Elizabeth Benham’s picture

By: Elizabeth Benham

Each year during national Weights and Measures Week (March 1 to 7), we celebrate the contributions made by the weights and measures community to ensure accuracy and fair competition in commercial transactions based on weight or measure. This year’s theme, “Measuring Up to the New Normal,” was especially meaningful because 2020 will be remembered as one of the most unusual years we’ll likely experience in our lifetimes. The year highlighted how a common challenge can positively transform how we do business.


Weights and Measures Week commemorates the signing of the first U.S. weights and measures law by President John Adams in 1799. Marble bust by artist Daniel Chester French.

This year, the National Institute of Standards and Technology (NIST) recognized the contributions made by the weights and measures community to sustain equity in the marketplace. Equity in the marketplace takes the effort of many people and institutions.

Silke von Gemmingen’s picture

By: Silke von Gemmingen

The global pandemic has radically impacted the supply chain and logistics industry, making the need for robotic automation more urgent than ever. With more than 70 percent of labor in warehousing now dedicated to picking and packing, numerous companies are gradually investing in logistics automation. But what happens when robots must handle an unlimited number of (unknown) stock-keeping units (SKUs)? These companies need a fast, reliable, and robust way to automate picking and placing a large variety of objects.

This challenge was taken up successfully by the Dutch company Fizyr. The computer vision company based in Delft focuses on enabling robots to pick unknown objects even in harsh logistics environments. The result is an automated vision solution that enables logistic automation in various conditions and applications, like item picking, parcel handling, depalletizing, truck unloading, or baggage handling. To complete the system with the optimal hardware, Fizyr integrates compact, robust Ensenso 3D cameras in combination with high-performance GigE uEye cameras from IDS.

Catherine Cooksey’s picture

By: Catherine Cooksey

New employees at the National Institute of Standards and Technology (NIST) are often surprised to learn that our agency is part of the U.S. Department of Commerce. How could this be? On the surface it seems that the missions of the two organizations couldn’t be more different. The Department of Commerce would appear to be concerned with, well, commerce, while NIST is well known for its Nobel Prize-winning scientific and technological work.

But the connection can be explained through our agency’s mission statement: “To promote U.S. innovation and industrial competitiveness by advancing measurement science, standards, and technology in ways that enhance economic security and improve our quality of life.”

MIT News’s picture

By: MIT News

Buildings account for about 40 percent of U.S. energy consumption, and are responsible for one-third of global carbon dioxide emissions. Making buildings more energy-efficient is not only a cost-saving measure, but also a crucial climate-change mitigation strategy. Hence the rise of “smart” buildings, which are increasingly becoming the norm around the world.

Smart buildings automate systems like heating, ventilation, and air conditioning (HVAC), lighting, electricity, and security. Automation requires sensory data, such as indoor and outdoor temperature and humidity, carbon dioxide concentration, and occupancy status. Smart buildings leverage data in a combination of technologies that can make them more energy-efficient.

Since HVAC systems account for nearly half of a building’s energy use, smart buildings use smart thermostats, which automate HVAC controls and can learn the temperature preferences of a building’s occupants.

Loretta Marie Perera’s picture

By: Loretta Marie Perera

Recently, the team at 4C Creative Cad CAM Consultants in Emmen, Netherlands, was given a unique task: How to get a vintage Harley Davidson motorcycle back on the road again.

What was fun about this project wasn’t how challenging it was, or how much expertise it required. The joy was in the end itself, to provide a straightforward solution to a question that had been on the mind of one man for decades: How to get his vintage motorcycle capable of starting and riding on the streets. The solution was to scan a broken part that could no longer be found and 3D print a replacement.

The problem was brought to Carl van de Rijzen of Visual First in the Netherlands, who has been working with Edwin Rappard of 4C Creative CAD CAM Consultants for more than two years. Living on opposite ends of the country, the two have never met in person. “I send something to Edwin, he scans it and sends it back,” says van de Rijzen. The same thing occurred in this case.

NVision Inc.’s picture

By: NVision Inc.

