Dirk Dusharme @ Quality Digest’s picture

By: Dirk Dusharme @ Quality Digest

In any lab setting, bench space is limited. Between samples, notebooks, laptops, and other various supplies, it can be hard to find a place to put your test or measurement equipment.

If you use microscopes in your daily inspection work, the need to use two systems to look at one sample compounds the problem. Inspectors often observe a sample on a low-magnification microscope to identify an area of interest, then move the sample to a high-magnification and high-resolution microscope to take measurements and capture images. The process of moving samples and reacquiring the area of interest on another microscope is inefficient and means you have two systems taking up valuable space.

The solution to this challenge is surprisingly simple: use one system that provides low magnification for the initial review and high magnification with high resolution for the detailed inspection. One example is the Olympus DSX1000 digital microscope.

Ryan E. Day’s picture

By: Ryan E. Day

Manufacturing is a very competitive business where high-quality products are expected. And some clients require extremely tight surface measurement tolerances, so being competitive means investing in tools that can satisfy customer requirements.

The confocal advantage

Submicron 3D observation and measurement is a game-changing reality with confocal microscopy. Optical and standard digital microscopes are unable to measure such tiny shapes with the level of resolution and clarity that smart manufacturing demands.

Operators using Olympus’ LEXT OL5100 laser microscope have the advantage of more than 17,000X magnification. This kind of power allows for nanometer-scale measurements used for step-height and volumetric measurements. Many manufacturing clients also require surface roughness analysis of materials at this scale.

Olympus LEXT OL5100
Olympus LEXT OL5100 microscope

Multiple Authors
By: Joe Chew, Jeroen van Tilborg

The Berkeley Lab Laser Accelerator (BELLA) Center at the Department of Energy’s Lawrence Berkeley National Laboratory (Berkeley Lab) has developed and tested an innovative optical system to precisely measure and control the position and pointing angle of high-power laser beams with unprecedented accuracy—without interrupting or disturbing the beams. The new system will help users throughout the sciences get the most out of high-power lasers.

The experimental validation effort was led by doctoral candidate Fumika Isono of Berkeley Lab and UC Berkeley. Her findings are described in a paper published recently by the Cambridge University Press journal, High Power Laser Science and Engineering.

“This is a tremendous advancement in measurement and control that will benefit high-power laser facilities worldwide,” says Cameron Geddes, director of Berkeley Lab’s Accelerator Technology and Applied Physics (ATAP) Division, of which the BELLA Center is a part.

NVision Inc.’s picture

By: NVision Inc.

NVision’s 3D laser and computed tomography (CT) scanning services are helping RedBone, a manufacturer of hand-crafted goose calls, get its unique products to customers faster, enabling the Elton, Louisiana, company to significantly expand its output.

After scanning two key components of a call—the barrel and the insert—NVision (Southlake, Texas) provided RedBone with computer-aided design (CAD) files containing all the dimensional details of the hand-built parts, which are then used for reproduction. As a result, RedBone has been able to grow its business while ensuring that every replicated part perfectly matches the specific dimensions of the hand-built original.

Founded in 2005 by Nathan Wright, RedBone creates goose calls that are highly regarded throughout the hunting and calling-competition communities for their quality of design, construction, and sound. Calls produced by RedBone include the specklebelly goose, snow goose, blue goose, Ross’s goose, and cackler goose. The company also produces calls for sandhill cranes, buzzards, coyotes, and more. RedBone calls have been used successfully in numerous calling competitions, including 12 of the last 14 World Championships, and are considered to be among the best available on the market.

Nate Serafino’s picture

By: Nate Serafino

Industrial X-ray and computed tomography (CT) for nondestructive testing are rapidly expanding, with new applications and inspection systems emerging all the time. With so many choices available, it is critical to match the right technology with your individual inspection goals. Understanding the tradeoffs between X-ray/CT system types and what works best for your specific applications will help save time and money for years to come.

