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Overhung load adaptors provide load support and contamination protection

Overhung load adaptors (OHLA) provide both overhung radial and axial load support to protect electrified mobile equipment motors from heavy application loads, extending the lifetime of the motor and alleviating the cost of downtime both from maintenance costs and loss of production. They seal out dirt, grime, and other contaminants too. Zero-Max OHLAs are available in an extensive offering of standard models (including Extra-Duty options) for typical applications or customized designs.
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Why choose electric for linear actuators?

Tolomatic has been delivering a new type of linear motion technology that is giving hydraulics a run for its money. Learn the benefits of electric linear motion systems, the iceberg principle showing total cost of ownership, critical parameters of sizing, and conversion tips.
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New AC hypoid inverter-duty gearmotors

Bodine Electric Company introduces 12 new AC inverter-duty hypoid hollow shaft gearmotors. These type 42R-25H2 and 42R-30H3 drives combine an all-new AC inverter-duty, 230/460-VAC motor with two hypoid gearheads. When used with an AC inverter (VFD) control, these units deliver maintenance-free and reliable high-torque output. They are ideal for conveyors, gates, packaging, and other industrial automation equipment that demands both high torque and low power consumption from the driving gearmotor.
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Next-gen warehouse automation: Siemens, Universal Robots, and Zivid partner up

Universal Robots, Siemens, and Zivid have created a new solution combining UR's cobot arms with Siemens' SIMATIC Robot Pick AI software and Zivid's 3D sensors to create a deep-learning picking solution for warehouse automation and intra-logistics fulfillment. It works regardless of object shape, size, opacity, or transparency and is a significant leap in solving the complex challenges faced by the logistics and e-commerce sectors.
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Innovative DuoDrive gear and motor unit is UL/CSA certified

The DuoDrive integrated gear unit and motor from NORD DRIVE-SYSTEMS is a compact, high-efficiency solution engineered for users in the fields of intralogistics, pharmaceutical, and the food and beverage industries. This drive combines a IE5+ synchronous motor and single-stage helical gear unit into one compact housing with a smooth, easy-to-clean surface. It has a system efficiency up to 92% and is available in two case sizes with a power range of 0.5 to 4.0 hp.
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BLDC flat motor with high output torque and speed reduction

Portescap's 60ECF brushless DC slotted flat motor is the newest frame size to join its flat motor portfolio. This 60-mm BLDC motor features a 38.2-mm body length and an outer-rotor slotted configuration with an open-body design, allowing it to deliver improved heat management in a compact package. Combined with Portescap gearheads, it delivers extremely high output torque and speed reduction. Available in both sensored and sensorless options. A great choice for applications such as electric grippers and exoskeletons, eVTOLs, and surgical robots.
Learn more and view all the specs.

Application story: Complete gearbox and coupling assembly for actuator system

Learn how GAM engineers not only sized and selected the appropriate gear reducers and couplings required to drive two ball screws in unison using a single motor, but how they also designed the mounting adapters necessary to complete the system. One-stop shopping eliminated unnecessary components and resulted in a 15% reduction in system cost.
Read this informative GAM blog.

Next-gen motor for pump and fan applications

The next evolution of the award-winning Aircore EC motor from Infinitum is a high-efficiency system designed to power commercial and industrial applications such as HVAC fans, pumps, and data centers with less energy consumption, reduced emissions, and reduced waste. It features an integrated variable frequency drive and delivers upward of 93% system efficiency, as well as class-leading power and torque density in a low-footprint package that is 20% lighter than the previous version. Four sizes available.
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Telescoping linear actuators for space-constrained applications

Rollon's new TLS telescoping linear actuators enable long stroke lengths with minimal closed lengths, which is especially good for applications with minimal vertical clearance. These actuators integrate seamlessly into multi-axis systems and are available in two- or three-stage versions. Equipped with a built-in automated lubrication system, the TLS Series features a synchronized drive system, requiring only a single motor to achieve motion. Four sizes (100, 230, 280, and 360) with up to 3,000-mm stroke length.
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Competitively priced long-stroke parallel gripper

