Machine Learning capable of upgrading FDM 3D printed parts to such a level that they overtake their injection moulded counterparts.

Nonlinear state estimation (SE), with the goal of estimating complex bus voltages based on all types of measurements available in the power system, is usually solved using the iterative Gauss-Newton method. The nonlinear SE presents some difficulties when considering inputs from both phasor measurement units and supervisory control and data acquisition system. These include numerical instabilities, convergence time depending on the starting point of the iterative method, and the quadratic computational complexity of a single iteration regarding the number of state variables. This paper introduces an original graph neural network based SE implementation over the augmented factor graph of the nonlinear power system SE, capable of incorporating measurements on both branches and buses, as well as both phasor and legacy measurements. The proposed regression model has linear computational complexity during the inference time once trained, with a possibility of distributed implementation. Since the method is noniterative and non-matrix-based, it is resilient to the problems that the Gauss-Newton solver is prone to. Aside from prediction accuracy on the test set, the proposed model demonstrates robustness when simulating cyber attacks and unobservable scenarios due to communication irregularities. In those cases, prediction errors are sustained locally, with no effect on the rest of the power system's results.

With the rapid development of powder-based additive manufacturing, depowdering, a process of removing unfused powder that covers 3D-printed parts, has become a major bottleneck to further improve its productiveness. Traditional manual depowdering is extremely time-consuming and costly, and some prior automated systems either require pre-depowdering or lack adaptability to different 3D-printed parts. To solve these problems, we introduce a robotic system that automatically removes unfused powder from the surface of 3D-printed parts. The key component is a visual perception system, which consists of a pose-tracking module that tracks the 6D pose of powder-occluded parts in real-time, and a progress estimation module that estimates the depowdering completion percentage. The tracking module can be run efficiently on a laptop CPU at up to 60 FPS. Experiments show that our depowdering system can remove unfused powder from the surface of various 3D-printed parts without causing any damage. To the best of our knowledge, this is one of the first vision-based robotic depowdering systems that adapt to parts with various shapes without the need for pre-depowdering.

A multilingual (English/Spanish) immersive learning environment is being created by an assistant professor at the University of Texas using virtual reality technology to help students better grasp the topic of additive manufacturing. The project, titled "Enhancing Active Learning in Additive Manufacturing Using a Bilingual, Assisted Virtual-Reality Platform," will be under the direction of Shuchisnigdha Deb, assistant professor in the Department of Industrial, Manufacturing, and Systems Engineering (IMSE). The research is being supported by a grant from the National Science Foundation worth $837,000. Emma Yang and Amanda Olsen, two other ISME faculty members from the College of Education's Department of Curriculum and Instruction, are also on the team. Deb asserted that teaching kids about additive manufacturing is crucial for the advancement of robots, augmented reality, and other technologies.

A research team led by Prof. Gao Ge and Jiang Li from the Hefei Institutes of Physical Science of the Chinese Academy of Sciences has investigated the fault diagnosis of a pulse width modulation converter and proposed a neural network fault diagnosis algorithm to solve existing problems in this field. Results were published in IEEE Transactions on Power Electronics. Pulse width modulation has the advantages of high efficiency, high power density and high reliability. But due to the complexity of the drive systems and the diversity of fusion joint operation, pulse-width modulating voltage source converter systems are prone to suffer critical failures. Therefore, research on fault diagnostic technology is of deep concern, especially open-circuit fault diagnosis, which was what scientists have been focusing in this study.

Scientists and engineers are constantly developing new materials with unique properties that can be used for 3D printing, but figuring out howto print with these materials can be a complex, costly conundrum. Often, an expert operator must use manual trial-and-error -- possibly making thousands of prints -- to determine ideal parameters that consistently print a new material effectively. These parameters include printing speed and how much material the printer deposits. MIT researchers have now used artificial intelligence to streamline this procedure. They developed a machine-learning system that uses computer vision to watch the manufacturing process and then correct errors in how it handles the material in real-time.

Tables and chairs that self-assemble from 3D-printed wood could spell an end to the nightmare of trying to assemble flat-pack furniture. Scientists in Israel have created a printable'wood ink' that can be programmed to morph into complex shapes as it dries, like domes, helices and even Pringle shapes. The experts have so far printed designs that are only a few inches long, but they aim to produce much larger objects, like chairs, tables and shelves. In the future, large wooden products could be shipped flat to a destination and then dried by the customer to form the final shape at home. Pictured is the printed wood ink before it has been dried.

Infineon Technologies AG has acquired the Berlin-based startup Industrial Analytics IA GmbH. Thus, Infineon is strengthening its software and services business in artificial intelligence for predictive analysis. Infineon is acquiring 100 percent of the company's shares. Both parties have agreed not to disclose the amount of the transaction. Peter Wawer, President of Infineon's Industrial Power Control division, said Industrial Analytics has outstanding expertise in predictive analysis for industrial machinery and equipment using artificial intelligence.

Predicting mechanical properties in metal additive manufacturing (MAM) is vital to ensure the printed parts' performance, reliability, and whether they can fulfill requirements for a specific application. Conducting experiments to estimate mechanical properties in MAM processes, however, is a laborious and expensive task. Also, they can solely be designed for a particular material in a certain MAM process. Nonetheless, Machine learning (ML) methods, which are more flexible and cost-effective solutions, can be utilized to predict mechanical properties based on the processing parameters and material properties. To this end, in this work, a comprehensive framework for benchmarking ML for mechanical properties is introduced. An extensive experimental dataset is collected from more than 90 MAM articles and 140 MAM companies' data sheets containing MAM processing conditions, machines, materials, and resultant mechanical properties, including yield strength, ultimate tensile strength, elastic modulus, elongation, hardness as well as surface roughness. Physics-aware MAM featurization, adjustable ML models, and evaluation metrics are proposed to construct a comprehensive learning framework for mechanical properties prediction. Additionally, the Explainable AI method, i.e., SHAP analysis was studied to explain and interpret the ML models' predicted values for mechanical properties. Moreover, data-driven explicit models have been identified to estimate mechanical properties based on the processing parameters and material properties with more interpretability as compared to the employed ML models.

Technological advances in holography, robotics, and 3D printing are starting to realize the vision of a holodeck. These immersive 3D displays must address user safety from the start to be viable. A holodeck's safety challenges are novel because its applications will involve explicit physical interactions between humans and synthesized 3D objects and experiences in real-time. This pioneering paper first proposes research directions for modeling safety in future holodeck applications from traditional physical human-robot interaction modeling. Subsequently, we propose a test-bed to enable safety validation of physical human-robot interaction based on existing augmented reality and virtual simulation technology.