Rare earth elements, which are important in the development of microchips, electronics, and electric motors, have become extremely sought after, and for decades, these elements were almost exclusively sourced in China. Nevertheless, several nations have, in recent years, either initiated production or increased their current production, resulting in a considerably more diverse rare earths market. 

These machine vision processes provide flexibility and ease of operation, while extending a manufacturer's vision capabilities in terms of resolution, speed, interfaces, and flexibility. These solutions give powerful visual quality control, identification, error proofing, traceability, and image processing.
 
Among their many uses, Balluff machine vision is ideal for:
 
Optical-based traceability
Verifying automated processes
Vision-guided robotics
Our image processing products can be set up without the need for prior knowledge of these systems. The industrial cameras are ready in color and monochrome and four different image resolutions, allowing for great flexibility in installation.
 
Camera Features:
Available in GigE and USB3
GigE industrialized M12 connector is more robust than common RJ45 connectors
Controller Features:
Standardized industrial network interfaces, such as Ethernet IP, Profinet, TCP/UDP
Flexible for applications with single or multiple cameras
Fast data processing based on the user-friendly BVS Cockpit interface



This article is originally posted on Tronserve.com

DENSO, the world's second largest mobility supplier, has invested $1.95 million to open the North Technical Training Center at its Battle Creek, Michigan, facility. The center is developed to be at the front of automated and data-rich manufacturing, also recognized as Industry 4.0, and will act as a training hub for DENSO technical talent. It's an urgent resource for the company as automotive and manufacturing technology speedily develops and is created with DENSO's Long-Term Policy 2030 in mind, which aims to create new value for advanced mobility.
 
As the auto industry steps toward electrification and automated driving, DENSO is devoted to providing advanced, tech-forward training grounds for its employees. DENSO's Battle Creek training center is the third of its kind for the company, with others in Tennessee and Mexico. It will serve employees in technical fields, such as skilled trades, machine technicians and engineering. Employees identified for training will get a customized plan to create new technical skills, gain new understanding of Internet of Things (IoT) technologies and improve production efficiency - all of which are key factors in boosting functional productivity and profitability.
 
'It's a vital time in the auto industry and we cannot transform manufacturing and engineering without our people. That is why DENSO is concentrated on introducing our employees to new technologies, delivering cross training for collaboration and developing skills that breed innovation,' said Kenichiro Ito, senior executive officer for DENSO Corporation and chief executive officer of DENSO's North American Headquarters. 'The gifted workers who learn at the Technical Training Center will come out with new ways and creative ideas to change the world through manufacturing.'
 
Kevin Carson, president of DENSO's thermal manufacturing facility in Battle Creek, said, 'When most of our work force was in school, there was no such thing as IoT, automation or Industry 4.0. The automotive industry is greatly changing and that means our machines and the technology that powers them must change.
 
'We are committed to supporting our employees and ensuring they have the resources necessary to stay nimble, learn new skills and flourish in their careers.'



This article is originally posted on Tronserve.com

Servitization — where companies shift from firmly new product sales to rather selling the outcome a product delivers — and change go hand-in-hand. And, we all know that without regard for the circumstances, change can be unnerving. The manufacturing industry particularly has remained relatively unchanged for decades, but this new era is demanding original equipment manufacturers (OEMs) to entirely upend the status quo. 

Industry 4.0 brings relatively numerous opportunities, including seemingly unrestricted choices, for technology investments. Here are just some of the many challenges companies face when looking to deal with Industry 4.0:

One of the primary misconceptions in manufacturing is that outsourced IT only solves back-office struggles. With greater regularity, it is seen as an approach to establish workflows for preventable concerns, whether they're routine updates or break-fix employee tickets.

Drone racing’s ultimate vision of quadcopters weaving nimbly through hurdle courses has attracted far less excitement and investment than self-driving cars directed at reshaping ground transportation. But the U.S. military and defense industry are betting on autonomous drone racing as the next frontier for developing AI so that it can handle high-speed navigation within tight spaces without human involvement.
 
