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hyperMILL 2024 CAD/CAM software suite

OPEN MIND Technologies has introduced its latest hyperMILL 2024 CAD/CAM software suite, which includes a range of powerful enhancements to its core toolpath capabilities, as well as new functionality for increased NC programming efficiency in applications ranging from 2.5D machining to 5-axis milling. New and enhanced capabilities include: Optimized Deep Hole Drilling, a new algorithm for 3- and 5-axis Rest Machining, an enhanced path layout for the 3D Plane Machining cycle, better error detection, and much more.
Learn more.

One-part epoxy changes from red to clear under UV

Master Bond UV15RCL is a low-viscosity, cationic-type UV-curing system with a special color-changing feature. The red material changes to clear once exposed to UV light, indicating that there is UV light access across the adhesive material. Although this change in color from red to clear does not indicate a full cure, it does confirm that the UV light has reached the polymer. This epoxy is an excellent electrical insulator. UV15RCL adheres well to metals, glass, ceramics, and many plastics, including acrylics and polycarbonates.
Learn more.

SPIROL Press-N-Lok™ Pin for plastic housings

The Press-N-Lok™ Pin was designed to permanently retain two plastic components to each other. As the pin is inserted, the plastic backfills into the area around the two opposing barbs, resulting in maximum retention. Assembly time is quicker, and it requires lower assembly equipment costs compared to screws and adhesives -- just Press-N-Lok™!
Learn more about the new Press-N-Lok™ Pin.

Why hybrid bearings are becoming the new industry standard

A combination of steel outer and inner rings with ceramic balls or rollers is giving hybrid bearings unique properties, making them suitable for use in a wide range of modern applications. SKF hybrid bearings make use of silicon nitride (twice as hard as bearing steel) rolling elements and are available as ball bearings, cylindrical roller bearings, and in custom designs. From electric erosion prevention to friction reduction and extended maintenance intervals, learn all about next-gen hybrid bearings.
Read the SKF technical article.

3M and Ansys train engineers on simulating adhesives

Ansys and 3M have created an advanced simulation training program enabling engineers to enhance the design and sustainability of their products when using tapes and adhesives as part of the design. Simulation enables engineers to validate engineering decisions when analyzing advanced polymeric materials -- especially when bonding components made of different materials. Understand the behavior of adhesives under real-world conditions for accurate modeling and design.
Read this informative Ansys blog.

New FATH T-slotted rail components in black from AutomationDirect

Automation-Direct has added a wide assortment of black-colored FATH T-slotted hardware components to match their SureFrame black anodized T-slotted rails, including: cube connectors (2D and 3D) and angle connectors, joining plates of many types, brackets, and pivot joints. Also included are foot consoles, linear bearings in silver and black, cam lever brakes, and L-handle brakes. FATH T-slotted hardware components are easy to install, allow for numerous T-slotted structure configurations, and have a 1-year warranty against defects.
Learn more.

Weird stuff: Moon dust simulant for 3D printing

Crafted from a lunar regolith simulant, Basalt Moon Dust Filamet™ (not a typo) available from The Virtual Foundry closely mirrors the makeup of lunar regolith found in mare regions of the Moon. It enables users with standard fused filament fabrication (FFF) 3D printers to print with unparalleled realism. Try out your ideas before you go for that big space contract, or help your kid get an A on that special science project.
Learn more.

Break the mold with custom injection molding by Rogan

With 90 years of industry experience, Rogan Corporation possesses the expertise to deliver custom injection molding solutions that set businesses apart. As a low-cost, high-volume solution, injection molding is the most widely used plastics manufacturing process. Rogan processes include single-shot, two-shot, overmolding, and assembly. Elevate your parts with secondary operations: drilling and tapping, hot stamping, special finishes, punch press, gluing, painting, and more.
Learn more.