The DHPL from Festo is a new generation of pneumatic long-stroke grippers that offers a host of advantages for high-load and high-torque applications. It is interchangeable with competitive long-stroke grippers and provides the added benefits of lighter weight, higher precision, and no maintenance. It is ideal for gripping larger items, including stacking boxes, gripping shaped parts, and keeping bags open. It has high repetition accuracy due to three rugged guide rods and a rack-and-pinion design.
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Extend your range of motion: Controllers for mini motors

FAULHABER has added another extremely compact Motion Controller without housing to its product range. The new MC3603 controller is ideal for integration in equipment manufacturing and medical tech applications. With 36 V and 3 A (peak current 9 A), it covers the power range up to 100 W and is suitable for DC motors with encoder, brushless drives, or linear motors.
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When is a frameless brushless DC motor the right choice?

Frameless BLDC motors fit easily into small, compact machines that require high precision, high torque, and high efficiency, such as robotic applications where a mix of low weight and inertia is critical. Learn from the experts at SDP/SI how these motors can replace heavier, less efficient hydraulic components by decreasing operating and maintenance costs. These motors are also more environmentally friendly than others.
View the video.

Tiny and smart: Step motor with closed-loop control

Nanotec's new PD1-C step motor features an integrated controller and absolute encoder with closed-loop control. With a flange size of merely 28 mm (NEMA 11), this compact motor reaches a max holding torque of 18 Ncm and a peak current of 3 A. Three motor versions are available: IP20 protection, IP65 protection, and a motor with open housing that can be modified with custom connectors. Ideal for applications with space constraints, effectively reducing both wiring complexity and installation costs.
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Closed loop steppers drive new motion control applications

According to the motion experts at Performance Motion Devices, when it comes to step motors, the drive technique called closed loop stepper is making everything old new again and driving a burst of interest in the use of two-phase step motors. It's "winning back machine designers who may have relegated step motors to the category of low cost but low performance."
Read this informative Performance Motion Devices article.

Intelligent compact drives with extended fieldbus options

The intelligent PD6 compact drives from Nanotec are now available with Profinet and EtherNet/IP. They combine motor, controller, and encoder in a space-saving package. With its 80-mm flange and a rated power of 942 W, the PD6-EB is the most powerful brushless DC motor of this product family. The stepper motor version has an 86-mm flange (NEMA 34) and a holding torque up to 10 Nm. Features include acceleration feed forward and jerk-limited ramps. Reduced installation time and wiring make the PD6 series a highly profitable choice for machine tools, packaging machines, or conveyor belts.
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New test reveals artificial intelligence still lacks common sense

Despite advances in natural language processing, AI still doesn't have the common sense to understand human language, finds a new USC study.

By Caitlin Dawson, University of Southern California Viterbi School of Engineering

Natural language processing (NLP) has taken great strides recently, but how much does artificial intelligence (AI) understand of what it reads? Less than we thought, according to researchers at USC's Department of Computer Science. In a recent paper, Assistant Professor Xiang Ren and PhD student Yuchen Lin found that despite advances, AI still doesn't have the common sense needed to generate plausible sentences.

"Current machine text-generation models can write an article that may be convincing to many humans, but they're basically mimicking what they have seen in the training phase," said Lin. "Our goal in this paper is to study the problem of whether current state-of-the-art text-generation models can write sentences to describe natural scenarios in our everyday lives."

Understanding scenarios in daily life
Specifically, Ren and Lin tested the models' ability to reason and showed there is a large gap between current text-generation models and human performance. Given a set of common nouns and verbs, state-of-the-art NLP computer models were tasked with creating believable sentences describing an everyday scenario. While the models generated grammatically correct sentences, they were often logically incoherent.

For instance, here's one example sentence generated by a state-of-the-art model using the words dog, frisbee, throw, catch: Two dogs are throwing frisbees at each other.