The autonomous drone challenge needs split-second move-making with six degrees of freedom instead of a car’s mere two degrees of road freedom. One research team developing the AI necessary for controlling autonomous racing drones is the Robotics and Perception Group at the University of Zurich in Switzerland. In late May, the Swiss researchers were among nine teams revealed to be contending in the two-year AlphaPilot open innovation challenge sponsored by U.S. aerospace company Lockheed Martin. The winning team will walk away with up to $2.25 million for defeating other autonomous racing drones and a professional human drone pilot in head-to-head events.
 
“I think it is crucial to first point out that with an autonomous drone to finish a racing track at high speeds or even beating a human pilot does not imply that we can have autonomous drones [capable of] navigating in real-world, complex, unstructured, unknown environments such as disaster zones, collapsed buildings, caves, tunnels or narrow pipes, forests, military scenarios, and so on,” says Davide Scaramuzza, a professor of robotics and perception at the University of Zurich and ETH Zurich. “However, the robust and computationally efficient state evaluation algorithms, control, and planning algorithms formulated for autonomous drone racing would represent a starting point.”
 
The nine teams that made the cut—from a pool of 424 AlphaPilot applicants—will compete in four 2019 racing events organized under the Drone Racing League’s Artificial Intelligence Robotic Racing Circuit, says Keith Lynn, program manager for AlphaPilot at Lockheed Martin. To guarantee an apples-to-apples comparison of each team’s AI secret sauce, each AlphaPilot team will upload its AI code into similar, specially-built drones that have the NVIDIA Xavier GPU at the core of the onboard computing hardware.
 
“Lockheed Martin is providing mentorship to the nine AlphaPilot teams to assist their AI tech development and innovations,” says Lynn. The company “will be hosting a week-long Developers Summit at MIT in July, devoted to workshopping and improving AlphaPilot teams’ code,” he added. He notes that each team will keep the intellectual property rights to its AI code.
 
The AlphaPilot challenge takes determination from older autonomous drone racing events hosted by academic researchers, Scaramuzza says. He credits Hyungpil Moon, a professor of robotics and mechanical engineering at Sungkyunkwan University in South Korea, for having organized the annual autonomous drone racing competition at the International Conference on Intelligent Robots and Systems since 2016.
 
It’s no easy task to build and train AI that can perform high-speed flight through intricate environments by relying on visual navigation. One big problem comes from how drones can accelerate sharply, take sharp turns, fly sideways, do zig-zag patterns and even perform back flips. That means camera images can suddenly appear tilted or even upside down during drone flight. Motion blur may occur when a drone flies very close to structures at high speeds and camera pixels collect light from multiple directions. Both cameras and visual software can also challenge to compensate for sudden changes between light and dark parts of an environment.
 
To lend AI a helping hand, Scaramuzza’s group recently exhibited a drone racing dataset that contains sensible training data taken from a drone flown by a professional pilot in both indoor and outdoor spaces. The data, which consists of complicated aerial maneuvers such as back flips, flight sequences that cover hundreds of meters, and flight speeds of up to 83 kilometers per hour, was offered at the 2019 IEEE International Conference on Robotics and Automation.
 
The drone racing dataset also features data grabbed by the group’s special bioinspired event cameras that can identify changes in motion on a per-pixel basis within microseconds. By comparison, common cameras need milliseconds (each millisecond being 1,000 microseconds) to compare motion changes in each image frame. The event cameras have already proven capable of helping drones nimbly dodge soccer balls thrown at them by the Swiss lab’s researchers.
 
The Swiss group’s work on the racing drone dataset obtained funding in part from the U.S. Defense Advanced Research Projects Agency (DARPA), which acts as the U.S. military’s special R&D arm for more innovative projects. Exclusively, the funding came from DARPA’s Fast Lightweight Autonomy program that envisions smaller autonomous drones capable of flying at high speeds through cluttered environments without GPS guidance or communication with human pilots.
 
