In an article recently published in the journal Additive Manufacturing, researchers discussed computer vision and deep learning for in-situ optimization of thermoset composite additive manufacturing (AM). A new extrusion AM process called direct ink writing (DIW) offers unequaled design flexibility with a wide range of feedstock materials. Although composite DIW has made great strides, this technology still has a long way to go before it can be considered a cornerstone of contemporary composite manufacturing. To consistently produce high-quality prints, it is essential to comprehend the complex interactions between the DIW process, print quality, and material performance. Machine learning (ML) methods can be used to simulate the impacts of process parameters and autonomously optimize the AM as a solution to this issue.

Flying robots are usually rather delicate and require protective enclosures when facing the risk of collision, while high complexity and reduced payload are recurrent problems with collision-resilient flying robots. Inspired by arthropods' exoskeletons, we design a simple, open source, easily manufactured, semi-rigid structure with soft joints that can withstand high-velocity impacts. With an exoskeleton, the protective shell becomes part of the main robot structure, thereby minimizing its loss in payload capacity. Our design is simple to build and customize using cheap components (e.g. bamboo skewers) and consumer-grade 3D printers. The result is CogniFly, a sub-250g autonomous quadcopter that survives multiple collisions at speeds up to 7m/s. In addition to its collision-resiliency, CogniFly is easy to program using Python or Buzz, carries sensors that allow it to fly for approx. 17min without the need of GPS or an external motion capture system, has enough computing power to run deep neural network models on-board and was designed to facilitate integration with an automated battery swapping system. This structure becomes an ideal platform for high-risk activities (such as flying in a cluttered environment or reinforcement learning training) by dramatically reducing the risks of damaging its own hardware or the environment. Source code, 3D files, instructions and videos are available through the project's website (https://thecognifly.github.io).

The additive manufacturing (AM) industry is now almost 40 years old, yet severe problems still persist in yield, accuracy and efficiency. Hard-won breakthroughs in physics, chemistry and engineering have developed prescriptions for the motion of mechatronics, and the dispensing of the materials, the ideal temperature and humidity of processes and machines should follow these equations exactly. Everything, in theory, should turn out perfect, but this is not always the case in practice. Out of a batch of one dozen parts that are 3D-printed, maybe only seven, eight or nine meet structural and functional standards, based on my experience. Additive manufacturing technology is very, very good, but it has not yet fully matured.

The new technique involves a 3D printing process for engineered active materials with multiple functions, or'metamaterials.' It enables the manufacturing of the entire mechanical and electronic systems required for operating a robot at once. After the'meta-bot' has been 3D printed, it can carry out movement, propulsion, sensing, and decision-making.

We're starting today's 3D Printing News Briefs with a new system announcement, as Farsoon just introduced its FS200M 2 platform to the AMEA and North America AM market. Moving on, Senvol and Northrop Grumman presented together at RAPID about using machine learning to improve process parameter optimization. Finally, United Performance Metals announced a new Additive Manufacturing Solutions Center, and a new Innovation Centre for advanced materials & digitalization was established by TWI and Manchester Metropolitan University. Farsoon Technologies has introduced the latest addition to its medium-size metal LPBF line--the FS200M 2 platform, with a powerful dual 500-watt laser configuration and 425 x 230 x 300 mm build volume. The company says the versatile, compact printer offers maximized productivity and turn-over rates, and is well-suited for medium to high volume metal series production and prototyping.

John Deere is announcing the acquisition of a state-of-the-art algorithm package from artificial intelligence startup Light. For those of you wondering when driverless vehicles will truly begin to make their mark on society, the answer is: today. Up front: No, you won't be seeing green tractors rolling themselves down city streets anytime soon. But the timeline for fully autonomous farming is being massively accelerated. Today's purchase is all about John Deere's need for speed -- and accuracy, but first let's talk about rapid development.

Future generations of robots will work very differently from those that assemble entire vehicles or solder electronics onto circuit boards at lightning speed on factory floors today. They will leave the factory halls and start working with people, handing them a tool at the right moment or assisting them in assembling heavy components. They will appear in agriculture, helping harvest the fields or process the fruits. And they will increasingly be found in living rooms, supporting and entertaining people there or simply making them feel less alone. Of course, these robots will also look different from the enormous metallic contraptions found in today's industrial plants.

"Furthermore, the machine learning models enable our software to derive modifications to the nominal designs, known as compensation plans, so that …

New additive manufacturing methods are needed to realize more complex soft robots. One example is soft fluidic robotics, which exploits fluidic power and stiffness gradients. Porous structures are an interesting type for this approach, as they are flexible and allow for fluid transport. Within this work, the Infill-Foam (InFoam) is proposed to print structures with graded porosity by liquid rope coiling (LRC). By exploiting LRC, the InFoam method could exploit the repeatable coiling patterns to print structures. To this end, only the characterization of the relation between nozzle height and coil radius and the extruded length were necessary (at a fixed temperature). Then by adjusting the nozzle height and/or extrusion speed the porosity of the printed structure could be set. The InFoam method was demonstrated by printing porous structures using styrene-ethylene-butylene-styrene (SEBS) with porosities ranging from 46\% to 89\%. In compression tests, the cubes showed large changes in modulus (more than 200 times), density (-89\% compared to bulk), and energy dissipation. The InFoam method combined coiling and normal plotting to realize a large range of porosity gradients. This grading was exploited to realize rectangular structures with varying deformation patterns, which included twisting, contraction, and bending. Furthermore, the InFoam method was shown to be capable of programming the behavior of bending actuators by varying the porosity. Both the output force and stroke showed correlations similar to those of the cubes. Thus, the InFoam method can fabricate and program the mechanical behavior of a soft fluidic (porous) actuator by grading porosity.

The heist at the center of the 2018 ensemble comedy movie Ocean's 8 required the protagonists to switch valuable jewels for 3-D-printed copies. "Replicators," which generate food or tools from basic raw materials, have been a staple of science fiction in film and TV for generations. Yet while Hollywood has been quick to seize on the potential of additive manufacturing (AM), these technologies have been slow to find their blockbuster applications in real-world manufacturing. Compared with traditional production approaches, AM technologies offer four potential sources of value. First, their ability to generate almost any 3-D shape allows designers the freedom to create parts that perform better or cost less than conventional alternatives.