Relativity Space, a California-based company that can 3D print an entire rocket and can build large metal 3D printers, has now secured $300 million in a Series D funding round. Relativity Space is founded by Tim Ellis in the year 2015. It combines 3D printing, autonomous robotics, and Artificial Intelligence to build a rocket in less than 60 days. The company is as of now on its way to launch an entirely 3D printed rocket to orbit. The company has a team size of 230 employees.
AI Has Cracked a Key Mathematical Puzzle for Understanding Our World Karen Hao MIT Technology Review "Partial differential equations can describe everything from planetary motion to plate tectonics, but they're notoriously hard to solve. Physicists 3D Print a Boat That Could Sail Down a Human Hair John Biggs Gizmodo "Researchers at Leiden University have 3D printed the smallest boat in the world: a 30-micrometer copy of Benchy the tug boat, a well-known 3D printer test object. This boat is so small, it could float down the interior of a human hair. The 3D-printed boat is part of an exploration of microswimmers, microscopic organisms or objects that can move through liquids." Record-Smashing Hybrid Drone Stays Airborne for a Crazy 10 Hours, 14 Minutes Luke Dormehl Digital Trends "i'HYBRiX is an innovation, inspired by hybrid cars, that combines the best of both technologies,' a spokesperson for Quaternium told Digital Trends, referring to the drone's clever gasoline and battery-electric hybrid power system.
A tiny California start-up is looking to printers to solve the housing crisis – actually, a very large 3D printer. The company, Mighty Buildings, has been showcasing small (350 square foot) studio apartment models of its new "ADU" units (Accessory Dwelling Units) aimed at backyards and selling for around $115,000. That is, if you do the work and deal with local governments to get all the permits, connect the utilities and install the unit. Have Mighty set it up for you, and you're looking around $184,000. Sam Ruben, the co-founder of the firm, says Mighty can have the home in place in just over two weeks.
Humans are innately capable of performing complex movements with their hands via the articulation of their endoskeletal structure. These movements are made possible by ligaments and tendons that are elastically connected to a fairly rigid bone structure. Researchers at University of California- Santa Cruz and Ritsumeikan University in Japan have recently designed and fabricated a robotic finger inspired by the human endoskeletal structure. This biomimetic robotic finger, presented at this year's International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), was assembled using a multi-material 3-D printer. "Developing a robotic hand that has hard and soft components, just like the human hand, is a research topic that I wanted to explore for years," Maryam Tebyani, one of the researchers who carried out the study, told TechXplore.
Oak Ridge National Laboratory is behind the development of a new type of artificial intelligence (AI) software called Peregrine, meant to improve the quality of functional parts being produced via powder bed 3D printers. Peregrine requires no "expensive characterization equipment," yet possesses the ability to evaluate parts during manufacturing. "Capturing that information creates a digital'clone' for each part, providing a trove of data from the raw material to the operational component," said Vincent Paquit, leader of advanced manufacturing data analytics research as part of ORNL's Imaging, Signals and Machine Learning group. "We then use that data to qualify the part and to inform future builds across multiple part geometries and with multiple materials, achieving new levels of automation and manufacturing quality assurance." Oak Ridge National Laboratory researcher Chase Joslin uses Peregrine software to monitor and analyze a component being 3D printed at the Manufacturing Demonstration Facility at ORNL (Image: Luke Scime, ORNL, U.S. Dept. of Energy) The software is based on a convolutional neural network that imitates the human brain, rapidly evaluating images from cameras during printing.
Increasing the penetration of variable generation has a substantial effect on the operational reliability of power systems. The higher level of uncertainty that stems from this variability makes it more difficult to determine whether a given operating condition will be secure or insecure. Data-driven techniques provide a promising way to identify security rules that can be embedded in economic dispatch model to keep power system operating states secure. This paper proposes using a sparse weighted oblique decision tree to learn accurate, understandable, and embeddable security rules that are linear and can be extracted as sparse matrices using a recursive algorithm. These matrices can then be easily embedded as security constraints in power system economic dispatch calculations using the Big-M method. Tests on several large datasets with high renewable energy penetration demonstrate the effectiveness of the proposed method. In particular, the sparse weighted oblique decision tree outperforms the state-of-art weighted oblique decision tree while keeping the security rules simple. When embedded in the economic dispatch, these rules significantly increase the percentage of secure states and reduce the average solution time.
However, one issue that still persists is how to avoid printing objects that don't meet expectations and thus can't be used, leading to a waste in materials and resources. Scientists at the University of Southern California's (USC's) Viterbi School of Engineering has come up with what they think is a solution to the problem with a new machine-learning-based way to ensure more accuracy when it comes to 3D-printing jobs. Researchers from the Daniel J. Epstein Department of Industrial and Systems Engineering developed a new set of algorithms and a software tool called PrintFixer that they said can improve 3D-printing accuracy by 50 percent or more. The team, led by Qiang Huang, associate professor of industrial and systems engineering and chemical engineering and materials science, hopes the technology can help make additive manufacturing processes more economical and sustainable by eliminating wasteful processes, he said. "It can actually take industry eight iterative builds to get one part correct, for various reasons," said Qiang, who led the research.
Additive Manufacturing (AM) is a crucial component of the smart industry. In this paper, we propose an automated quality grading system for the AM process using a deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and classifying the failure in AM process at different extruder speeds and temperatures. The model demonstrates the accuracy of 94% and specificity of 96%, as well as above 75% in three classifier measures of the Fscore, the sensitivity, and precision for classifying the quality of the printing process in five grades in real-time. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process that eliminates the manual inspection of parts after they are entirely built. The quality monitoring signal can also be used by the machine to suggest remedial actions by adjusting the parameters in real-time. The proposed quality predictive model serves as a proof-of-concept for any type of AM machines to produce reliable parts with fewer quality hiccups while limiting the waste of both time and materials.
We propose the SupRB learning system, a new Pittsburgh-style learning classifier system (LCS) for supervised learning on multi-dimensional continuous decision problems. SupRB learns an approximation of a quality function from examples (consisting of situations, choices and associated qualities) and is then able to make an optimal choice as well as predict the quality of a choice in a given situation. One area of application for SupRB is parametrization of industrial machinery. In this field, acceptance of the recommendations of machine learning systems is highly reliant on operators' trust. While an essential and much-researched ingredient for that trust is prediction quality, it seems that this alone is not enough. At least as important is a human-understandable explanation of the reasoning behind a recommendation. While many state-of-the-art methods such as artificial neural networks fall short of this, LCSs such as SupRB provide human-readable rules that can be understood very easily. The prevalent LCSs are not directly applicable to this problem as they lack support for continuous choices. This paper lays the foundations for SupRB and shows its general applicability on a simplified model of an additive manufacturing problem.
Robots will soon be everywhere – especially if ordinary objects can be turned into them. A computer program can now use 3D-printing to turn household objects into hand-activated robots. It can be used to turn on the water taps on a bathroom sink with the wave of a hand, or to give a window the ability to shut itself when the weather gets cold. Xiang'Anthony' Chen at the University of California in Los Angeles and colleagues developed the tool, known as Robiot, to automate simple physical tasks.