NVision’s engineering services are helping managers of coal-fired power plants converting to natural gas to determine more quickly where to install updated instrumentation necessary to retrofit turbines to accommodate the new power source.

“By measuring the equipment via laser scanning, then creating precise 3D models of the turbine assemblies for engineers to analyze for optimal installation points, we can significantly expedite the plants’ transitions,” says Steve Kersen, president of NVision. “This can result in huge cost savings for projects that would otherwise have been budgeted for a lengthier period using less sophisticated measurement methods. In one recent project, a Southeast power plant converting to a combined-cycle gas turbine (CCGT*) system will increase wattage output by more than 30 percent and save more than $250,000 by using our services.”

Douglas Allen’s picture

By: Douglas Allen

Any number derived from real observation is made up of three components. The first of these is the intended signal, the “perfect” value from the object being observed. The second is error (or noise) caused by environmental disturbance and/or interference. The third is bias, a regular and consistent deviation from the perfect value.

O = S + N + B, or observation equals signal plus noise plus bias

The signal usually is predictably constant, as is the bias. Identifying and eliminating bias requires a set of techniques beyond the scope of this article, so for the remainder of this, we will consider both as components of the signal, leaving a somewhat simpler equation for our observation.

O = S + N, or observation equals signal plus noise

This article focuses on removing the random noise component from the observation and leaving the signal component. The noise is in the form of chance variation, which sometimes enhances the signal and sometimes detracts from it. If we could separate the noise from the signal and eliminate it, our observation would be pure signal, or a precise and consistent value.

Lawrence Livermore National Laboratory’s picture

By: Lawrence Livermore National Laboratory

A team of Lawrence Livermore National Laboratory (LLNL) scientists has simulated the droplet-ejection process in an emerging metal 3D-printing technique called “liquid metal jetting” (LMJ), a critical aspect to the continued advancement of liquid metal printing technologies.

In their paper, which was published in the journal Physics of Fluids, the team describes the simulating of metal droplets during LMJ, a novel process in which molten droplets of liquid metal are jetted from a nozzle to 3D-print a part in layers. The process does not require lasers or metal powder and is more similar to inkjet printing techniques.

Using the model, researchers studied the primary breakup dynamics of the metal droplets, essential to improving the understanding of LMJ. LMJ has advantages over powder-based approaches in that it provides a wider material set and does not require production or handling of potentially hazardous powders, researchers said.

Loretta Marie Perera’s picture

By: Loretta Marie Perera

A steam train not seen since the 1960s is being rebuilt by a group of engineering enthusiasts, assisted by the metrology experts at the University of Sheffield Advanced Manufacturing Research Centre (AMRC). With a little extra help from Hexagon’s advanced industrial laser tracker technology, the team got the measure of a mysterious discrepancy between the original drawings and the actual locomotive.

The Standard Steam Locomotive Co. group has set itself the ambitious challenge to recreate, operate, and maintain a lost class of British steam train—a British Railways’ Standard Class 6 “Clan”—using a combination of the original 1950s design drawings and 21st-century engineering. The plan is to incorporate modern design and manufacturing techniques and technologies into the build.

Jérôme-Alexandre Lavoie’s picture

By: Jérôme-Alexandre Lavoie

With the increasing popularity of electric vehicles (EV), a lot of engineers and quality control specialists are facing new challenges when inspecting parts. Whereas traditional cars had primarily mechanical parts, EVs now feature complex electrical-mechanical devices controlled by software. Although they have fewer moving parts than gasoline vehicles, EVs have myriad complicated subsystems—all of which affect the performance and handling of these vehicles.

In order to improve product safety and production throughput, more EV manufacturers are turning to automated quality control systems in plants and right on their production floors. Anomalies can be instantaneously reported back to the engineering staff for quick corrective measures. Speeding up inspections leads to more throughput and a faster time to market.

Inefficient quality control, lack of skilled labor slow throughput

In today’s tough labor market, there is a clear lack of skilled labor with the experience and expertise required to perform effective quality control inspections.

Syndicate content