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.

As a digital manufacturing method, additive manufacturing has already managed to establish itself in a wide variety of industries. Whether in medicine, the automotive sector or the consumer goods industry, there is hardly any sector that does not benefit from the strengths of 3D printing. Among other things, the technology innovates production processes by making components both more flexible and more sustainable. Nevertheless, 3D printing has not yet been able to realize its full potential in terms of productivity. Could artificial intelligence be the key?

Artificial Intelligence and 3D printing online are two of the most hot issues in the field of technology, and the possibilities it will impact our lives in the future. As we have explained in a number of our blog posts in the past such as this one about the most innovative metal 3D printing software .You can utilize 3D printing service in almost unlimited number of circumstances. What is the best way to use it to tackle AI problems? A group of researchers have discovered a interesting method to use this technology in its Artificial Intelligence projects. Recently The European Space Agency came out with an innovative 3D printing software.

Ford and security company ADT have formed a joint venture that will help businesses protect vehicles and expensive equipment they carry from theft. The first product from the venture called Canopy is a system that uses cameras, radar, global positioning and artificial intelligence to spot a potential thief and notify ADT, which will monitor the feeds and notify the customer or authorities if needed. The system will be offered as accessory on Ford and other top-selling vehicles starting next year. The company expects to integrate it in factory vehicles and offer it to other manufacturers over time. The first cameras will be available in the United States and the United Kingdom, including Ford pickups and commercial vans.

Characterizing meltpool shape and geometry is essential in metal Additive Manufacturing (MAM) to control the printing process and avoid defects. Predicting meltpool flaws based on process parameters and powder material is difficult due to the complex nature of MAM process. Machine learning (ML) techniques can be useful in connecting process parameters to the type of flaws in the meltpool. In this work, we introduced a comprehensive framework for benchmarking ML for melt pool characterization. An extensive experimental dataset has been collected from more than 80 MAM articles containing MAM processing conditions, materials, meltpool dimensions, meltpool modes and flaw types. We introduced physics-aware MAM featurization, versatile ML models, and evaluation metrics to create a comprehensive learning framework for meltpool defect and geometry prediction. This benchmark can serve as a basis for melt pool control and process optimization. In addition, data-driven explicit models have been identified to estimate meltpool geometry from process parameters and material properties which outperform Rosenthal estimation for meltpool geometry while maintaining interpretability.

Like any good buzzwordy phrase, "Industry 4.0" has its high and low points. An example of a low point, for instance, would be that it's a subtle way of presenting our dystopian future as an exciting new idea, rather than simply the inevitable working out of market dynamics. Contrarily, what is perhaps its highest point is that it instantly suggests the inextricable relationship between the trajectories of what could otherwise seem like very distinct economic trends. A French-Swedish startup, for instance, ADAXIS (founded in 2021), is predicated entirely on developing a software platform that recognizes the necessarily overlapping objectives between three of the sectors most closely associated with the phrase "Industry 4.0": artificial intelligence (AI), additive manufacturing (AM), and robotics. Earlier this week, ADAXIS announced that its software platform, currently in development and called AdaOne, has just received over €1 million in its first, pre-seed round of funding.

With the combination of 3D printing and robotics, researchers are designing robots with the latest technologies and adding different advanced functions into them. For instance, humanoid robots are the best example. These robots at times represent humans or at times they mimic humans in some of the other ways such as walking, talking, gestures, etc.… These robots are not just used for entertainment but also in doing chores, caretaking, medical applications, and many. In the below article, you can learn about the most advanced 3D printed robots.