The test is based on the assumption that coherent ideas (in this case, a person throws a frisbee, and a dog catches it) can't be generated without a deeper awareness of common-sense concepts. In other words, common sense is more than just the correct understanding of language -- it means you don't have to explain everything in a conversation. This is a fundamental challenge in the goal of developing generalizable AI. Beyond academia, it's relevant for consumers too.

Without an understanding of language, chatbots and voice assistants built on these state-of-the-art natural language models are vulnerable to failure. It's also crucial if robots are to become more present in human environments. After all, if you ask a robot for hot milk, you expect it to know you want a cup of milk, not the whole carton.

"We also show that if a generation model performs better on our test, it can also benefit other applications that need common-sense reasoning, such as robotic learning," said Lin. "Robots need to understand natural scenarios in our daily life before they make reasonable actions to interact with people."

Joining Lin and Ren on the paper are USC's Wangchunshu Zhou, Ming Shen, Pei Zhou; Chandra Bhagavatula from the Allen Institute of Artificial Intelligence; and Yejin Choi from the Allen Institute of Artificial Intelligence and Paul G. Allen School of Computer Science & Engineering, University of Washington.

The common-sense test
Common-sense reasoning, or the ability to make inferences using basic knowledge about the world -- like the fact that dogs cannot throw frisbees to each other -- has resisted AI researchers' efforts for decades. State-of-the-art deep-learning models can now reach around 90% accuracy, so it would seem that NLP has gotten closer to its goal.

But Ren, an expert in natural language processing, and Lin, his student, needed more convincing about this statistic's accuracy. In their paper, published in the Findings of Empirical Methods in Natural Language Processing (EMNLP) conference on Nov. 16, 2020, they challenge the effectiveness of the benchmark and, therefore, the level of progress the field has actually made.

"Humans acquire the ability to compose sentences by learning to understand and use common concepts that they recognize in their surrounding environment," said Lin. "Acquiring this ability is regarded as a major milestone in human development. But we wanted to test if machines can really acquire such generative common-sense reasoning ability."

Examples of sentences generated by state-of-the-art text-generation models from the paper "CommonGen: A Constrained Text Generation Challenge for Generative Common Sense reasoning."

 

 

 

 

To evaluate different machine models, the pair developed a constrained text-generation task called CommonGen, which can be used as a benchmark to test the generative common sense of machines. The researchers presented a dataset consisting of 35,141 concepts associated with 77,449 sentences. They found that even the best-performing model only achieved an accuracy rate of 31.6% versus 63.5% for humans.

"We were surprised that the models cannot recall the simple common-sense knowledge that a human throwing a frisbee should be much more reasonable than a dog doing it," said Lin. "We find even the strongest model, called the T5, after training with a large dataset, can still make silly mistakes."

It seems, said the researchers, that previous tests have not sufficiently challenged the models on their common-sense abilities, instead mimicking what they have seen in the training phase.

"Previous studies have primarily focused on discriminative common sense," said Ren. "They test machines with multi-choice questions, where the search space for the machine is small -- usually four or five candidates."

For instance, a typical setting for discriminative common-sense testing is a multiple-choice question-answering task, for example: "Where do adults use glue sticks?" A: classroom B: office C: desk drawer.

The answer here is "B: office." Even computers can figure this out without much trouble. In contrast, a generative setting is more open-ended, such as the CommonGen task, where a model is asked to generate a natural sentence from given concepts.

Ren explains: "With extensive model training, it is very easy to have a good performance on those tasks. Unlike those discriminative common-sense reasoning tasks, our proposed test focuses on the generative aspect of machine common sense."

Ren and Lin hope the data set will serve as a new benchmark to benefit future research about introducing common sense to natural language generation. In fact, they even have a leaderboard depicting scores achieved by the various popular models to help other researchers determine their viability for future projects.

"Robots need to understand natural scenarios in our daily life before they make reasonable actions to interact with people," said Lin.

"By introducing common sense and other domain-specific knowledge to machines, I believe that one day we can see AI agents such as Samantha in the movie 'Her' that generate natural responses and interact with our lives."

Published November 2020

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