Wire-feed laser additive manufacturing (WLAM) is gaining wide interest due to its high level of automation, high deposition rates, and good quality of printed parts. In-process monitoring and feedback controls that would reduce the uncertainty in the quality of the material are in the early stages of development. Machine learning promises the ability to accelerate the adoption of new processes and property design in additive manufacturing by making process-structure-property connections between process setting inputs and material quality outcomes. The molten pool dimensional information and temperature are the indicators for achieving the high quality of the build, which can be directly controlled by processing parameters. For the purpose of in situ quality control, the process parameters should be controlled in real-time based on sensed information from the process, in particular the molten pool. Thus, the molten pool-process relations are of preliminary importance. This paper analyzes experimentally collected in situ sensing data from the molten pool under a set of controlled process parameters in a WLAM system. The variations in the steady-state and transient state of the molten pool are presented with respect to the change of independent process parameters. A multi-modality convolutional neural network (CNN) architecture is proposed for predicting the control parameter directly from the measurable molten pool sensor data for achieving desired geometric and microstructural properties. Dropout and regularization are applied to the CNN architecture to avoid the problem of overfitting. The results highlighted that the multi-modal CNN, which receives temperature profile as an external feature to the features extracted from the image data, has improved prediction performance compared to the image-based uni-modality CNN approach.

Founded in 2013, Markforged is the creator of an integrated metal and carbon fiber additive manufacturing platform, The Digital Forge - a cloud and ML-based 3D printing platform designed to interconnect all of the company's systems currently being used around the world. It is claimed to be the first such platform to use machine learning, a feature that enables the company's Eiger print preparation software to constantly learn from the 12,000 systems in its 73-country-wide global fleet. As such, says the company, every print on a connected Markforged system should theoretically be more accurate than the last. A* was founded and is led by technology industry veteran and investor Kevin Hartz. The combined company will have an estimated post-transaction equity value of approximately $2.1 billion at closing.

In recent years, the'maker movement' has emerged as a social phenomenon driven by novel technological possibilities.1 With the help of inexpensive, yet highly versatile means of production (for example, CNC milling machines, 3D printers) and easy-to-use software tools, makers free themselves from their traditional role as passive consumers and evolve into innovators and producers. Although the act of physical production seems to be at the center of the movement, a large part of the creative work takes place in the online sphere. These digital activities and their outcomes provide a rich source of information that can be used to gain a more nuanced understanding of how the digitization affects the creative process itself. Of all the production methods available to makers, 3D printing is probably the most versatile and requires only a limited understanding of the production process. Several 3D design software packages allow even lay people to turn their ideas into printable designs.

From Star Trek's replicators to Richie Rich's wishing machine, popular culture has a long history of parading flashy machines that can instantly output any item to a user's delight. While 3D printers have now made it possible to produce a range of objects that include product models, jewelry, and novelty toys, we still lack the ability to fabricate more complex devices that are essentially ready-to-go right out of the printer. A group from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) recently developed a new system to print functional, custom-made devices and robots, without human intervention. Their single system uses a three-ingredient recipe that lets users create structural geometry, print traces, and assemble electronic components like sensors and actuators. "LaserFactory" has two parts that work in harmony: a software toolkit that allows users to design custom devices, and a hardware platform that fabricates them.

Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks is typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as the graph-structured data with high dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many studies on extending deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNNs structures (e.g., graph convolutional networks, graph recurrent neural networks, graph attention networks, graph generative networks, spatial-temporal graph convolutional networks, and hybrid forms of GNNs) are summarized, and key applications in power systems such as fault diagnosis, power prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed.

We propose a new adversarial training approach for injecting robustness when designing controllers for upcoming cyber-physical power systems. Previous approaches relying deeply on simulations are not able to cope with the rising complexity and are too costly when used online in terms of computation budget. In comparison, our method proves to be computationally efficient online while displaying useful robustness properties. To do so we model an adversarial framework, propose the implementation of a fixed opponent policy and test it on a L2RPN (Learning to Run a Power Network) environment. That environment is a synthetic but realistic modeling of a cyber-physical system accounting for one third of the IEEE 118 grid. Using adversarial testing, we analyze the results of submitted trained agents from the robustness track of the L2RPN competition. We then further assess the performance of those agents in regards to the continuous N-1 problem through tailored evaluation metrics. We discover that some agents trained in an adversarial way demonstrate interesting preventive behaviors in that regard, which we discuss.

Tiny plastic particles that can cause cancer are emitted by 3D printers -- with such being the most toxic to children under the age of nine, experts have warned. The printers work be depositing successive layers of thermoplastics, metals, nanomaterials, polymers, slowly building up a complete object. The global 3D printing market was worth an estimated £8.71 billion last year -- a figure increasing as more people purchase printers for their own homes. Researchers from the US, however, have found that the devices pose an unexpected health risk -- in addition to their known contribution to plastic pollution. During the hours it can take to complete a print, various particulates and chemical by-products can be released into the surrounding environment.

Agricultural equipment giant Deere & Co. next summer will debut in farm fields a solution that combines machine vision and machine learning, to distinguish weeds from plants. Agriculture giant Deere & Co. plans to roll out a system next summer that combines machine vision and machine learning to improve the identification of individual plants and weeds. Deere's Jahmy Hindman said neural network models could be trained to only spray weeds in crop fields, killing everything except genetically modified plants designed to survive chemical applications. Said Hindman, "We are interested in being able to manage each plant over the course of its life, minimizing inputs and maximizing productivity." The technology would take pictures of plants, and a machine cruising the field would make the decision to spray in just seconds.

What if you could ride your own giant LEGO electric skateboard, make a synthesizer that you can play with a barcode reader, or build a strong robot dog based on the Boston Dynamics dog robot? Today sees the start of a new series of videos that focuses on James Bruton's open source robot projects. James Bruton is a former toy designer, current YouTube maker and general robotics, electrical and mechanical engineer. He has a reputation for building robot dogs and building Iron Man inspired cosplays. He uses 3D printing, CNC and sometimes welding to build all sorts of robotics related creations.

When it comes to the construction industry machine learning means many things. However, at its core, it all comes back to one thing: data. The more data that is produced through telematics, the more advanced artificial intelligence (AI) becomes, due to it having more data to learn from. The more complex the data the better for AI, and as AI becomes more advanced its decision-making improves. This means that construction is becoming more efficient thanks to a loop where data and AI are feeding into each other.