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What can you do with touchless magnetic angle sensors?

Novotechnik has put together an informative video highlighting real-world applications for their RFC, RFE, and RSA Series touchless magnetic angle sensors. You may be surprised at the variety of off-highway, marine, material handling, and industrial uses. You'll learn how they work (using a Hall effect microprocessor to detect position) and their key advantages, including eliminated wear and tear on these non-mechanical components. We love when manufacturers provide such useful examples.
View the video.

What can the new Autodesk Inventor AI Assistant do for you?

Autodesk Assistant brings industry-specific context to help execute tasks and orchestrate actions across your 3D models -- not just answer questions. Designed to understand your workflows, Assistant appears as a dockable panel alongside your Inventor workspace and includes the ability to perform complex tasks or gather information from your designs without writing a single line of code. Find out what this new AI "colleague" can do for you.
Watch this informative Autodesk video.

Useful! Snap-together LED enclosure lighting

Seifert StripLite SL 4000 Series LED enclosure lighting provides bright illumination to 700 lumens. On/off switch and motion sensor models are available. Easily daisy chain up to 16 light strips. Magnetic or clip mounting. See video/info on website or contact Bristol Instruments for more information.
Learn about snap-together lighting.

Next-gen multi-touch panels

Beckhoff's Next line of multi-touch control panels and panel PCs is engineered for demanding human-machine interface and control tasks. These panels offer convenient operation with advanced multi-touch technology, a high-quality look and feel, anti-glare and anti-ghosting effects, and a wide choice of formats (from 7 to 23.8 in.) and options. A main draw is the line's attractive pricing.
Learn more.

Most powerful handheld 3D laser scanner on the market

Creaform, a business of AMETEK, has launched HandySCAN 3D|EVO Series, the most powerful handheld 3D laser scanning solution on the market. This innovative series features a built-in touchscreen display and an integrated high-res 12-MP photo camera, incorporating augmented reality (AR) and advanced on-scanner visualization. Users can streamline repetitive inspections and enhance quality control processes using the new auto-alignment feature. Powered by 46 blue laser lines with accuracy of 0.020 mm. The Creaform Metrology Suite includes four application software modules: Scan-to-CAD, Inspection, Automation, and Dynamic Tracking. So many more features.
Learn more.

Continental develops first sensor to measure heat in EV motors

Global automotive supplier Continental has developed a new sensor technology that measures the temperature inside permanently excited synchronous motors in electric vehicles directly on the rotor for the first time.
Read the full article.

LEDs with highest output power available

The new OCI-460 SWIR LED series from EPIGAP OSA Photonics features markedly improved output power compared to the company's previous OCI-480 package and all competitive SMD SWIR LED devices. For example, model OCI-460 ID1550-XS operates at 1,550 nm and features drive current up to 1.5A to deliver approximately 13% higher output efficiency over EPIGAP's OCI-480 package. This impressive advancement features 96% higher output power compared to any other SWIR SMD LED currently on the market. Ideal for use in sensing, machine vision, and more.
Learn more.

AI and collaboration in SOLIDWORKS

Discover AURA, the new AI assistant built into SOLID-WORKS, in this informative video from TriMech Group. What can AURA do for you? It can streamline workflows and make collaborating on and tracking projects even easier, for starters. Other top features of SOLIDWORKS Design 2026 are also covered. Some good tips here.
View the TriMech Group video.

Solutions for weighing and force measurement

Automation-Direct now offers Sensy 2172L series single point, 5510 series shear beam, and 2782 series tension/compression load cells that deliver flexible solutions for weighing and force measurement. They are ideal for applications ranging from small packaging scales to rugged industrial tanks and conveyor systems. Built from aircraft-grade aluminum or stainless steel, these models feature built-in overload protection, accuracies down to 0.03% of full scale, protection ratings up to IP67, and capacities up to 2,000 kg.
Learn more.

Top Product: Future-proof enclosure cooling

Seifert's new SLIMLINE NEO ushers in next-generation industrial cooling with natural refrigerant R290 (GWP 0.02) and high-efficiency inverter technology. It cuts energy costs with EER up to 3.6, reduces refrigerant charge by 75%, and extends electronics life. A fully redesigned, lighter, smaller enclosure delivers lower vibration, better component protection, and easier handling. Available in two elegant surfaces: stainless steel and mild steel, powder coated.
Learn more.

Coin cell supercapacitors: High capacity, quick release

Coin cell supercapa-citors are compact, high-capacity energy storage devices that rapidly charge and discharge and endure far more cycles than rechargeable batteries. They're ideal for high switching loads such as real-time clock and battery back-up power, battery-swap ride-through, and LED or audible alarms. SCHURTER's latest versions support up to 5.5 V and 100 to 1,500 mF.
Learn more.

