Plans have been unveiled for a 3D-printed community of 100 new homes in the Austin area, Texas -- which would become the largest development of its kind in the United States to date when construction begins next year. The project is the result of a collaboration between real estate and homebuilding firm Lennar and ICON, a construction engineering company specialising in the development of large-scale 3D-printing technology. They are joined by the architectural firm BIG-Bjarke Ingels Group, who will be designing the houses which will be produced using so-called'additive manufacturing' -- in which objects are printed up one single layer at a time. According to ICON, their'Vulcan construction system' can produce resilient, energy-efficient homes both faster and with less waste than conventional building approaches, while also offering more freedom of design. Buildings and other structures can be built as large as 3,000 square feet using the system, they added.

The time-domain simulation is the fundamental tool for power system transient stability analysis. Accurate and reliable simulations rely on accurate dynamic component modeling. In practical power systems, dynamic component modeling has long faced the challenges of model determination and model calibration, especially with the rapid development of renewable generation and power electronics. In this paper, based on the general framework of neural ordinary differential equations (ODEs), a modified neural ODE module and a neural differential-algebraic equations (DAEs) module for power system dynamic component modeling are proposed. The modules adopt an autoencoder to raise the dimension of state variables, model the dynamics of components with artificial neural networks (ANNs), and keep the numerical integration structure. In the neural DAE module, an additional ANN is used to calculate injection currents. The neural models can be easily integrated into time-domain simulations. With datasets consisting of sampled curves of input variables and output variables, the proposed modules can be used to fulfill the tasks of parameter inference, physics-data-integrated modeling, black-box modeling, etc., and can be easily integrated into power system dynamic simulations. Some simple numerical tests are carried out in the IEEE-39 system and prove the validity and potentiality of the proposed modules.

A partnership between the Massachusetts Institute of Technology and the chemical giant BASF has managed to successfully create an AI-driven process to speed up the discovery of custom 3D printing materials. Chemists usually develop a few iterations of a material candidate over a couple of days and test them in the lab. The new machine-learning algorithm can churn out hundreds of those iterations with the desired characteristics in the same timeframe. This would save time and raw material costs, as well as lessen the environmental impact of the discarded chemicals. Not only that, but the algorithm may also come up with ideas that the material's engineer could have overlooked for various reasons.

Researchers at MIT and BASF have developed a data-driven system that accelerates the process of discovering new 3D printing materials that have multiple mechanical properties. A new machine-learning system costs less, generates less waste, and can be more innovative than manual discovery methods. The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses. To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste.

The growing popularity of 3D printing for manufacturing all sorts of items, from customized medical devices to affordable homes, has created more demand for new 3D printing materials designed for very specific uses. To cut down on the time it takes to discover these new materials, researchers at MIT have developed a data-driven process that uses machine learning to optimize new 3D printing materials with multiple characteristics, like toughness and compression strength. By streamlining materials development, the system lowers costs and lessens the environmental impact by reducing the amount of chemical waste. The machine learning algorithm could also spur innovation by suggesting unique chemical formulations that human intuition might miss. "Materials development is still very much a manual process. A chemist goes into a lab, mixes ingredients by hand, makes samples, tests them, and comes to a final formulation. But rather than having a chemist who can only do a couple of iterations over a span of days, our system can do hundreds of iterations over the same time span," says Mike Foshey, a mechanical engineer and project manager in the Computational Design and Fabrication Group (CDFG) of the Computer Science and Artificial Intelligence Laboratory (CSAIL), and co-lead author of the paper.

Author and futurist Tom Goodwin goes to London in episode three of The Edge to explore how new technologies like 3D printing, artificial intelligence and algorithms are enabling the next revolution in design. Tom meets Benjamin Hubert, the founder of Layer, a studio that combines the latest technologies with "human-centred design". He sees an example of that vision in their 3D-printed wheelchair, where each seat is custom-designed using algorithms to ensure optimal comfort and performance. Next Tom talks to Mollie Claypool, an academic turned practitioner who is working on a sustainable, modular building method, like Lego for houses. The idea is to build a whole system that can be used by communities to design and build the homes and spaces they need.

Artificial Intelligence (AI) has made significant progress in the past 5 years and is playing a more and more important role in power system analysis and control. It is foreseeable that the future power system transient stability simulations will be deeply integrated with AI. However, the existing power system dynamic simulation tools are not AI-friendly enough. In this paper, a general design of an AI-oriented power system transient stability simulator is proposed. It is a parallel simulator with a flexible application programming interface so that the simulator has rapid simulation speed, neural network supportability, and network topology accessibility. A prototype of this design is implemented and made public based on our previously realized simulator. Tests of this AI-oriented simulator are carried out under multiple scenarios, which proves that the design and implementation of the simulator are reasonable, AI-friendly, and highly efficient.

MIT has unveiled a new toolkit that lets users design health-sensing devices that can detect how muscles move. The university's Science and and Artificial Intelligence Laboratory (CSAIL) created the kit using something called "electrical impedance tomography" (EIT), that measures internal conductivity to gauge whether muscles are activated or relaxed. The research could allow for wearables that monitor distracted driving, hand gestures or muscle movements for physical rehabilitation. In a paper, the researchers wrote that EIT sensing usually requires expensive hardware setups and complex algorithms to decipher the data. The advent of 3D printing, inexpensive electronics and open-source EIT image libraries has made it feasible for more users, but designing a wearable setup is still a challenge.

Fibre jamming is a relatively new and understudied soft robotic mechanism that has previously found success when used in stiffness-tuneable arms and fingers. However, to date researchers have not fully taken advantage of the freedom offered by contemporary fabrication techniques including multi-material 3D printing in the creation of fibre jamming structures. In this research, we present a novel, modular, multi-material, 3D printed, fibre jamming tendon unit for use in a stiffness-tuneable compliant robotic ankle, or Jammkle. We describe the design and fabrication of the Jammkle and highlight its advantages compared to examples from modern literature. We develop a multiphysics model of the tendon unit, showing good agreement with experimental data. Finally, we demonstrate a practical application by integrating multiple tendon units into a robotic ankle and perform extensive testing and characterisation. We show that the Jammkle outperforms comparative leg structures in terms of compliance, damping, and slip prevention.

Bengaluru, August 27, 2021: Peekay Group has signed an agreement with Bengaluru Airport City Limited (BACL)to develop a 3D Printing technology facility at the Airport City that is being developed in the BLR Airport premises. This facility will house a production centre as well as an experience zone to learn 3D printing & ideate for innovative solutions. In addition, the facility will be used to train technology experts, up-grade skills, increase awareness of various 3D printing applications. This will play a role in engaging youngsters in this new era of technology driven manufacturing. The global 3D printing market size is estimated to reach US$ 62.79 billion by 2028 and is expected to witness a CAGR of 21.0% from 2021 to 2028.