One of the most important concepts to learn prior to selecting an industrial CT system is the difference between flat-panel detector cone beam CT systems (CBCT) and line-detector fan beam CT systems (FBCT). Both modalities are powerful solutions for CT inspections; however, significant performance and time trade-offs exist between them. In fact, many CT system manufacturers offer CT systems equipped with a flat-panel X-ray detector and a linear detector array. Both technologies enable powerful 3D visualization capabilities of internal features and defects, but each excels in different applications and use cases. The different use cases in conjunction with price and time differences between FBCT and CBCT make this concept critical for any prospective buyer to understand.

William A. Levinson’s picture

By: William A. Levinson

Part one of this article showed that it is possible, by means of a Visual Basic for Applications program in Microsoft Excel, to calculate the fraction of in-specification product that is rejected by a non-capable gage, as well as the fraction of nonconforming product that is accepted. This calculation requires only 1) the process performance metrics, including the parameters of the distribution of the critical to quality characteristic, which need not be normal; and 2) the gage variation as assessed by measurement systems analysis (MSA).

Part 2 of the series shows how to optimize the acceptance limits to either minimize the cost of wrong decisions, or assure the customer that it will receive no more than a specified fraction of nonconforming work.

Raghava Kashyapa’s picture

By: Raghava Kashyapa

Bearings are important components of mechanical equipment. They are specifically designed to convert the direct friction from parts in relative rotation into rolling friction or sliding friction of the bearing. As a result, bearings are extremely important in reducing the friction coefficient and ensuring the long-term stable operation of a machine.

The bearing surface and bearing rollers both have an important impact on the installation performance, use, quality, and life of the bearing. Common defects on a bearing roller’s surface, such as wear, cracks, bruises, pitting, scratches, or deformation, can lead to machine vibration and noise, accelerate oxidation and wear, and even damage the machine. It is thus paramount to inspect the surface of the bearing as well as the bearing rollers to prevent defective products from entering the market.

The assembly of bearings globally has been fully automated for the most part. However, the bearing roller inspection and surface inspection of the bearing before and after assembly is still largely based on manual inspection. The method is labor-intensive, inefficient, costly, and easily affected by such factors as inspector qualification and experience, visual resolution of the naked eye, and fatigue.

Hari Polu’s picture

By: Hari Polu

Manufacturers of high-end semiconductor electronic products used in consumer, industrial, and military applications have long relied on precise testing methodologies to identify the location of defects such as voids, cracks, and the delamination of different layers within a microelectronic device, also known as a microchip. Manufacturers also employ scanning acoustic microscopy (SAM), a noninvasive and nondestructive ultrasonic testing method, which became an industry standard to detect and analyze flaws during various chip-production steps and in the final quality inspection after packaging.

In addition, SAM is often used as a failure analysis method to identify a specific root-cause failure mechanism when a device fails during use.

scanning acoustic microscopy

Emily Newton’s picture

By: Emily Newton

Effective equipment testing is essential for manufacturers of industrial equipment and end-users. Without testing, defects and damage can shorten the life span of equipment, cause unplanned downtime, and reduce the quality of finished goods.

This is especially true for businesses in sectors like food and beverage manufacturing, where equipment being in good condition is necessary to maintain safety and quality standards.

New industry 4.0 technology is transforming how businesses approach industrial equipment testing. Techniques enabled by innovations like AI and IoT devices can help companies automate testing processes and gather additional information on equipment performance.

IIoT enables new types of data collection

In some cases, new industrial IoT (IIoT) devices may make it practical to collect real-time operational data on parameters that were difficult or impractical to track automatically in the past.

William A. Levinson’s picture

By: William A. Levinson

The first part of this series introduced measurement systems analysis for attribute data, or attribute agreement analysis. AIAG1 provides a comprehensive overview, and Jd Marhevko2 has done an outstanding job of extending it to judgment inspections as well as go/no-go gages. Part two will cover the analytical method, which allows more detailed quantification of the gage standard deviation and also bias, if any, with the aid of parts that can be measured in terms of real numbers.

Part one laid out the procedure for data collection as well as the signal detection approach, which identifies and quantifies the zone around the specification limits where inspectors and gages will not obtain consistent results. The signal detection approach can also deliver a rough estimate of the gage’s repeatability or equipment variation. Go/no-go gages that can be purchased in specific dimensions, or set to specific dimensions (e.g., with gage blocks) do indeed have gage standard deviations even though they return pass/fail results.

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