a craft.