Such speedy drones could serve as military scouts checking out dangerous buildings or alleys. They could also someday help search-and-rescue teams find people trapped in semi-collapsed buildings or lost in the woods. Being able to fly at high speed without crashing into things also makes a drone more excellent at all kinds of tasks by making the most of restricted battery life, Scaramuzza says. After all, most drone battery life gets used up by the need to hover in flight and doesn’t get emptied much by flying faster.
 
Even if AI manages to conquer the drone racing obstacle courses, that would be the end of the beginning of the technology’s development. What would still be involved? Scaramuzza specifically singled out the need to handle low-visibility conditions involving smoke, dust, fog, rain, snow, fire, hail, as some of the biggest challenges for vision-based algorithms and AI in complicated real-life environments.
 
“I think we should develop and release datasets containing smoke, dust, fog, rain, fire, etc. if we require to allow using autonomous robots to complement human rescuers in saving people lives after an earthquake or natural disaster in the future,” Scaramuzza says.



This article is originally posted on Tronserve.com

Camarillo, CA - June 5, 2019 - Binder USA, LP has integrated to its 720 Series of tiny round connectors with a new snap-in IP67 twin distributor. The new twin distributor - a single male connector into two female connectors - provides a welcome new option for designers and installers who want a miniature circular connector for today's machine building and automation applications. The new twin distributor features 3 and 5 gold-plated contacts rated up to 7A at 250V (5A at 125 V). The twin vendor is also a streamlined robust splitter, the ideal solution for splitting off a connection with two covered paths.
 
The IP67 protection rating makes the binder Twin Distributor ideal for use in applications where a simple waterproof connection is recommended. Medical grade versions are also available.
Product highlights consist of:
binder Series: 720
Twin distributor: male connector into two female connectors
Wiring: 1:1
Contacts: 3 and 5
Degree of protection: IP67
Rated voltage: 250 V - 125 V
Rated current: 7 A - 5A (per conductor)
Contact plating: Au (gold)
Colors: Black (standard), red, blue, or green



This article is originally posted on Tronserve.com

Hoffman Estates, IL., June 7, 2019 - Industrial automation solutions provider Omron Automation Americas has included more than 2,500 new models to its E2E NEXT line of proximity sensors. Highlights of the new models come with an increased sensing distance that minimizes place contact during output and IoT capabilities for improved predictive maintenance.
 
Contact avoidance is crucial for maximizing uptime. Seventy percent of unexpected equipment downtime is triggered by component failures, of which proximity sensors account for a large proportion. E2E sensors are designed to help manufacturers reduce possibilities causes of unexpected downtime while benefiting from the sensors' environmental resistance.
 
The improvement of IO-Link functionality allows the sensors to decrease recovery time by indicating the location and cause of deficiencies. They can also notice warning signs of impending failures and notify users via the network. The combination of IoT capabilities, long sensing distance, and oil resistant sheathing reduces the risk of sudden equipment shutdowns by a factor of three.
 
In recap, Omron's new E2E proximity sensors are:
 
•              Stable. The E2E NEXT line's long sensing distance prevents unexpected equipment downtime caused by target contact, making the continued operations more stable.
•              Flexible. The extra-long sensing distance makes it possible to solve size-limited applications with smaller form factor sensors.
•              IoT-enabled. The DC 3-wire models use IO-Link to help identify the location and cause of failures in real time.
•              Oil-resistant. E2E sensors are resistant to cutting oil, which accounts for approximately 30% of unexpected component failures.
•              Easy to use. The sensors' user-friendly design makes it easy to confirm detection status and ensures that your facility can recover quickly without requiring advanced support.



This article is originally posted on Tronserve.com

Every now and then an experimental plane is displayed in an endeavor to display the innovative spirit of the organization building it, as part engineering experiment and part marketing project. The latest display is a university student’s design picked up by the national carrier KLM Royal Dutch Airlines.

