The dynamics of a power system with large penetration of renewable energy resources are becoming more nonlinear due to the intermittence of these resources and the switching of their power electronic devices. Therefore, it is crucial to accurately identify the dynamical modes of oscillation of such a power system when it is subject to disturbances to initiate appropriate preventive or corrective control actions. In this paper, we propose a high-order blind source identification (HOBI) algorithm based on the copula statistic to address these non-linear dynamics in modal analysis. The method combined with Hilbert transform (HOBI-HT) and iteration procedure (HOBMI) can identify all the modes as well as the model order from the observation signals obtained from the number of channels as low as one. We access the performance of the proposed method on numerical simulation signals and recorded data from a simulation of time domain analysis on the classical 11-Bus 4-Machine test system. Our simulation results outperform the state-of-the-art method in accuracy and effectiveness.

In context of laser powder bed fusion (L-PBF), it is known that the properties of the final fabricated product highly depend on the temperature distribution and its gradient over the manufacturing plate. In this paper, we propose a novel means to predict the temperature gradient distributions during the printing process by making use of neural networks. This is realized by employing heat maps produced by an optimized printing protocol simulation and used for training a specifically tailored recurrent neural network in terms of a long short-term memory architecture. The aim of this is to avoid extreme and inhomogeneous temperature distribution that may occur across the plate in the course of the printing process. In order to train the neural network, we adopt a well-engineered simulation and unsupervised learning framework. To maintain a minimized average thermal gradient across the plate, a cost function is introduced as the core criteria, which is inspired and optimized by considering the well-known traveling salesman problem (TSP). As time evolves the unsupervised printing process governed by TSP produces a history of temperature heat maps that maintain minimized average thermal gradient. All in one, we propose an intelligent printing tool that provides control over the substantial printing process components for L-PBF, i.e.\ optimal nozzle trajectory deployment as well as online temperature prediction for controlling printing quality.

Our interview today is truly unique: we have interviewed artificial intelligence (AI)! We wanted to test the now well-known ChatGPT and thought we would interview it as we would a 3D printing expert. ChatGPT (short for Generative Pre-trained Transformer) is a chatbot system developed by OpenAI, based on an artificial intelligence model using machine learning technology. When we asked it to introduce itself, it told us, "I'm here to help you solve your problems. I will be glad to help you in any way I can."

Understanding the thermal behavior of additive manufacturing (AM) processes is crucial for enhancing the quality control and enabling customized process design. Most purely physics-based computational models suffer from intensive computational costs and the need of calibrating unknown parameters, thus not suitable for online control and iterative design application. Data-driven models taking advantage of the latest developed computational tools can serve as a more efficient surrogate, but they are usually trained over a large amount of simulation data and often fail to effectively use small but high-quality experimental data. In this work, we developed a hybrid physics-based data-driven thermal modeling approach of AM processes using physics-informed neural networks. Specifically, partially observed temperature data measured from an infrared camera is combined with the physics laws to predict full-field temperature history and to discover unknown material and process parameters. In the numerical and experimental examples, the effectiveness of adding auxiliary training data and using the pretrained model on training efficiency and prediction accuracy, as well as the ability to identify unknown parameters with partially observed data, are demonstrated. The results show that the hybrid thermal model can effectively identify unknown parameters and capture the full-field temperature accurately, and thus it has the potential to be used in iterative process design and real-time process control of AM.

Learning from nature has been a quest of humanity for millennia. While this has taken the form of humans assessing natural designs such as bones, butterfly wings, or spider webs, we can now achieve generating designs using advanced computational algorithms. In this paper we report novel biologically inspired designs of diatom structures, enabled using transformer neural networks, using natural language models to learn, process and transfer insights across manifestations. We illustrate a series of novel diatom-based designs and also report a manufactured specimen, created using additive manufacturing. The method applied here could be expanded to focus on other biological design cues, implement a systematic optimization to meet certain design targets, and include a hybrid set of material design sets.

Utilizing machine learning, the team also discovered two modes of keyhole oscillation. "Our findings not only advance additive manufacturing research, …

Early this week, tractor maker John Deere said it had signed a memorandum of understanding with the American Farm Bureau Federation, an agricultural trade group, promising to make it easier for farmers to access tools and software needed to repair their own equipment. The deal looked like a concession from the agricultural equipment maker, a major target of the right-to-repair movement, which campaigns for better access to documents and tools needed for people to repair their own gear. But right-to-repair advocates say that despite some good points, the agreement changes little, and farmers still face unfair barriers to maintaining equipment they own. Kevin O'Reilly, a director of the right-to-repair campaign run by the US Public Interest Research Group, a grassroots lobbying organization, says the timing of Deere's deal suggests the company may be trying to quash recent interest in right-to-repair laws from state legislators. In the past two years, corn belt states including Nebraska and Missouri, and also Montana, have considered giving farmers a legal right to tools needed to repair their own equipment. But no laws have been passed.

A John Deere autonomous tractor is on display at CES 2022 in Las Vegas, Nevada. A John Deere autonomous tractor is on display at CES 2022 in Las Vegas, Nevada. Like many parts of modern life, tractors have gone high-tech, often running on advanced computer systems. But some manufacturers are tight-lipped about how these electronics work, making it difficult or nearly impossible for farmers and independent repair shops to diagnose and fix problems with the equipment. An agreement by John Deere may finally give farmers a greater hand in repairing the company's products.

Normally we do not cover topics related to cosmetics, being a website focused on the more industrial, loud, and explosive applications of additive manufacturing. But we are interested in production-grade 3D printing, as well as AI, computer vision, and computer software. And this new story from cosmetic giant Neutrogena has all of those components, so we'll cover it. Read on to find out how we let an AI pass judgment on this writer's face skin! It's CES 2023 week which means we will be seeing plenty of stories of interesting new innovations saturating tech websites.

It features an AI camera in the robot arm that can capture up to 30 frames per second. It comes equipped with RISK-V-based processors and AI accelerators to support features like real-time face recognition, voice recognition, and object detection. Other features in AI cameras include image classification, color recognition, line tracking, human segmentation, and more. The robot arm works with Wi-Fi, and the Bluetooth feature allows users to pair their smartphones with it. It features an intuitive 2.4" touchscreen display and a microphone with voice recognition.