In recent years, the'maker movement' has emerged as a social phenomenon driven by novel technological possibilities.1 With the help of inexpensive, yet highly versatile means of production (for example, CNC milling machines, 3D printers) and easy-to-use software tools, makers free themselves from their traditional role as passive consumers and evolve into innovators and producers. Although the act of physical production seems to be at the center of the movement, a large part of the creative work takes place in the online sphere. These digital activities and their outcomes provide a rich source of information that can be used to gain a more nuanced understanding of how the digitization affects the creative process itself. Of all the production methods available to makers, 3D printing is probably the most versatile and requires only a limited understanding of the production process. Several 3D design software packages allow even lay people to turn their ideas into printable designs.
From Star Trek's replicators to Richie Rich's wishing machine, popular culture has a long history of parading flashy machines that can instantly output any item to a user's delight. While 3D printers have now made it possible to produce a range of objects that include product models, jewelry, and novelty toys, we still lack the ability to fabricate more complex devices that are essentially ready-to-go right out of the printer. A group from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) recently developed a new system to print functional, custom-made devices and robots, without human intervention. Their single system uses a three-ingredient recipe that lets users create structural geometry, print traces, and assemble electronic components like sensors and actuators. "LaserFactory" has two parts that work in harmony: a software toolkit that allows users to design custom devices, and a hardware platform that fabricates them.
We propose a new adversarial training approach for injecting robustness when designing controllers for upcoming cyber-physical power systems. Previous approaches relying deeply on simulations are not able to cope with the rising complexity and are too costly when used online in terms of computation budget. In comparison, our method proves to be computationally efficient online while displaying useful robustness properties. To do so we model an adversarial framework, propose the implementation of a fixed opponent policy and test it on a L2RPN (Learning to Run a Power Network) environment. That environment is a synthetic but realistic modeling of a cyber-physical system accounting for one third of the IEEE 118 grid. Using adversarial testing, we analyze the results of submitted trained agents from the robustness track of the L2RPN competition. We then further assess the performance of those agents in regards to the continuous N-1 problem through tailored evaluation metrics. We discover that some agents trained in an adversarial way demonstrate interesting preventive behaviors in that regard, which we discuss.
For the past five years, Antonio Pellegrino has been working on a high-pressure job: the scientist is leading an upgrade to the largest and most powerful particle accelerator ever built, which sits one hundred meters beneath the Franco-Swiss border at the European Organization for Nuclear Research (CERN). Commonly called the large hadron collider (LHC), the accelerator is a 27-kilometer-long ring in which particles such as protons and electrons are projected against one another at high speeds, recreating the conditions that existed one hundredth of a billionth of a second after the Big Bang – all for modern-day scientists to observe thanks to various high-precision detectors that sit inside the accelerator. SEE: An IT pro's guide to robotic process automation (free PDF) (TechRepublic) LHC was put on hold in 2015 for engineers and physicians to find ways to improve the accuracy of the accelerator's detectors. To do exactly that, Pellegrino turned to a technology that is currently used to design products ranging from food and fashion to human cells: 3D printing. Thanks to a partnership with technology company 3D Systems, some parts of the LHC have been springing out of 3D printers, as part of the design of the accelerator's detectors' cooling system.
What if you could ride your own giant LEGO electric skateboard, make a synthesizer that you can play with a barcode reader, or build a strong robot dog based on the Boston Dynamics dog robot? Today sees the start of a new series of videos that focuses on James Bruton's open source robot projects. James Bruton is a former toy designer, current YouTube maker and general robotics, electrical and mechanical engineer. He has a reputation for building robot dogs and building Iron Man inspired cosplays. He uses 3D printing, CNC and sometimes welding to build all sorts of robotics related creations.
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.
I-nteract is a cyber-physical system that enables real-time interaction with both virtual and real artifacts to design 3D models for additive manufacturing by leveraging on mixed reality technologies. This paper presents novel advances in the development of the interaction platform I-nteract to generate 3D models using both constructive solid geometry and artificial intelligence. The system also enables the user to adjust the dimensions of the 3D models with respect to their physical workspace. The effectiveness of the system is demonstrated by generating 3D models of furniture (e.g., chairs and tables) and fitting them into the physical space in a mixed reality environment.
The integration of AI and 3D printing in manufacturing can help increase unit production rate, detect defects, and provide real-time control over the manufacturing process. As the name suggests, additive manufacturing is a method of building products by adding layers of components on one another. AI, on the other hand, as everyone knows, can automate monotonous tasks and bring accuracy in those tasks. The manufacturing sector has many repetitive labor tasks that make AI a perfect match for the manufacturing and 3D printing process.. AI can increase the production rate and accuracy of 3D production. Using computer vision, manufacturers can reverse engineer the existing models and create a new and improved product design.