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.

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of high sampling rates of PMUs. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.

British department store Selfridges has recruited an 8 foot-tall'upcycling' robot that can 3D-print recycled plastic into personalised designer objects. At Selfridges' store at Oxford Street in central London, the robot will be printing items made of plastic taken from the world's seas. It's creating a variety of designer objects from the plastic, including vases, chairs, stools and lampshades, which can be selected and bought by customers. The items have been designed by Nagami, a Spanish firm specialising in high-end furniture and homeware. The Selfridges' robot is 3D-printing the items through the rest of April, which cost anything from £155 to £830.

Predictive maintenance can be an effective way to prevent industrial machinery failures and expensive downtime by proactively monitoring the condition of your equipment, so you can be alerted to any anomalies before equipment failures occur. Installing sensors and the necessary infrastructure for data connectivity, storage, analytics, and alerting are the foundational elements for enabling predictive maintenance solutions. However, even after installing the ad hoc infrastructure, many companies use basic data analytics and simple modeling approaches that are often ineffective at detecting issues early enough to avoid downtime. Also, implementing a machine learning (ML) solution for your equipment can be difficult and time-consuming. With Amazon Lookout for Equipment, you can automatically analyze sensor data for your industrial equipment to detect abnormal machine behavior--with no ML experience required.

Artificial Intelligence is on track to revolutionize the construction industry like few technologies before it. But how can it be applied to heavy equipment? For several years now, artificial intelligence (AI) has been gaining in capability and reliability as the technology is developed and improved. Today, AI in construction is becoming more and more commonplace, offering a wide variety of advantages to construction professionals and their clients. In particular, AI has capabilities that can greatly improve operations on construction sites.