An industrial revolution is an essential shift in the way industry works that forever changes the way manufacturing is done. Nevertheless, besieged by bright shiny objects of Industry 4.0, manufacturers should focus on lasting goals other than being swept up by the abundance of new technologies, such as cloud, big data, IoT, mobile, location, additive manufacturing, edge computing, miniaturization, augmented and virtual reality, artificial intelligence (AI), automation, and robotics.

The concern that technology will swap a job or worker has existed for centuries. The infamous example of Luddites breaking textile machinery to preserve their jobs perhaps have happened in the 19th century, but many in the 21st century still react to change with defensiveness and concern.

Amazon stated Wednesday that it plans to use self-piloted drones to supply packages to shoppers' home in the upcoming months.

At Amazon’s re:MARS seminar in Las Vegas today, who else but Amazon is introducing two new robots designed to make its fulfillment centers even more satisfying. Xanthus (named after a mythological horse that could very temporarily talk but let’s not read too much into that) is a thoroughly redesigned drive unit, one of the robotic mobile bases that carries piles of stuff around for humans to pick from. It has a thinner profile, a third of the parts, costs half as much, and can wear different modules on top to perform a much wider variety of tasks than its predecessor.
 
Pegasus (named after a mythological horse that could fly but let’s not read too much into that either) is also a mobile robot, but much smaller than Xanthus, designed to help the company quickly and effectively sort individual packages. For Amazon, it’s a totally new large-scale robotic system involving tightly coordinated fleets of robots tossing boxes down chutes, and it’s just as fun to watch as it sounds.
 
Amazon has 800 Pegasus products just deployed at a sorting facility in the United States, adding to their newly updated total of 200,000 robotic drive units worldwide. If the Pegasus system looks familiar, it’s because other warehouse automation companies have had something that’s at least superficially very similar up and running for years.
 
But the most interesting headline that Amazon made, kind of low key and right at the end of their re:MARS talk, is that they’re working on ways of making some of their mobile robots actually collaborative, leveraging some of the technology that they acquired from Boulder, Colo.-based warehouse robotics startup Canvas Technology earlier this year:
 
“With our recent acquisition of Canvas, we wish to be able to combine this drive platform with AI and autonomous mobility capabilities, and for the first time, let our robots to move outside of our robotic drive fields, and interact collaboratively with our associates to do a number of mobility tasks,” said Brad Porter, VP of robotics at Amazon.
 
At the moment, Amazon’s robots are actually separated from humans except for one hugely structured station where the human only communicates with the robot in one or two very specific ways. We were told a few months ago that Amazon would like to have mobile robots that are able to move things through the areas of fulfillment centers that have people in them, but that they’re (quite rightly) worried about the safety aspects of having robots and humans work around each other. Other companies are already doing this on a smaller scale, and it means developing a reliable safety system that can deal with randomly moving humans, environmental changes, and all kinds of other stuff. It’s much more difficult than having a nice, clean, roped-off area to work in where a wayward human would be an exception rather than just another part of the job.
 
It now sounds like Canvas has delivered the secret sauce that Amazon wanted to launch implementing this level of autonomy. As for what it’s going to look like, our best guess is that Amazon is going to have to do a little bit more than slap some additional sensors onto Xanthus or Pegasus, if for no other reason than the robots will almost certainly need more ground clearance to let them operate away from the reliably flat floors that they’re accustomed to. We’re expecting to see them performing many of the tasks that companies like Fetch Robotics and OTTO Motors are doing already — moving everything from small boxes to large pallets to keep humans from having to waste time walking.
 
Of course, this all feeds back into what drives Amazon more than anything else: efficiency. And for better or worse, humans are not distinctively good at moving things from place to place, so it’s no surprise that Amazon wants to automate that, too. The good news is that, at least for now, Amazon actually needs humans to babysit all those robots.
 



