Many believe we are living within the fourth industrial revolution, as the impact of smart technologies overtakes every area of our lives, including the engineering industry. It is marked by emerging technology breakthroughs in robotics, artificial intelligence, the Internet of Things, additive manufacturing/3D printing and fully autonomous vehicles. This revolution is expected to impact all disciplines, industries, and economies as connected machines begin to interact and, ultimately, even make decisions autonomously. The growth of additive manufacturing, a process that builds parts layer-by-layer from sliced CAD models to form solid objects, has the potential to become a new key technology. Electronics, engineering and medicinal electrical devices specialist Siemens has been investing in this innovative technology right from its inception, and is now driving the industrialisation and commercialisation of these processes.

Predictive maintenance based on machine learning has reached the point where it can benefit virtually every manufacturer, big or small, an expert will tell engineers at the upcoming Pacific Design & Manufacturing Show. Kayed Almasarweh, IBM's Watson and cognitive solutions lead, contends that machine learning and artificial intelligence can minimize unplanned downtime, eliminate maintenance guesswork, optimize supply chain management, and reduce warranty costs in products, if used correctly. "This is not only for big manufacturing operations; it's for everybody," Almasarweh told Design News. "Once you get it implemented with the right data, you can get a return on investment almost immediately." Almasarweh will provide a high-level view of predictive maintenance based on machine learning in a session titled, "Applying IoT and Machine Learning for Predictive Maintenance," at the Anaheim Convention Center on February 6th.

Whill released its first personal electric vehicle in 2016 (in Japan, it came west the following year). Since then it has released new models with different audiences in mind. Here at CES 2019, it's showing its Autonomous Drive System (ADS), which as the name suggests, will shuttle you to where you want to go on its own. Thanks to the addition of front- and rear-mounted cameras, the Autonomous Drive System can navigate public (indoor or outdoor) spaces on its own. A spokesperson told me that the ADS was partly its answer to scooters and bikes offered by the likes of Lime and Bird.

Sam Rothwell, HSO team lead, explores the manufacturing trends that we may see appearing in 2019. The comments below explore why there will be a further rise in the use of technologies such as AI, 3D printing and IoT as they become more affordable and are increasingly seen as vital tools to manufacturers. Rothwell says: "In the manufacturing sector, we have been seeing the rise of a number of key technologies recently, specifically artificial intelligence (AI), Internet of Things (IoT) and additive manufacturing (3D printing). "We expect, in 2019, to see this trend continue, especially in AI and IoT adoption, as manufacturers are increasing investment in these now affordable technologies which are underpinning more accurate decision making and unlocking new business models, such as shifting towards service-based product offerings. "The main operational areas we see potential in, are primarily around the predictive maintenance area, lowering costs by collecting data on real-world performance versus expected, then allowing adjustments to be made to future design. "This improvement in predictive maintenance has also allowed manufacturers to think differently about how they deliver value to their customer base, shifting from specific and individual product sales, towards ongoing subscription or per-usage billing.

This image shows the original paintings (across the top), as illuminated by different light sources (from left, 6410K, 4291K & 3410K) and below are the same paintings reproduced by the AI in RePaint. This image shows that RePaint works effectively in dramatically different lighting conditions.MIT CSAIL A team from the Massachusetts Institute of Technology (MIT) Computer Science and Artificial Intelligence Laboratory (CSAIL) has designed a system called RePaint that uses artificial intelligence (AI) and 3D printing to reproduce paintings. The system is a workflow for spectral reproduction of paintings can capture the spectral color, regardless of light, and reproduce it. This technology could be used by museums to reproduce artwork that has been stolen or is on loan to another museum. Despite the fact that the reproductions made by the researchers were only the size of a business card, the team noted that RePaint was four times more accurate than state-of-the-art physical models at creating the exact color shades for different artworks.

If you've ever wanted to get your hands on the Mona Lisa, you may just be in luck. For researchers have found a way for art fanatics to create their own version of a priceless masterpiece, through a combination of AI and 3D printing. The replicas have been made by researchers from the Massachusetts Institute of Technology (MIT) using a piece of software called'RePaint'. The replicas have been made by researchers from the Massachusetts Institute of Technology (MIT) using a piece of software called'RePaint' (pictured) Despite the progress so far, the team says they have a few improvements to make before they can whip up a dazzling dupe of'Starry Night.' 'If you just reproduce the colour of a painting as it looks in the gallery, it might look different in your home,' says Changil Kim, one of the researchers from MIT that published a paper on the system, which will be presented in December. 'Our system works under any lighting condition, which shows a far greater colour reproduction capability than almost any other previous work.'

Contemporary smart power grids are being challenged by rapid voltage fluctuations, due to large-scale deployment of renewable generation, electric vehicles, and demand response programs. In this context, monitoring the grid's operating conditions in real time becomes increasingly critical. With the emergent large scale and nonconvexity however, past optimization based power system state estimation (PSSE) schemes are computationally expensive or yield suboptimal performance. To bypass these hurdles, this paper advocates deep neural networks (DNNs) for real-time power system monitoring. By unrolling a state-of-the-art prox-linear SE solver, a novel modelspecific DNN is developed for real-time PSSE, which entails a minimal tuning effort, and is easy to train. To further enable system awareness even ahead of the time horizon, as well as to endow the DNN-based estimator with resilience, deep recurrent neural networks (RNNs) are pursued for power system state forecasting. Deep RNNs exploit the long-term nonlinear dependencies present in the historical voltage time series to enable forecasting, and they are easy to implement. Numerical tests showcase improved performance of the proposed DNN-based estimation and forecasting approaches compared with existing alternatives. Empirically, the novel model-specific DNN-based PSSE offers nearly an order of magnitude improvement in performance over competing alternatives, including the widely adopted Gauss-Newton PSSE solver, in our tests using real load data on the IEEE 118-bus benchmark system.

This method of concurrent 3D-printing, known as swarm printing, paves the way for a team of mobile robots to print even bigger structures in future. Developed by Assistant Professor Pham Quang Cuong and his team at NTU's Singapore Centre for 3D Printing, this new multi-robot technology was published in Automation in Construction, a top tier journal for civil engineering. The NTU scientist was also behind the Ikea Bot earlier this year where two robots assembled an Ikea chair in 8 min 55s. Using a specially formulated cement mix suitable for 3-D printing, this new development will allow for unique concrete designs currently not possible with conventional casting. Structures can also be produced on demand and in a much shorter period.

Results from Fanuc Corp. and Komatsu were a mixed bag Monday. Factory-automation giant Fanuc reported an 8.4 percent drop in fiscal first-half operating income and saw its shares rise, while construction-equipment-maker Komatsu posted an 80 percent profit surge that was rewarded with a stock decline. Put that perplexing share reaction down to the topsy-turvy world of machinery-makers, where investors tend to view dismal earnings as a sign that a company is nearing the bottom, and good results as a warning that it's close to the top. The overall picture, though, is that concerns sparked by U.S. bellwether Caterpillar Inc. last week of late-cycle cost pressures and a deteriorating China outlook have been overdone, at least as far as the Japanese firms are concerned. China's faltering economy has been a key focus. Fanuc's sales in the country, already shrinking, fell a further 42 percent in the quarter through Sept. 30, compared with the previous three months.

When you think of artificial intelligence (AI), chances are that a vision of supremely intelligent computers and robots taking over the world and enslaving the human race spring to mind. We've been conditioned by sci-fi books and movies to fear the worst. The reality is far different – mundane even. AI is basically the operation of algorithms that automatically optimize themselves as they go – a process known as'machine learning'. It may sound simple, but it yields powerful results that are revolutionizing the world we live in.