For the past decade, policymakers and nongovernmental organizations have pushed for greater transparency in supply chains, with the goal of encouraging more responsible sourcing practices. The Dodd-Frank Act, for example, required firms to disclose their suppliers’ involvement with any “conflict minerals” such as gold, tin, or tantalum, a metal used in phones and computers. More recently, France passed legislation to ensure carbon emissions reporting.

At the same time, many companies have pledged to be more vigilant and open about protecting the people who manufacture their products. After the Rana Plaza disaster in Bangladesh in 2013—a building collapse that killed more than a thousand garment and textile workers—a number of brands joined a coalition to hold their suppliers accountable.

Buildings account for about 40 percent of U.S. energy consumption, and are responsible for one-third of global carbon dioxide emissions. Making buildings more energy-efficient is not only a cost-saving measure, but also a crucial climate-change mitigation strategy. Hence the rise of “smart” buildings, which are increasingly becoming the norm around the world.

Smart buildings automate systems like heating, ventilation, and air conditioning (HVAC), lighting, electricity, and security. Automation requires sensory data, such as indoor and outdoor temperature and humidity, carbon dioxide concentration, and occupancy status. Smart buildings leverage data in a combination of technologies that can make them more energy-efficient.

Since HVAC systems account for nearly half of a building’s energy use, smart buildings use smart thermostats, which automate HVAC controls and can learn the temperature preferences of a building’s occupants.

Last month, Google forced out a prominent AI ethics researcher after she voiced frustration with the company for making her withdraw a research paper. The paper pointed out the risks of language-processing artificial intelligence, the type used in Google Search and other text analysis products.

Among the risks is the large carbon footprint of developing this kind of AI technology. By some estimates, training an AI model generates as much carbon emissions as it takes to build and drive five cars over their lifetimes.

I am a researcher who studies and develops AI models, and I am all too familiar with the skyrocketing energy and financial costs of AI research. Why have AI models become so power hungry, and how are they different from traditional data center computation?

The future of advanced manufacturing in the United States is being built at innovative facilities that enable experimentation in process and product development. The people and organizations at these next-generation facilities are part of a collaborative effort to remove barriers of entry and create an ecosystem to build supply chains and provide a path for the commercialization of emerging technologies.

These facilities are working on initiatives that include:
• Using advanced fiber technology to make programmable backpacks that have no wires or batteries but connect to the digital world.
• Using light instead of electronics to power cloud-based data centers, increasing the speed of transfer tenfold while drastically reducing energy use and cost.
• Extending the range of electric vehicles by reducing weight and mitigating energy loss during transfers.

This would not be possible without Manufacturing USA, a network of 16 manufacturing innovation institutes and their sponsoring federal agencies—the Departments of Commerce, Defense, and Energy. Manufacturing USA was created in 2014 to secure U.S. global leadership in advanced manufacturing by connecting people, ideas, and technology.

A team of Lawrence Livermore National Laboratory (LLNL) scientists has simulated the droplet-ejection process in an emerging metal 3D-printing technique called “liquid metal jetting” (LMJ), a critical aspect to the continued advancement of liquid metal printing technologies.

In their paper, which was published in the journal Physics of Fluids, the team describes the simulating of metal droplets during LMJ, a novel process in which molten droplets of liquid metal are jetted from a nozzle to 3D-print a part in layers. The process does not require lasers or metal powder and is more similar to inkjet printing techniques.

Using the model, researchers studied the primary breakup dynamics of the metal droplets, essential to improving the understanding of LMJ. LMJ has advantages over powder-based approaches in that it provides a wider material set and does not require production or handling of potentially hazardous powders, researchers said.

This story was originally published by Knowable Magazine.

When Stefanie Tellex was 10 or 12, around 1990, she learned to program. Her great-aunt had given her instructional books, and she would type code into her father’s desktop computer. One program she typed in was a famous artificial intelligence program called ELIZA, which aped a psychotherapist. Tellex would tap out questions, and ELIZA would respond with formulaic text answers.

“I was just fascinated with the idea that a computer could talk to you, that a computer could be alive, like a person is alive,” Tellex says. Even ELIZA’s rote answers gave her a glimmer of what might be possible.

Advanced metal alloys are essential in key parts of modern life, from cars to satellites, from construction materials to electronics. But creating new alloys for specific uses, with optimized strength, hardness, corrosion resistance, conductivity, and so on, has been limited by researchers’ fuzzy understanding of what happens at the boundaries between the tiny crystalline grains that make up most metals.

When two metals are mixed together, the atoms of the secondary metal might collect along these grain boundaries, or they might spread out through the lattice of atoms within the grains. The material’s overall properties are determined largely by the behavior of these atoms, but until now there has been no systematic way to predict what they will do.

Researchers at MIT have now found a way, using a combination of computer simulations and a machine-learning process, to produce the kinds of detailed predictions of these properties that could guide the development of new alloys for a wide variety of applications. The findings are described today in the journal Nature Communications, in a paper by graduate student Malik Wagih, postdoc Peter Larsen, and professor of materials science and engineering Christopher Schuh.

The ongoing global Covid-19 pandemic has forced companies of all types to rapidly update policies and procedures governing how they share information in response to a world that is constantly changing around them. For the manufacturing sector in particular, their workforce is more spread out than it has ever been, but communication remains essential.

Many knew that telecommuting was the future of the workforce in the United States—it’s just that few could have predicted that “the future” would have come along quite as quickly as it did. In April 2020, at the peak of the first wave of the pandemic, a massive 51 percent of people were working from home. Although that number had dropped to 33 percent by the following October, it’s still enormous, creating a challenge for the manufacturing sector in particular.

Thankfully, a wide range of digital tools have emerged for manufacturing companies that not only enable the rapid communication need right now, but that may also put them in a better decision-making position than they were in before all of this began.

Whether you subscribe to the scientific definition of data (information on which operations are performed by a computer and transmitted in the form of electrical signals) or the philosophical definition (that which is known and used as the basis of reasoning or calculation), I think most people use the word “data” incorrectly.

If you’re a data scientist, or you become upset that this will be the only time I use the singular form “datum,” this article will probably disgust you, and I apologize. On the other hand, if you’re in marketing, sales, or just about any other department, then I hope this will help clarify the overused but super-useful word “data.”

One of the roots of the problem with the word is when it’s used as a generic noun. It’s overused and causes confusion. Confusion is the enemy, particularly in sales and marketing. Granted, data is a useful word because it’s short. When used among a group of people who work with the same type of data, the term works as a reference for what everyone knows you’re talking about.

Digital applications in manufacturing are not only becoming increasingly accepted; they are expected. However, for smaller manufacturers, the process of making this switch can be daunting. Initial expenses, as well as the cost of training employees, is enough to stop the process altogether.

But beginning the process of “going digital” doesn’t have to be overwhelming. With a little guidance and education, all manufacturers can start to implement digital manufacturing concepts in a staged approach that best fits their individual work environments. Here are our top five recommendations for digital applications that can help you get started.

1. Digital performance management

Since 2010, the percentage of business that is conducted digitally has grown from 4 percent to almost 12 percent, and that trend is expected to continue. Finding an integrated way to analyze both business and IT metrics is key to optimizing the experience of this growing enterprise. Enter data performance management.