This article is originally posted on Tronserve.com

Amazon has been working away at its Prime Air urban and suburban drone delivery for years. Many years. It’s been at least half a decade now. And for the entire time, we’ve been complaining that Amazon has been focusing on how to build drones that can physically transport objects rather than how to build drones that can safely and reliably transport objects in a manner that makes economic sense and that people actually need.
 
At its re:MARS conference today, Amazon showed off a brand-new version of its Prime Air drone. The design is really unique, highlighting a hybrid tailsitter design with 6 degrees of freedom, but people have been futzing with weird drone designs for a long time, and this may or may not be a.) what Amazon has really settled on long-term or b.) the best way of doing things, versus other strategies like Google Wing’s dangly box.
 
What’s much more fun is that Amazon looks to now be addressing the issue of safety, and has added a comprehensive suite of on-board sensing and computing that will help the drone deal with many of the complicated barriers that it’s likely to encounter while doing its job.
 
We should point out right away that Amazon’s soothing piano music signifies that you cannot hear what this drone sounds like in flight, and noise is turning out to be one of the biggest problems with urban and suburban delivery drones, as Google Wing has noticed in Australia. Amazon seems to be taking the same “oh people will just get used to it” approach as Google is, and for better or worse that’s probably what’s going to end up happening. Sigh.
 
The pretty cool bit about today’s announcement is the addition of sense and avoid to Amazon’s drones, which Jeff Wilke, Amazon’s chief executive for worldwide consumer, detailed in a blog post. This is a good start, although I would push back a tiny bit on the assertion that Wilke ends with that “our drones are safe.” This technology undoubtedly has the potential to make Amazon’s drones much safer than they were before, but my guess is that statements like “our drones can recognize and avoid wires” would probably be more precisely written as “our drones have the ability to understand and avoid wires most of the time when conditions are favorable.'
 
Our drones need to be able to identify static and moving objects coming from any direction. We employ diverse sensors and advanced algorithms, such as multi-view stereo vision, to detect static objects like a chimney. To notice moving objects, like a paraglider or helicopter, we use proprietary computer-vision and machine learning algorithms. For the drone to descend for delivery, we need a small area around the delivery location that is clear of people, animals, or obstacles. We determine this using explainable stereo vision in parallel with sophisticated AI algorithms taught to identify people and animals from above.
 
A customer’s yard may have clotheslines, telephone wires, or electrical wires. Wire detection is one of the hardest problems for low-altitude flights. Through the use of computer-vision techniques we’ve created, our drones can know and avoid wires as they descend into, and ascend out of, a customer’s yard. This is a good start, although I would push back a little bit on the assertion that Wilke ends with that “our drones are safe.” This technology definitely has the potential to make Amazon’s drones much safer than they were before, but my guess is that statements like “our drones can recognize and avoid wires” would probably be more properly written as “our drones have the capability to recognize and prevent wires most of the time when conditions are favorable.”
 
And even with all this progress, I can’t help but come back to the fundamental question of whether this kind of drone delivery is actually worth it. I love robots, and I’m having a truly hard time thinking of this as anything more than a novelty, especially considering the growth of both autonomous vehicles and sidewalk robots (which Amazon is also working on). Amazon brings up the environmental impact of delivery as another argument in favor of drones, suggesting that “an electric drone, charged using sustainable means, traveling to drop off a package is a vast improvement over a car on the road.” Likely true, as long as the car is delivering just one package—I’m not sure how the numbers work out if you’re comparing drones to a loaded delivery van, though. And again, noise pollution needs to be thought about, too.
 
Delivery drones are the right answer, I think, in some cases. Medical supply delivery is one. Remote delivery is another. It’s less clear whether suburban delivery really fills a long-term need, or whether companies like Amazon and Google are mostly just doing it because they can. But either way, it’s great to see Amazon acknowledging these hard problems, and we’re looking forth to seeing some of their technologies, like obstacle avoidance, in action, which Amazon says could happen within months.



