In the past few years, advanced industrial companies have made solid progress in improving productivity along the manufacturing value chain. In the US, for instance, the productivity of industrial workers has increased by 47 percent over the past 20 years. But the traditional levers that have driven these gains, such as lean operations, Six Sigma, and total quality management, are starting to run out of steam, and the incremental benefits they deliver are declining. As a result, leading companies are now looking to disruptive technologies for their next horizon of performance improvement. Many are starting to experiment with technologies such as machine-to-machine digital connectivity (the Industrial Internet of Things, or IIoT), artificial intelligence (AI), machine learning, advanced automation, robotics, and additive manufacturing.
A team at the US National Renewable Energy Laboratory (NREL) is working on autonomous energy grid (AEG) technology to ensure the electricity grid of the future can manage a growing base of intelligent energy devices, variable renewable energy, and advanced controls. "The future grid will be much more distributed too complex to control with today's techniques and technologies," said Benjamin Kroposki, director of NREL's Power Systems Engineering Center. "We need a path to get there--to reach the potential of all these new technologies integrating into the power system." The AEG effort envisions a self-driving power system - a very "aware" network of technologies and distributed controls that work together to efficiently match bi-directional energy supply to energy demand. This is a hard pivot from today's system, in which centralized control is used to manage one-way electricity flows to consumers along power lines that spoke out from central generators.
Many organizations have started to build infrastructure for AI using IBM Power Systems, which leverage NVIDIA GPUs. Enterprises often focus on building AI solutions that provide high availability, automated orchestration and the like, which can add to the cost of the solution. Educational institutions and research organizations, however, often look for solutions that give them more flexibility in utilizing underlying resources optimally for their machine learning and deep learning (ML/DL) workloads, and with much lower costs. Researchers may require running parallel DL training jobs using different AI runtimes. Professors may require allocating and deallocating AI runtimes to multiple students for AI assignments.
This paper presents approaches to determine a network based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The intent is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad-hoc and subjective prices. A data mining approach with machine learning methods is used to estimate a price range based on the profile characteristics of 3D printing service suppliers. The model considers factors such as supplier experience, supplier capabilities, customer reviews and ratings from past orders, and scale of operations among others to estimate a price range for suppliers' services. Data was gathered from existing marketplace websites, which was then used to train and test the model. The model demonstrates an accuracy of 65% for US based suppliers and 59% for Europe based suppliers to classify a supplier's 3D Printer listing in one of the seven price categories. The improvement over baseline accuracy of 25% demonstrates that machine learning based methods are promising for network based pricing in manufacturing marketplaces. Conventional methodologies for pricing services through activity based costing are inefficient in strategically pricing 3D printing service offering in a connected marketplace. As opposed to arbitrarily determining prices, this work proposes an approach to determine prices through data mining methods to estimate competitive prices. Such tools can be built into online marketplaces to help independent service bureaus to determine service price rates.
Into 3D printing world, "spaghetti" is the common term for the tangled mess of stringy plastic that's often the result of a failed print. Fear of their print bed turning into a hot plate of PLA spaghetti is enough to keep many users from leaving their machines operating overnight or while they're out of the house. The Spaghetti Detective, an open source project that lets machine learning take over when you can't sit watching the printer all day, might help those users to overcome that fear. This software monitors your prints for you, and notify you if it detects a possible print failure. The Spaghetti Detective is a plugin for OctoPrint, which runs on a Raspberry Pi, and gives you the ability to remotely control your 3D printer and view a live video feed as it runs.
Volvo Group and NVIDIA are delivering autonomy to the world's transportation industries, using AI to revolutionize how people and products move all over the world. At its headquarters in Gothenburg, Sweden, Volvo Group announced Tuesday that it's using the NVIDIA DRIVE end-to-end autonomous driving platform to train, test and deploy self-driving AI vehicles, targeting public transport, freight transport, refuse and recycling collection, construction, mining, forestry and more. By injecting AI into these industries, Volvo Group and NVIDIA can create amazing new vehicles and deliver more productive services. The two companies are co-locating engineering teams in Gothenburg and Silicon Valley. Together, they will build on the DRIVE AGX Pegasus platform for in-vehicle AI computing and utilize the full DRIVE AV software stack for 360-degree sensor processing, perception, map localization and path planning.
This paper introduces a novel framework to construct the region of attraction (ROA) of a power system centered around a stable equilibrium by using stable state trajectories of system dynamics. Most existing works on estimating ROA rely on analytical Lyapunov functions, which are subject to two limitations: the analytic Lyapunov functions may not be always readily available, and the resulting ROA may be overly conservative. This work overcomes these two limitations by leveraging the converse Lyapunov theorem in control theory to eliminate the need of an analytic Lyapunov function and learning the unknown Lyapunov function with the Gaussian Process (GP) approach. In addition, a Gaussian Process Upper Confidence Bound (GP-UCB) based sampling algorithm is designed to reconcile the trade-off between the exploitation for enlarging the ROA and the exploration for reducing the uncertainty of sampling region. Within the constructed ROA, it is guaranteed in probability that the system state will converge to the stable equilibrium with a confidence level. Numerical simulations are also conducted to validate the assessment approach for the ROA of the single machine infinite bus system and the New England $39$-bus system. Numerical results demonstrate that our approach can significantly enlarge the estimated ROA compared to that of the analytic Lyapunov counterpart.
Objects made with 3-D printing can be lighter, stronger, and more complex than those produced through traditional manufacturing methods. But several technical challenges must be overcome before 3-D printing transforms the production of most devices. Commercially available printers generally offer only high speed, high precision, or high-quality materials. Rarely do they offer all three, limiting their usefulness as a manufacturing tool. Today, 3-D printing is used mainly for prototyping and low-volume production of specialized parts.
Markforged, a company that makes industrial 3D printers, today announced that it has closed an $82 million Series D round. "Markforged set out to change the pace of human innovation by enabling engineers, inventors and manufacturers to print industrial-grade parts at a fraction of the time and cost of traditional methods," says Greg Mark, CEO and co-founder. "We're very excited to have Summit join us as we help accelerate the next industrial revolution with broadly accessible and reliable 3D printing." That's a lofty aspiration, but it might not be far off. The $12 trillion manufacturing sector is undergoing a transformation thanks to flexible automation technologies, including autonomous mobile robots and collaborative robotics.
In just 30 years' time, it is forecasted that the human population of our planet will be close to 10 billion. Producing enough food to feed these hungry mouths will be a challenge, and demographic trends such as urbanization, particularly in developing countries, will only add to that. To meet that challenge, agricultural businesses are pinning their hopes on technology, and that idea that increasingly sophisticated data and analytics tools will help to drive efficiencies and cut waste in agriculture and food production. Leading the way is John Deere – the 180-year-old manufacturer of farming and industrial machinery which has spent the past decade transforming itself into an artificial intelligence (AI) and data-driven business. I have covered John Deere before here.