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Versatile Transport System: Turbocharge conveyance

THK's Versatile Transport System is a high-mix production solution that will keep your production line moving. Its linear motor drive enables high-speed operations, and processing can be performed directly on top of the system's freely recirculating sliders. This highly precise, modular system has many unique features, including easily adjustable stop positions, flex layouts with path splitting and parallelization, and easy addition/subtraction of extension pieces.
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Tech Tip: How to keep heavy loads balanced

Some Thomson smart linear actuators have a position-based synchro-nization option to help manage unbalanced loads when using multiple units. The system adjusts the speed of each actuator to keep them starting, moving, and stopping synchronously, regardless of their respective load distribution. So useful. So smart.
Learn all about this feature.

Micropositioning stages ensure high accuracy

PI now offers fast delivery of the L-511 linear microposi-tioning stage, which is designed for applications requiring minimum incremental motion down to 20 nm and drive forces up to 22 lb. The L-511 can be combined to form XY or XYZ motion systems and integrated with rotary stages for enhanced flexibility. Features high-load recirculating ball bearings for exceptional durability, even under demanding, repetitive cycles. To enhance positioning accuracy and automation throughput, this stage integrates non-contact, direction-sensing optical reference point switches located at mid-travel.
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Robots think and act on the fly at moving assembly line speeds

Inbolt and FANUC are launching a manufacturing breakthrough enabling FANUC robots to tackle one of the most complex automation challenges: performing production tasks on continuously moving parts at line speeds. With Inbolt's AI-powered 3D vision, manufacturers can now automate screw insertion, bolt rundown, glue application, and other high-precision tasks on parts moving down the line without costly infrastructure investments or cycle time compromises.
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Best high-speed rotary bearing in THK history

THK has developed its best-performing, high-speed rotary bearing ever: the High-Speed, Double-Row Angular Contact Ring BWH. This rotary bearing has balls aligned inside a cage between the inner and outer rings and is part of the THK Rotary Series, along with the cross-roller ring. The main features of this product are its ability to receive loads in all directions as well as its high rigidity and rotational accuracy, which are equal to that of cross-roller rings. By adopting a new structure to change the rolling elements from rollers to balls, this product achieves the greatest high-speed performance ever offered by THK.
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Elevating tables: Precise vertical positioning in tight spaces

As semicon-ductors and optical components become smaller and more sophisticated, the TZ Series of precision elevating tables from IKO International provides exceptional vertical positioning accuracy in a compact size. This unit features a unique wedge mechanism guided in the vertical direction by a pair of IKO C-Lube Super MX linear motion rolling guides arranged in parallel to achieve highly precise positioning with exceptional rigidity. An optional linear encoder provides full closed loop control to achieve positioning accuracy as high as 0.005 mm, with repeatability of +/-0.001 mm.
Learn more and get all the specs.

This cobot is all about safety around people

The COBOTTA PRO from DENSO Robotics is a lightweight, high-speed collaborative robot designed for communication between workers and robots while maximizing productivity. It delivers a blend of productivity and safety for both simple tasks and multi-step processes like assembly and inspection work. The 6-axis unit operates at speeds up to 2,500 mm per sec when no workers are near and slows or stops when people approach. Two models available: PRO 900 (max payload 6 kg) and PRO 1300 (max payload 12 kg). Many more functions and features.
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Powerful, pull-type clapper solenoids handle myriad jobs

New powerful, low-profile, pull-type clapper solenoids are available from Magnetic Sensor Systems (MSS). Applications include valve control, locks, starters, ventilators, clamping, sorting, appliances, tools, HVAC, brakes, clutches, switches, mixing, fire suppression systems, door controls, detent latches, and more. The S-16-264 Series of 17 Pull-Type Clapper Solenoids have ampere turns (windings) adjusted to meet the specific force and duty cycle requirements of your application. They provide up to 130 lb (578 N) of force.
Get all the specs for these solenoids and other options.

Tech Tip: Belt, screw, or chain-driven actuator?

Bishop-Wisecarver provides a quick, very useful guide to help you evaluate the right drive strategy for your system: belt, screw, or chain-driven actuator. Each drive type has unique advantages and limitations, so evaluating all your options will help you find the most suitable actuator setup for your specific application needs.
Read the Bishop-Wisecarver blog.

Ultra-precise linear stage -- down to 0.005 microns

PI, a global leader in precision motion control and nanoposi-tioning, now offers fast delivery of the L-511 linear micropositioning stage, which is designed for applications requiring minimum incremental motion down to 20 nm, drive forces up to 22 lb, and multi-axis configuration options. The L-511 can be combined to form XY or XYZ motion systems and integrated with rotary stages. A variety of drive and encoder options (stepper and servo motors, rotary, and linear encoders) enable ultra-fine sensitivity. Applications include: metrology, laser processing, semiconductors, biotech, optical alignment, and advanced automation.
Learn more and get all the specs.

Choosing the right stepper motor: PM or hybrid?

According to the experts at Lin Engineering, there are two primary types of stepper motors to consider: permanent magnet (PM) and hybrid. But which is right for your application? Both types have their advantages and disadvantages, and the choice ultimately depends on your specific requirements.
Read this informative Lin Engineering article.

New PTFE-free linear guide for precise positioning

The new drylin WWP linear guide from igus features a PTFE-free locking carriage. Engineered from lubrication-free, high-performance polymers and aluminum, the guide offers a lightweight, hygienic, and low-maintenance alternative to complex mechanical and electronic adjustment systems. It is significantly more compact and lightweight than conventional recirculating ball-bearing systems. Applications include interior components in vehicles, aircraft, and furniture.
Learn more and get all the specs.

