NVision Inc.’s picture

By: NVision Inc.

Air conditioning is hotter than ever—hot as in demand, a must-have, a comfort most of us want, and, with higher-trending temperatures generating heat-related health issues, one that many can no longer live without.

NVision, a leader in 3D noncontact optical scanning and engineering, has worked extensively with the HVAC (heating/ventilation/air conditioning) industry for more than 30 years. Recently, NVision helped major HVAC manufacturers successfully:
• Evaluate and confirm the viability of converting copper system components to aluminum
• Reverse-engineer and improve the design of air conditioning (A/C) drip pans

More than a century after Willis H. Carrier built the first modern electrical air conditioning unit, A/C systems are a fixture in most U.S. homes. According to the U.S. Energy Information Administration (EIA), nearly 90% of U.S. households in 2020 were using A/C. Internationally, the use of A/C is expected to rise during the next 30 years, with the world’s hottest regions producing much of the demand.

Patrice Parent’s picture

By: Patrice Parent

Battery trays are structural elements that enclose and safeguard the battery modules and their supporting electrical and thermal management systems. Battery modules must precisely fit into the battery tray, which must fit seamlessly within the vehicle's chassis.

Variations in tray dimensions can result in improper assembly or misalignment. Specifically, flatness errors can lead to bad welds and loose connections to the power system (disrupting the electricity flow) or to the cooling system (altering the heat transfer). Such deficiencies will eventually affect the battery’s performance and longevity, and reduce the car’s efficiency.

Man wearing a Creaform black shirt uses the MetraSCAN to measure an EV battery tray with C-Track in the foreground while the scan is simultaneously showing on a screen in the VXelements software.
Precise, fast, and certified quality inspection is of the utmost importance for EV manufacturers.

Challenge

How can an EV part manufacturer perform quality inspection within the required time and tolerances?

NIST’s picture

By: NIST

In research, sometimes the bumpy path proves to be the best one. By creating tiny, periodic bumps in a miniature racetrack for light, researchers at the National Institute of Standards and Technology (NIST) and their colleagues at the Joint Quantum Institute (JQI), a research partnership between the University of Maryland and NIST, have converted near-infrared (NIR) laser light into specific desired wavelengths of visible light with high accuracy and efficiency.

The technique has potential applications in precision timekeeping and quantum information science, which require highly specific wavelengths of visible laser light that cannot always be achieved with diode lasers (devices akin to LED lights) to drive atomic or solid-state systems.

In previous experiments, NIST researchers have used ring microresonators to transform near-infrared laser light into a combination of longer and shorter wavelengths. Credit: S. Kelley/NIST

Ideally, the wavelengths should be generated in a compact device, such as a photonic chip, so that quantum sensors and optical atomic clocks can be deployed outside the laboratory, no longer tethered to bulky optical equipment.

Donald J. Wheeler’s picture

By: Donald J. Wheeler

Clear thinking and simplicity of analysis require concise, clear, and correct notions about probability models and how to use them. Here, we’ll examine the basic properties of the family of gamma and chi-square distributions that play major roles in the development of statistical techniques. An appreciation of how these probability models function will allow you to analyze your data with confidence that your results are reliable, solid, and correct.

The gamma family of distributions

Gamma distributions are widely used in all areas of statistics, and are found in most statistical software. Since software facilitates our use of the gamma models, the following formulas are given here only in the interest of clarity of notation. Gamma distributions depend upon two parameters, denoted here by alpha and beta. The probability density function for the gamma family has the form:

where the symbol Γ(α) denotes the gamma function (for α > 0):

The mean and variance for a gamma distribution are:

Miron Shtiglitz’s picture

By: Miron Shtiglitz

The main benefit of deploying artificial intelligence (AI) for quality inspection is a significant improvement in defect detection. However, the data generated and stored by inspection systems have the potential to deliver additional benefits, including major improvements in yield.

Anyone working in the world of quality inspection will be aware of the limits of manual inspection and the potential benefits of greater automation, including systems that use AI and deep learning algorithms. With recent advances in AI, the most sophisticated inspection systems available today can reduce the error rate to below 1%. In comparison, for manual inspectors a host of factors, such as fatigue and cognitive bias, means the error rate is usually closer to 10%.