This article is originally posted on Tronserve.com

[WEST CHESTER, OH - JUNE 2019] The Fastems RoboCell One sets new requirements in respect to incredibly adaptable and flexible machining, specially heavy workpieces within batches of various sizes. The easy-to-configure robot cell is basically fashioned for handling heavy workpieces weighing up to 176 lbs. (80 kg) and for automating up to two machine tools of the same type - either lathes or milling machines. The solution consequently comes into its own at the point where existing automation systems reach their limits solely due to the maximum weight of the workpiece.
 
The RoboCell One has been exclusively manufactured for the versatile production of different batch sizes such as a wide variety of components. A special feature of the robot cell is an optional, automated gripper change system for the versatile handling of workpieces and the simple implementation of new components - without interrupting production operations. The robot can correctly be mounted with single, double, or special grippers. Up to six different grippers may be used for handling workpieces in specific production operations.
A single robot that can be handled in one linear axis is able to provide two devices with workpieces in a flexible manner. Further, different products can be simultaneously produced by using both machines together. To accomplish this scenario, the robot automatically transforms its grippers for loading/unloading the respective unit. The result is optimal use of machine capacity, even when orders are frequently adjusted.
Above all, through its use in combination with the Fastems Manufacturing Management Software (MMS) for controlling the robot cell and the machines that are connected to it, RoboCell One provides virtually unrivalled advantages for production operations. The MMS automatically plans the entire production plan dependent on the production orders, including the changes of workpieces for batches of different sizes, and taking account of all the resources that are necessary. In addition, the software shows the current production status in real time (order progress, schedule, etc.), it computes the machinery capacities for exceptional orders, and it shows the operator in advance the machinery retooling operations that are needed. This means that not only are production costs per workpiece reduced, throughput times are also cut thanks to the anticipatory MMS detailed planning.
When the RoboCell One is first switched to the production of a new workpiece, virtually no knowledge of robotics is necessary. This greatly simplifies production, particularly in the case of groups of components. Instead of the robot having to be laboriously taught, it is configured through parametric programming via the MMS. The necessary values and/or parameters for workpiece handling are stored in the control system via the intuitive MMS interface. This means that new parts master data and orders can be produced both quickly and simply during production operations, i.e. whilst the machine is actually moving.



This article is originally posted on Tronserve.com

MONROVIA, CA - AMADA MIYACHI AMERICA Inc., a leading manufacturer of resistance welding, laser welding, marking, cutting, and micromachining equipment and systems, announces the new MM-L300A Laser Weld Monitor. The high-resolution and compact MM-L300A offers operators real-time feedback on laser weld quality, fashioned to recognize production errors such as gaps between parts, missing parts, over-penetration, incorrect focus, and cover gas absence. The compact, lightweight unit supports laser welding technologies for spot or seam welds.
 
This high-accuracy monitor is best for process development and quality control applications of laser welding. The MM-L300A decides weld success by discovering and recording a thermal signal from the area of laser interaction and provides an output waveform around which limits (max/min or envelope) can be set. Once the limits are set, the unit compares a new weld waveform in real time to identify good or bad weld. Providing high temporal resolution—down to 1 microsecond the MM-L300A with the SU-N300A committed thermal sensor allows precision monitoring of both CW and pulsed lasers.
The MM-L300A features easy-to-use software for quick sensor configuration, waveform envelope limit set-up, and real-time or saved waveform analysis on Windows® PCs. Plus, with machine-selectable setup schedules, the unit can monitor different welding conditions. For process design flexibility, this approximately 7 lb (3 kg) system reduces set-up space when integrated into a production line, and the sensor mounts either on the optical axis of the laser or in an off-axis position.



This article is originally posted on Tronserve.com

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Afghanistan, Kabul
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Togo, Lome, 824,738
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