The first conviction for 3D printing a firearm was recently reported in London, not long after 3D printed masks were used to trick face recognition. Although 3D printing processes vary widely, including melting metal powder with lasers or hardening liquid plastic "ink" with ultraviolet light, most people tend to think of 3D printing desktop machines that melt spools of plastic. Since these are often built or designed by enthusiasts, they are very affordable, with some models costing under £200. We research the realities of 3D printer usage by businesses and consumers – and so can dispel some of the most common fears around 3D printing. Designs for a "gun" that could be produced on a desktop 3D printer were first shared on the internet around 2013.

While 3D printing soft materials, such as silicone or proteins, offers many advantages, it also introduces many new and complicated variables to consider when creating a new part. The existing soft materials that can be 3D printed commercially are somewhat limited since they don't have all the properties that researchers need to fully advance their developments and they end up working within the constraints of the current technology. One of the main problems with 3D printing a soft material is that it tends to deform under the forces that normally occur, sometimes even during the build, so they require support materials. According to researchers from the College of Engineering at Carnegie Mellon University, that means that additive manufacturing of soft materials requires optimization of printable inks, formulations of these feedstocks, and complex printing processes that must balance a large number of disparate but highly correlated variables (such as metal powder particle size, melt pool shape and size or filament feeding rate, extrusion width, linear plotting speed and layer thickness or suspension viscosity). Due to the critical need for integrated methodologies, they have come up with a hierarchical machine learning (HML) algorithm that optimizes parameters of these type of materials for 3D printing, using Freeform Reversible Embedding (FRE)–a recently developed method for 3D printing of liquid polymer precursors that involves controlled deposition of a fluid precursor into a supporting aqueous bath.

Squeezing a house through a nozzle, like a pâtissier pumping fondant cream from a piping bag, may not be everyone's idea of cutting-edge construction. The glitzy emirate aspires to have a quarter of all new buildings constructed via 3D printing by 2030. Emaar, one of the Arabian Gulf's leading property developers, is heralding its nascent Arabian Ranches III residential project as offering Dubai's first such dwelling. Fabricating a three-dimensional model, or prototype, from a computer-aided design by adding successive layers of material is now standard practice in many industries, ranging from aerospace and architecture to medicine and high-end manufacturing. McKinsey, the consultancy, estimates the technique could have an annual economic impact worth $550 billion by 2025.

In particular, small soft robots at millimeter scale are of practical interest as they can be designed as a combination of miniature actuators simply driven by pneumatic pressure. They are also well suited for navigation in confined areas and manipulation of small objects. However, scaling down soft pneumatic robots to millimeters results in finer features that are reduced by more than one order of magnitude. The design complexity of such robots demands great delicacy when they are fabricated with traditional processes such as molding and soft lithography. Although emerging 3D printing technologies like digital light processing (DLP) offer high theoretical resolutions, dealing with microscale voids and channels without causing clogging has still been challenging.

The demands on machine learning methods to cater for ultra high dimensional datasets, datasets with millions of features, have been increasing in domains like life sciences and the Internet of Things (IoT). While Random Forests are suitable for "wide" datasets, current implementations such as Google's PLANET lack the ability to scale to such dimensions. Recent improvements by Yggdrasil begin to address these limitations but do not extend to Random Forest. This paper introduces CursedForest, a novel Random Forest implementation on top of Apache Spark and part of the VariantSpark platform, which parallelises processing of all nodes over the entire forest. CursedForest is 9 and up to 89 times faster than Google's PLANET and Yggdrasil, respectively, and is the first method capable of scaling to millions of features.

In "Importance of Thinking Differently…Hint: Don't Pave the Cow Path", I introduced the concept of the "3 Horizons of Digital Transformation." I wanted to provide a framework that helped organizations differentiate between "Digitalization" versus "Digital Transformation". Unfortunately, in succeeding client engagements, I realized I did a crappy job of explaining these 3 horizons. So, like how bad movies create "Return of" sequels in order to explain everything they screwed up in the original movie, consider this my justification for "Return of the 3 Horizons of Digital Transformation" thriller! This "Return of" blog will provide more details on the 3 stages – or horizons – through which your organization must navigate in order to achieve Digital Transformation.

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.