Three rhinos defined and printed using OpenFab. This poses an enormous computational challenge: large high-resolution prints comprise trillions of voxels and petabytes of data, and modeling and describing the input with spatially varying material mixtures at this scale are simply challenging. Existing 3D printing software is insufficient; in particular, most software is designed to support only a few million primitives, with discrete material choices per object. We present OpenFab, a programmable pipeline for synthesis of multimaterial 3D printed objects that is inspired by RenderMan and modern GPU pipelines. The pipeline supports procedural evaluation of geometric detail and material composition, using shader-like fablets, allowing models to be specified easily and efficiently. The pipeline is implemented in a streaming fashion: only a small fraction of the final volume is stored in memory, and output is fed to the printer with a little startup delay. We demonstrate it on a variety of multimaterial objects. State-of-the-art 3D printing hardware is capable of mixing many materials at up to 100s of dots per inch resolution, using technologies such as photopolymer phase-change inkjet technology. Each layer of the model is ultimately fed to the printer as a full-resolution bitmap where each "pixel" specifies a single material and all layers together define on the order of 108 voxels per cubic inch. This poses an enormous computational challenge as the resulting data is far too large to directly precompute and store; a single cubic foot at this resolution requires at least 1011 voxels and terabytes of storage. Even for small objects, the computation, memory, and storage demands are large.

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.

--Additive Manufacturing (AM) is a manufacturing paradigm that builds three-dimensional objects from a computer-aided design model by successively adding material layer by layer . AM has become very popular in the past decade due to its utility for fast prototyping such as 3D printing as well as manufacturing functional parts with complex geometries using processes such as laser metal deposition that would be difficult to create using traditional machining. As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run. However, as the simulations are computationally costly and time-consuming for predicting multiscale multi-physics phenomena in AM, physics-informed data-driven machine-learning systems for predicting the behavior of AM processes are immensely beneficial. Such models accelerate not only multiscale simulation tools but also empower real-time control systems using in-situ data. In this paper, we design and develop essential components of a scientific framework for developing a data-driven model-based real-time control system. Finite element methods are employed for solving time-dependent heat equations and developing the database. The proposed framework uses extremely randomized trees - an ensemble of bagged decision trees as the regression algorithm iteratively using temperatures of prior voxels and laser information as inputs to predict temperatures of subsequent voxels. The models achieve mean absolute percentage errors below 1% for predicting temperature profiles for AM processes. Additive Manufacturing (AM) is a modern manufacturing approach in which digital 3D design data is used to build parts by sequentially depositing layers of materials [1]. AM techniques are becoming very popular compared to traditional approaches because of their success in building complicated designs, fast prototyping, and low-volume or one-of-a-kind productions across many industries. Direct Metal Deposition (DMD) [2] is an AM technology where various materials such as steel or Titanium are used to develop the finished product.

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.

We present a novel unsupervised deep learning approach that utilizes the encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed not only to detect whether there exists an anomaly at a given time step, but also to predict what will happen next in the (sequential) process. We demonstrate our approach on a dataset collected from a real-world testbed. The dataset contains images collected under both normal conditions and synthetic anomalies. We show that the encoder-decoder model is able to identify the injected anomalies in a modern manufacturing process in an unsupervised fashion. In addition, it also gives hints about the temperature non-uniformity of the testbed during manufacturing, which is what we are not aware of before doing the experiment.

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.

A futuristic 3D-printed house that lets guests'experience Mars on Earth' will soon offer you the chance to experience what an interplanetary vacation of the future may be like, its creators say. Nestled in the woods of upstate New York along the Hudson River, Tera will be hired out to holiday-makers hoping to experience what sustainable life could be like on Mars. 'Tera' is the brainchild of AI SpaceFactory, a New York City design agency that was awarded $500,000 (£386,000) earlier this year for winning NASA'S 3D-Printed Habitat Challenge with its previous'Marsha' habitat. Each stay will be used to fund the mission of the firm behind its design, which hopes to research and develop the renewable and sustainable technologies of the future. This technology will be used both here on Earth and, they say, will be one day form the basis of a sustainable colony on the red planet.

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.