Factory Robots! See inside Tesla, Amazon and Audi's operations (supercut)
The future of mining is increasingly autonomous. Hexagon discusses building an autonomous program requires attention to detail and a wide range of technology incorporating command and control, safety and physical systems.
Plug-and-Play Robot Ecosystems on the Rise
Robot ecosystems are bringing plug-and-play ease to compatible hardware and software peripherals, while adding greater value and functionality to robots. Some might argue that the first robot ecosystem was the network of robot integrators that has expanded over the last couple decades to support robot manufacturers and their customers. Robot integrators continue to be vital to robotics adoption and proliferation. Yet an interesting phenomenon began to take shape a few years ago with the growing popularity of collaborative robots and the industry’s focus on ease of use.
Campbell describes the typical process for engineering a new gripping solution for a robot: “You have to first engineer a mechanical interface, which may mean an adapter plate, and maybe some other additional hardware. If you’re an integrator, it must be documented, because everything you do as an integrator you have to document. You have to engineer the electrical interface, how you’re going to control it, what kind of I/O signals, what kind of sensors. And then you have to design some kind of software.
“When I talk to integrators, they say it’s typically 1 to 3 days’ worth of work just to put a simple gripper on a robot. What we’ve been able to do in the UR+ program is chip away at time and cost throughout the project.”
The $150 Million Machine Keeping Moore’s Law Alive
ASML’s next-generation extreme ultraviolet lithography machines achieve previously unattainable levels of precision, which means chips can keep shrinking for years to come.
ASML introduced the first extreme ultraviolet (EUV) lithography machines for mass production in 2017, after decades spent mastering the technique. The machines perform a crucial role in the chipmaking ecosystem, and they have been used in the manufacture of the latest, most advanced chips, including those in new iPhones as well as computers used for artificial intelligence. The company’s next EUV system, a part of which is being built in Wilton, Connecticut, will use a new trick to minimize the wavelength of light it uses—shrinking the size of features on the resulting chips and boosting their performance—more than ever before.
COVID-19 Drives Industry 4.0 — and Reshoring
Resiliency concerns revealed by the COVID-19 pandemic are driving both reshoring and digital transformation. A realignment of priorities towards risk mitigation, agility, responsiveness and faster time-to-market are encouraging companies to shorten supply chains and reshore; 47 percent of small and medium-sized manufacturers (SMMs) are reevaluating supply chains.
To be cost-competitive, domestic manufacturers are looking to adopt Industry 4.0 technologies to close the labor price gap. New technologies are a game changer in achieving U.S. competitiveness and reshoring.
SnapEDA helps manufacturers find alternate chips in the semiconductor shortage
But Silicon Valley-based SnapEDA has the designs for entire catalogs of components — such as 75,000 components made by Panasonic — in its online catalog. More than 6.5 million parts are in the database.
“I created SnapEDA because I wanted to help product developers innovate faster,” SnapEDA CEO Natasha Baker said in an interview with VentureBeat. “Think of it like GitHub for electronics as a good analogy. But basically, it’s a place where product developers can go and get all the resources that they need to design electronics faster.”
Circular Car Factories
The next big shift will be an environmentally friendly movement dubbed the “circular auto factory.” According to some experts, the circular cars initiative will reshape the auto industry during the next two decades, as OEMs and suppliers attempt to achieve net-zero carbon emissions across the entire vehicle life cycle.
The term “circular car” refers to a theoretical vehicle that has efficiently maximized its use of aluminum, carbon-fiber composites, glass, fabric, rubber, steel, thermoplastics and other materials. Ideally, it would produce zero material waste and zero pollution during manufacture, utilization and disposal.
One of the key elements of a circular car factory is a closed-loop recycling program where disassembly lines are housed in the same facility as traditional final assembly lines. All vehicle components and materials are remanufactured, reused and recycled at the end of life.
Digital Part Inspection Software Creates New Business Opportunities
Converting the shop to a digital inspection management system didn’t feel like an option but a necessity. “We have to evolve this capability or we will be left behind,” Bobby says. He knew that other shops had solved the inspection equation and felt confident that digital management was the solution to the shop’s bottlenecks. When speaking about the decision to purchase the shop’s first seat of the software, Bob says, “We liked auto ballooning and we also liked the data capture and reporting.” But these features were just the beginning; the capabilities of the software were far reaching and changed the culture of the shop.
The Evolving Geography of the US Defense Industrial Base
Overall, defense contracts have grown more concentrated among fewer locations in the United States. Understanding where the U.S. defense industry is primarily located today offers indications of its connection to broader commercial sectors, whether they be in aviation, information technology, or other areas. The increased concentration of defense technologies and companies in geographic “clusters” could also exacerbate an already troubling divide between the U.S. military and the broader civilian population. Additionally, the changing landscape of the defense industry has implications for congressional oversight, as members of the legislative branch seek, or avoid, roles on key defense committees.
Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review
Failure prediction is the task of forecasting whether a material system of interest will fail at a specific point of time in the future. This task attains significance for strategies of industrial maintenance, such as predictive maintenance. For solving the prediction task, machine learning (ML) technology is increasingly being used, and the literature provides evidence for the effectiveness of ML-based prediction models. However, the state of recent research and the lessons learned are not well documented. Therefore, the objective of this review is to assess the adoption of ML technology for failure prediction in industrial maintenance and synthesize the reported results. We conducted a systematic search for experimental studies in peer-reviewed outlets published from 2012 to 2020. We screened a total of 1,024 articles, of which 34 met the inclusion criteria.
The on-going automotive semiconductor chip saga strikes again with GM and Ford cutting production of some of their most profitable vehicles. FedEx turns to AI robotic arms for package sorting just in time for the holiday season.