Quality inspection sensors on the job

However, a significantly reduced error rate isn’t the only area where automation can have a significant effect.

Silke von Gemmingen’s picture

By: Silke von Gemmingen

Smart waste management is one of the core tasks within smart cities, i.e., those urban areas in which innovative technologies and data-driven solutions are used. They aim to improve residents’ quality of life, minimize environmental impact, and use resources more efficiently. Conserving resources isn’t just about recycling, but also about innovative approaches to collecting waste and then disposing of or processing it in the best possible way. One of the most important components of smart cities is therefore the introduction of efficient waste collection systems. In addition, by May 1, 2025, at the latest, waste from organic waste bins delivered for composting or fermentation in Germany may only contain a maximum of 3% (by weight) of foreign matter when delivered.

Creaform’s picture

By: Creaform

End-to-end manufacturers are companies that lead products through the entire manufacturing process, from design to customer delivery. Unlike businesses that manufacture their products with a segmented manufacturing process, end-to-end manufacturers have complete control over the different parts of production, which ultimately affects the time, money, and effort associated with product innovation and time to market.

But what about product quality? What practices can end-to-end manufacturers adopt to increase quality and deliver high-end products to their customers? Considering that they control the entire manufacturing process, how can they judiciously use quality data from one step to enrich the next?

The challenges of end-to-end manufacturers

Nowadays, competition is strong. Customers demand quality, and they want it delivered at a reasonable price. The market eagerly awaits the launch of new products that are both innovative and attractive. Products must function without any defects for an indefinite period while being continuously improved and updated.

Eric Whitley’s picture

By: Eric Whitley

High-precision manufacturing is critical in industries where even the slightest deviation can lead to significant consequences. It encompasses processes that demand the utmost accuracy, often in sectors like aerospace, medical devices, and electronics. Precision is important due to its direct effect on the functionality, safety, and reliability of the end products.

Traditional methods of quality control in manufacturing primarily rely on manual inspections and standardized procedures. These methods, while effective to an extent, are often time-consuming and prone to human error. In contrast, artificial intelligence (AI) offers more accuracy and efficiency, automating the detection of defects and inconsistencies, and providing real-time solutions to maintain high quality control standards.

AI’s role in enhancing precision and accuracy

In manufacturing, AI is revolutionizing anomaly detection and resolution. Advanced algorithms and machine learning enable AI systems to process vast amounts of data and pinpoint minor inconsistencies that might escape human scrutiny. This processing ability not only boosts product quality but also enhances manufacturing efficiency.

Andrew Novick’s picture

By: Andrew Novick

While people around the country are preparing champagne and getting ready to watch the ball drop on New Year’s Eve, I’m closely monitoring our clocks at the National Institute of Standards and Technology (NIST). 

We actually monitor them every nanosecond of every day, not just on New Year’s Eve. We have the duty to uphold the official U.S. time. There are many ways we get this time to the public, which we call “distributing” the correct time. We send out the time via satellite, radio signals, telephone lines, through internet protocols, and our web clock at time.gov.

While we always ensure that we have the exact time, on New Year’s Eve (and daylight saving time changes), many more people are looking out for the correct time. Sometimes on New Year’s Eve, there is a leap second—it’s an extra second we add to the time worldwide to let Earth “catch up” to our official time. We haven’t had one of those in a while, but it does make New Year’s Eve even more interesting for timekeepers like me.

Industrial Inspection and Analysis’s picture

By: Industrial Inspection and Analysis

Unlike the traditional engineering process of designing a part, product, or component from the ground up, many times in life we need to start with an existing item and work backward to solve a problem. It’s a process known as reverse engineering, and it begins by obtaining accurate data about the object.

Industrial Inspection & Analysis (IIA) has been an early adopter of laser and CT scanning and re-creation of geometry for the manufacturing industry, from medical devices and heavy equipment to firearms, tooling, and plastic companies.

Reverse engineering is used by both corporations and individuals for a variety of reasons. Some common scenarios seen at IIA are highlighted below.

Scenario No. 1: No design files exist

Reverse engineering is commonly used to accurately reproduce older parts (such as legacy components), where the original drawing no longer exists or can’t be accessed. Among the most interesting uses of this technology is preserving historical objects.

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