Isaac Maw’s picture

By: Isaac Maw

In manufacturing today, data analysis tools can give management the information it needs to make better decisions in areas such as maintenance and labor. Unfortunately, however, many data analytics systems require large sets of historical data to generate accurate and useful results.

According to Rebecca Grollman, a data scientist at Bsquare, anomaly detection is different. These algorithms can begin generating useful information without needing to be trained on historical data. Although simple, anomaly detection can be used for applications such as detecting machine stoppage, sensor malfunctions, tracking production output, and more. recently spoke with Grollman about this solution. 

How essential is historical data in typical data science applications?

David L. Linville, Yongwoo Park, Nay Lin, and Yuanqun Lin’s default image

By: David L. Linville, Yongwoo Park, Nay Lin, and Yuanqun Lin

Calibrating an absolute distance meter (ADM) laser tracker requires long linear distances. For such distances, the room temperature is a significant factor. Even though the calibration room’s temperature is controlled within ± 2° C, actual temperature and temperature variation in one end of the room can be different from another because of uneven airflow in the room.

Controlling the air temperature and air flow along the rail can be costly for a laboratory. Thermally compensating the ADM distance for a laser traveling over a range of 50 m is not easy with just one temperature sensor. On the other hand, multiple temperature sensors placed along the ADM beam path further complicates ADM distance error compensation.

Ryan E. Day’s picture

By: Ryan E. Day

Brodie International provides liquid flow-meters and equipment for the petroleum and industrial markets. The company specializes in producing high-precision meters and valves that are used in the custody transfer of petroleum products.

The challenge

Brodie products involve components with complex shapes and assembly that made inspection measurements a serious challenge when using the traditional tools of their industry, which included height gauges, calipers, dial indicators, and a fixed coordinate measuring machine (CMM).

“We were using a fixed CMM,” says Tommy Rogers, quality manager at Brodie International. “Our older model CMM is good for measuring things like linear dimensions, hole patterns, tapers, circles, and geometry. But when it comes to measuring a compound curve like a helical shape, we were very limited.”

David H. Parker’s picture

By: David H. Parker

A 154-page report by Moreu and LaFave in 2012 explains unique problems railroad bridge engineers must contend with. The gross weight of cars went from 200,000 pounds to 263,000 pounds in the 1970s, and to 286,000 pounds in 1991. The ratio of live to dead loads are much greater for railroads than highways.

George Orji’s picture

By: George Orji

What are you looking to measure? This is one of the central questions for a metrologist (a measurement scientist) and is usually answered before measurements can proceed. It is impossible to make sense of the results without knowing the measurand—the actual physical dimension or other property of the sample you want to measure—regardless of the method you use.

However, the measurand could be hard to obtain if it is not defined properly, or if multiple instruments are involved.

Ryan E. Day’s picture

By: Ryan E. Day

If your manufacturing organization is going to grow, you know you need an inspection solution beyond the capabilities of micrometers and calipers. You know you need to gather more data in a faster and more reliable manner. It’s time to invest in a 3D inspection solution like a coordinate measuring machine (CMM). You also know CMMs require a significant investment and you shouldn’t rush in uninformed. Here are three questions to ask yourself to help you make wise decisions that will result in a good return on investment.

Stephan Schlamminger’s picture

By: Stephan Schlamminger

I discovered my affinity for attractive instruments while working a job before coming to NIST. My boss at the time had a love affair with the common hose clamp—the one with the worm gear.

Ryan E. Day’s picture

By: Ryan E. Day

Traditionally, technical jobs have been underrepresented by women. But that's changing, says Emily O'Dea, commercial services process manager at Hexagon Manufacturing Intelligence.

“Without a doubt we're definitely outnumbered,” says O’Dea. “I started [my career] in a smaller company. It was unusual because we were four application engineers, and three of us were women.”

Technical jobs can become great careers for both men and women. In today’s social and professional climate, we see efforts to encourage young women to study science, technology, engineering, and math (STEM) topics. We’ve even seen Hollywood reflect the changing mood in movies such as Hidden Figures, a film heralding the accomplishments of three black female mathematicians at NASA.

“Gender really shouldn't matter,” states O’Dea. “It’s a matter of what you enjoy and what you can teach others. It’s being able to be involved in this wonderful industry.”

NIST’s picture


A new measurement approach proposed by scientists at the National Institute of Standards and Technology (NIST) could lead to a better way to calibrate computed tomography (CT) scanners, potentially streamlining patient treatment by improving communication among doctors. 

Marlon Walker’s picture

By: Marlon Walker

Robots have been a part of industry longer than you might think. The patent for the first industrial robot, Unimate, was granted in 1961. While robots were sometimes utilized by larger manufacturers, such as automotive original equipment manufacturers (OEMs), they were rarely an option for small and medium-sized manufacturers (SMMs).

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