Heavy-duty gear units for mixing and agitating systems

MAXXDRIVE industrial gear units from NORD DRIVE-SYSTEMS are an established drive solution for heavy-duty applications. In addition to conveying, lifting, and driving, they also play an important role in mixing and agitating systems. MAXXDRIVE units feature a compact, one-piece UNICASE housing that delivers long service life, easy maintenance, and quiet operation. Their robust design handles high axial and radial loads, achieves output torques up to 2,495,900 lb-in., and powers up to 8,075 hp.
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What are non-captive linear actuators?

According to PBC Linear, their new non-captive linear actuators are different from the more common external versions of lead screw-driven linear actuators because they allow the lead screw to completely pass through the motor. This fundamental difference offers advantages for designs that have limited space available or for engineers looking to shrink the overall size of their design package.
Read the full PBC Linear blog.

Güdel introduces Swiss-quality tracks for cobots

Güdel Inc. is highlighting new technologies at Automate 2025 booth #2418 that demonstrate its unmatched ability to solve automation engineering challenges. One is the Cobomover, a 7th-axis linear track purpose-built for collaborative and lightweight robots. Designed and manufactured in Switzerland, this unit extends the working range of robots up to 5 m, allowing them to operate multiple workstations and perform a variety of tasks without manual repositioning. Compatible with over 60 cobots and small traditional robots.
Learn more and get all the specs.

Researchers successfully train a machine learning model in outer space for first time

For the first time, a project led by the University of Oxford has trained a machine learning model in outer space, on board a satellite. This achievement could revolutionize the capabilities of remote-sensing satellites by enabling real-time monitoring and decision-making for a range of applications.

Data collected by remote-sensing satellites is fundamental for many key activities, including aerial mapping, weather prediction, and monitoring deforestation. Currently, most satellites can only passively collect data, since they are not equipped to make decisions or detect changes. Instead, data has to be relayed to Earth to be processed, which typically takes several hours or even days. This limits the ability to identify and respond to rapidly emerging events, such as a natural disaster.

To overcome these restrictions, a group of researchers led by DPhil student Vit Ruzicka (Department of Computer Science, University of Oxford) took on the challenge of training the first machine learning program in outer space. During 2022, the team successfully pitched their idea to the Dashing through the Stars mission, which had issued an open call for project proposals to be carried out on board the ION SCV004 satellite, launched in January 2022. During the autumn of 2022, the team uplinked the code for the program to the satellite already in orbit.

The researchers trained a simple model to detect changes in cloud cover from aerial images directly on board the satellite, in contrast to training on the ground. The model was based on an approach called few-shot learning, which enables a model to learn the most important features to look for when it has only a few samples to train from. A key advantage is that the data can be compressed into smaller representations, making the model faster and more efficient.

Illustration of the data used for training the tiny cloud classification model (left), and the predictions on new scenes (right). The entire training process took about 1.5 sec, including the time for encoding the entire training dataset, and 10 epochs of training a classification model. [Image credit: Sentinel-2 data (ESA) processed by Vit Ruzicka]

 

 

 

 

Ruzicka explained, "The model we developed, called RaVAEn, first compresses the large image files into vectors of 128 numbers. During the training phase, the model learns to keep only the informative values in this vector; the ones that relate to the change it is trying to detect (in this case, whether there is a cloud present or not). This results in extremely fast training due to having only a very small classification model to train."

While the first part of the model, to compress the newly seen images, was trained on the ground, the second part (which decided whether the image contained clouds or not) was trained directly on the satellite.

Normally, developing a machine learning model would require several rounds of training, using the power of a cluster of linked computers. In contrast, the team's tiny model completed the training phase (using over 1,300 images) in around 1.5 sec.

When the team tested the model's performance on novel data, it automatically detected whether a cloud was present or not in around a tenth of a second. This involved encoding and analyzing a scene equivalent to an area of about 4.8 x 4.8 km² (equivalent to almost 450 football [soccer] pitches).

According to the researchers, the model could easily be adapted to carry out different tasks and to use other forms of data. Ruzicka added, "Having achieved this demonstration, we now intend to develop more advanced models that can automatically differentiate between changes of interest (for instance flooding, fires, and deforestation) and natural changes (such as natural changes in leaf color across the seasons). Another aim is to develop models for more complex data, including images from hyperspectral satellites. This could allow, for instance, the detection of methane leaks, and would have key implications for combatting climate change."

Performing machine learning in outer space could also help overcome the problem of on-board satellite sensors being affected by the harsh environmental conditions, so that they require regular calibration. "Our proposed system could be used in constellations of non-homogeneous satellites, where reliable information from one satellite can be applied to train the rest of the constellation," Ruzicka said. "This could be used, for instance, to recalibrate sensors that have degraded over time or experienced rapid changes in the environment."

Professor Andrew Markham, who supervised Ruzicka's DPhil research, said, "Machine learning has a huge potential for improving remote sensing -- the ability to push as much intelligence as possible into satellites will make space-based sensing increasingly autonomous. This would help to overcome the issues with the inherent delays between acquisition and action by allowing the satellite to learn from data on board. Vit's work serves as an interesting proof-of-principle."

This project was conducted in collaboration with the European Space Agency (ESA) Φ-lab via the Cognitive Cloud Computing in Space (3CS) campaign and the Trillium Technologies initiative Networked Intelligence in Space (NIO.space) and partners at D-Orbit and Unibap.

The work was presented at the International Geoscience and Remote Sensing Symposium (IGARSS) conference on July 21, 2023.

Source: University of Oxford

Published September 2023

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