Tech Tip: Mastering sheet metal bend calculations in Onshape

Mastering bend calculations in sheet metal design is a key skill that can impact the accuracy and manufactur-ability of your designs significantly. Explore the various options available to become a pro in this Onshape Tech Tip: K Factor, bend allowance, and bend deduction, with guidance on when each should be used. You will probably learn something even if you don't use this software.
Read the Onshape blog.

Digital Engineering: How a private jet gets a high-end refurb

Ever wonder how private jets get overhauled from standard OEM layouts to exotic, artful interiors? It takes engineering expertise, specialty design skills, and true craftspeople. Increasingly, it also takes automation provided by middleware to weave a digital thread through CAD, BOM, ERP, and PDM software.
Read the full article.

How AI is quietly transforming simulation

Is AI really useful, or is it just a passing trend? Balavignesh Vemparala, an R&D Engineer II at ANSYS, lays out a compelling case for how artificial intelligence is already hard at work in the simulation world with real results for users. From faster solves to accelerated workflows, improved quality and traceability, generative models, and more, discover what you might be overlooking when it comes to real-world AI application. Worth the read.
Read this informative ANSYS blog.

All about photoelectric sensors

From counting boxes on a conveyor and ensuring precise packaging and labeling to the automatic doors you walk through daily, learn all about photoelectric sensors and the options available from AutomationDirect. Did you know there are four main types? Familiarize yourself with their constructions, capabilities, and their main pros and cons. A good basic primer or refresh.
View the AutomationDirect video.

New framework predicts how materials respond to electric fields -- models a million atoms at a time

Researchers in the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a machine learning framework that can predict with quantum-level accuracy how materials respond to electric fields, up to the scale of a million atoms -- vastly accelerating simulations beyond quantum mechanical methods, which can model only a few hundred atoms at a time.

The work will allow scientists and engineers to carry out highly accurate, large-scale simulations of different materials' responses to many external stimuli, which could have major implications for advanced materials design or energy technologies.

The research was published in Nature Communications and led by Stefano Falletta, a former postdoctoral researcher in the group of senior author Boris Kozinsky, the Gordon McKay Professor of Materials Science and Mechanical Engineering at SEAS, and Professor of Chemistry and Chemical Biology.

For more than 30 years, the primary method for simulating the properties and behaviors of atoms and molecules has been density functional theory, a set of quantum-mechanical equations that is physics-based and highly accurate, but computationally intensive and thus limited to only smaller systems.

In recent years, studying quantum behavior of materials at larger sizes and longer time scales while retaining the accuracy of density functional theory has been bolstered by machine learning, but modeling responses to external stimuli has remained a challenge. This is because existing machine learning methods tend to overlook physical symmetries and conservation laws related to both energetic and electrical properties, leading to inaccuracies in property predictions.

To solve these issues, the authors devised a machine learning method that unifies distinct quantum behaviors like energy and polarization into one generalized potential energy function. Using density functional theory calculations as training and validation data, the model includes the effects of external fields and enforces the correct physics.

The new framework, called Allegro-pol, builds on a previously developed neural network architecture called Allegro that performed accurate simulations of the energy and atomic forces present in a given set of atoms. Falletta looked to extend the capabilities of Allegro to capture not only real-time molecular dynamics, but also what happens when the atoms undergo external perturbations, such as the application of an external electric field. Understanding these interactions is essential for discovering, for example, new ferroelectric materials and dielectric materials that could be used in applications such as non-volatile memory, capacitors, and energy storage devices.

"If you use physics-based methods that solve quantum mechanical equations, you can only go up to a few hundred atoms," said Falletta. "Whereas, with these machine learning methods, you can essentially scale up to hundreds of thousands of atoms or even a million."

They proved out their method by simulating infrared and electrical properties of silicon dioxide, as well as temperature-dependent ferroelectric switching in barium titanate.

Separations of electric poles in tetragonal barium titanate during transition from up to down polarization in the presence of an electric field. [Credit: Image by Stefano Falletta/Courtesy of Harvard John A. Paulson School of Engineering and Applied Sciences]

Falletta, who now works at Radical AI on accelerating materials discovery, said that foundational models based on the one described in Nature Communications could allow machine learning-enabled materials science to take off in ways that are only beginning to be understood.

"The field of computational materials discovery is moving toward better theories, better machine learning models, better infrastructure, bigger clusters, faster GPUs, and better generative models -- it's all kind of coming together," Falletta said. "It's definitely very exciting."

Source: Harvard John A. Paulson School of Engineering and Applied Sciences

Published June 2025

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