World's first current-carrying fastening technology

PEM^® eConnect™ current-carrying pins from Penn-Engineering provide superior electrical connections in applications that demand high performance from internal components, such as automotive electronics. This first-to-market tech provides repeatable, consistent electrical joints and superior installation unmatched by traditional fastening methods. Features include quick and secure automated installation, no hot spots or poor conductivity, and captivation options that include self-clinching and broaching styles.
Learn more about eConnect pins.

New interactive digital catalog from EXAIR

EXAIR's latest catalog offers readers an incredible source of innovative solutions for common industrial problems like conveying, cooling, cleaning, blowoff, drying, coating, and static buildup. This fully digital and interactive version of Catalog 35 is designed for easy browsing and added accessibility. Customers can view, download, print, and save either the full catalog or specific pages and sections. EXAIR products are designed to conserve compressed air and increase personnel safety in the process. Loaded with useful information.
Check out EXAIR's online catalog.

5 cost-saving design tips for CNC machining

Make sure your parts meet expectations the first time around. Xometry's director of application engineering, Greg Paulsen, presents five expert tips for cutting costs when designing custom CNC machined parts. This video covers corners and radii, designing for deep pockets, thread depths, thin walls, and more. Always excellent info from Paulsen at Xometry.
View the video.

What can you secure with a retaining ring? 20 examples

From the watch dial on your wrist to a wind turbine, no application is too small or too big for a Smalley retaining ring to secure. Light to heavy-duty loads? Carbon steel to exotic materials? No problem. See how retaining rings are used in slip clutches, bike locks, hip replacements, and even the Louvre Pyramid.
See the Smalley design applications.

Load fasteners with integrated RFID

A crane, rope, or chain may be required when something needs lifting -- plus anchoring points on the load. JW Winco offers a wide range of solutions to fasten the load securely, including: lifting eye bolts and rings (with or without rotation), eye rings with ball bearings, threaded lifting pins, shackles, lifting points for welding, and more. Some, such as the GN 581 Safety Swivel Lifting Eye Bolts, even have integrated RFID tags to clearly identify specific lifting points during wear and safety inspections and manage them digitally and without system interruption.
Learn more.

Couplings solve misalignments more precisely with targeted center designs

ALS Couplings from Miki Pulley feature a simplistic, three-piece construction and are available in three different types for more precisely handling parallel, angular, or axial misalignment applications. The key feature of this coupling design is its center element. Each of the three models has a center member that has a unique and durable material and shape. Also called a "spider," the center is designed to address and resolve the type of misalignment targeted. Ideal for unidirectional continuous movement or rapid bidirectional motion.
Learn more.

What is 3D-MID? Molded parts with integrated electronics from HARTING

3D-MID (three-dimensional mechatronic integrated devices) technology combines electronic and mechanical functionalities into a single, 3D component. It replaces the traditional printed circuit board and opens up many new opportunities. It takes injection-molded parts and uses laser-direct structuring to etch areas of conductor structures, which are filled with a copper plating process to create very precise electronic circuits. HARTING, the technology's developer, says it's "Like a PCB, but 3D." Tons of possibilities.
View the video.

Artificial intelligence system finds 'recipes' for producing new and novel materials by poring through millions of research papers

A new artificial-intelligence system aims to pore through research papers to deduce "recipes" for producing particular materials. [Image: Chelsea Turner/MIT]

 

 

By Larry Hardesty, MIT

In recent years, research efforts such as the Materials Genome Initiative and the Materials Project have produced a wealth of computational tools for designing new materials useful for a range of applications, from energy and electronics to aeronautics and civil engineering.

But developing processes for producing those materials has continued to depend on a combination of experience, intuition, and manual literature reviews.

A team of researchers at MIT, the University of Massachusetts at Amherst, and the University of California at Berkeley hopes to close that materials-science automation gap with a new artificial-intelligence system that would pore through research papers to deduce "recipes" for producing particular materials.

"Computational materials scientists have made a lot of progress in the ‘what' to make -- what material to design based on desired properties," says Elsa Olivetti, the Atlantic Richfield Assistant Professor of Energy Studies in MIT's Department of Materials Science and Engineering (DMSE). "But because of that success, the bottleneck has shifted to, ‘Okay, now how do I make it?'"

