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.

Data-driven state estimation (SE) is becoming increasingly important in modern power systems, as it allows for more efficient analysis of system behaviour using real-time measurement data. This paper thoroughly evaluates a phasor measurement unit-only state estimator based on graph neural networks (GNNs) applied over factor graphs. To assess the sample efficiency of the GNN model, we perform multiple training experiments on various training set sizes. Additionally, to evaluate the scalability of the GNN model, we conduct experiments on power systems of various sizes. Our results show that the GNN-based state estimator exhibits high accuracy and efficient use of data. Additionally, it demonstrated scalability in terms of both memory usage and inference time, making it a promising solution for data-driven SE in modern power systems.

A fundamental challenge in robot perception is the coupling of the sensor pose and robot pose. This has led to research in active vision where robot pose is changed to reorient the sensor to areas of interest for perception. Further, egomotion such as jitter, and external effects such as wind and others affect perception requiring additional effort in software such as image stabilization. This effect is particularly pronounced in micro-air vehicles and micro-robots who typically are lighter and subject to larger jitter but do not have the computational capability to perform stabilization in real-time. We present a novel microelectromechanical (MEMS) mirror LiDAR system to change the field of view of the LiDAR independent of the robot motion. Our design has the potential for use on small, low-power systems where the expensive components of the LiDAR can be placed external to the small robot. We show the utility of our approach in simulation and on prototype hardware mounted on a UAV. We believe that this LiDAR and its compact movable scanning design provide mechanisms to decouple robot and sensor geometry allowing us to simplify robot perception. We also demonstrate examples of motion compensation using IMU and external odometry feedback in hardware.

Due to the growing demand for skilled labor, companies are using remote employees and constructing new facilities. Media reported that Midwest CEOs are contacting Silicon Valley tech employees affected by hiring restrictions and layoffs. Deere & Co. is the world's biggest tractor manufacturer, based in Illinois. After major IT layoffs, companies like Deere & Co. aggressively recruit computer specialists to create autonomous tractors, mining vehicles, and intelligent agriculture technology. Some firms are providing remote work and creating new offices in large cities like Austin and Chicago to entice employees who don't want to migrate to the small Midwestern towns where many enterprises are situated.

Robot-assisted 3D printing has drawn a lot of attention by its capability to fabricate curved layers that are optimized according to different objectives. However, the support generation algorithm based on a fixed printing direction for planar layers cannot be directly applied for curved layers as the orientation of material accumulation is dynamically varied. In this paper, we propose a skeleton-based support generation method for robot-assisted 3D printing with curved layers. The support is represented as an implicit solid so that the problems of numerical robustness can be effectively avoided. The effectiveness of our algorithm is verified on a dual-material printing platform that consists of a robotic arm and a newly designed dual-material extruder. Experiments have been successfully conducted on our system to fabricate a variety of freeform models.