The researchers envision a database that contains materials recipes extracted from millions of papers. Scientists and engineers could enter the name of a target material and any other criteria -- precursor materials, reaction conditions, fabrication processes -- and pull up suggested recipes.

As a step toward realizing that vision, Olivetti and her colleagues have developed a machine-learning system that can analyze a research paper, deduce which of its paragraphs contain materials recipes, and classify the words in those paragraphs according to their roles within the recipes: names of target materials, numeric quantities, names of pieces of equipment, operating conditions, descriptive adjectives, and the like.

In a paper appearing in the latest issue of the journal Chemistry of Materials, they also demonstrate that a machine-learning system can analyze the extracted data to infer general characteristics of classes of materials -- such as the different temperature ranges that their synthesis requires -- or particular characteristics of individual materials -- such as the different physical forms they will take when their fabrication conditions vary.

Olivetti is the senior author on the paper, and she's joined by Edward Kim, an MIT graduate student in DMSE; Kevin Huang, a DMSE postdoc; Adam Saunders and Andrew McCallum, computer scientists at UMass Amherst; and Gerbrand Ceder, a Chancellor's Professor in the Department of Materials Science and Engineering at Berkeley.

Filling in the gaps
The researchers trained their system using a combination of supervised and unsupervised machine-learning techniques. "Supervised" means that the training data fed to the system is first annotated by humans; the system tries to find correlations between the raw data and the annotations. "Unsupervised" means that the training data is unannotated, and the system instead learns to cluster data together according to structural similarities.

Because materials-recipe extraction is a new area of research, Olivetti and her colleagues didn't have the luxury of large, annotated data sets accumulated over years by diverse teams of researchers. Instead, they had to annotate their data themselves -- ultimately, about 100 papers.

By machine-learning standards, that's a pretty small data set. To improve it, they used an algorithm developed at Google called Word2vec. Word2vec looks at the contexts in which words occur -- the words' syntactic roles within sentences and the other words around them -- and groups together words that tend to have similar contexts. So, for instance, if one paper contained the sentence "We heated the titanium tetracholoride to 500 C," and another contained the sentence "The sodium hydroxide was heated to 500 C," Word2vec would group "titanium tetracholoride" and "sodium hydroxide" together.

With Word2vec, the researchers were able to greatly expand their training set, since the machine-learning system could infer that a label attached to any given word was likely to apply to other words clustered with it. Instead of 100 papers, the researchers could thus train their system on around 640,000 papers.

Tip of the iceberg
To test the system's accuracy, however, they had to rely on the labeled data, since they had no criterion for evaluating its performance on the unlabeled data. In those tests, the system was able to identify with 99 percent accuracy the paragraphs that contained recipes and to label with 86 percent accuracy the words within those paragraphs.

The researchers hope that further work will improve the system's accuracy, and in ongoing work they are exploring a battery of deep learning techniques that can make further generalizations about the structure of materials recipes, with the goal of automatically devising recipes for materials not considered in the existing literature.

Much of Olivetti's prior research has concentrated on finding more cost-effective and environmentally responsible ways to produce useful materials, and she hopes that a database of materials recipes could abet that project.

"This is landmark work," says Ram Seshadri, the Fred and Linda R. Wudl Professor of Materials Science at the University of California at Santa Barbara. "The authors have taken on the difficult and ambitious challenge of capturing, through AI methods, strategies employed for the preparation of new materials. The work demonstrates the power of machine learning, but it would be accurate to say that the eventual judge of success or failure would require convincing practitioners that the utility of such methods can enable them to abandon their more instinctual approaches."

This research was supported by the National Science Foundation, Office of Naval Research, the Department of Energy, and seed support through the MIT Energy Initiative. Kim was partially supported by Natural Sciences and Engineering Research Council of Canada.

Published December 2017

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