Software drives workflows and ensures repeatable process control. Select your software first, then decide the type and brand of CMM you need for measurement and data collection. (Image: Verisurf.)

There are best practices for selecting software, and that includes measurement and inspection solutions for manufacturing enterprises. Too often, manufacturing companies either minimize the importance of measurement and inspection software or, worse, they make purchase decisions based on hardware features that may or may not provide the results they need.

Only after taking delivery and using the equipment do they realize they have purchased software that is not up to the task, or the wrong type of coordinate measuring machine (CMM) for their workflow and the results they require.

The goal of this article is to help you avoid making the same mistake, through a comprehensive approach to software selection with critical consideration given to needs assessment, compatibility and extensibility. If you have a clear perspective of your needs and a better understanding of what questions to ask and what features to insist upon, the result will be a grounded solution that provides results, enhanced workflow and added value across your manufacturing enterprise.

Assessing Needs and Communicating

You have probably heard it said that if you don’t know where you’re going, any road will take you there. This holds true for selecting the right measurement and inspection software for your business, as well.  Before purchasing any software, regardless of application, you need a clear assessment: 

• What problem or opportunity are you addressing with technology?
• How are you getting the data you need, today?
• What is your workflow?
• What benefits will be expected from your new software?
• Most importantly: Who else in the organization will be affected?

When it comes to measurement and inspection, there are a few considerations that are overlooked surprisingly often. These include identifying the maximum part volume to be measured, understanding the number of measurement points needed to calculate results and the speed required to complete the measurement process.  Failure to consider these points may lead to a solution that is inefficient or, worse, one that does not support a critical workflow.

Here’s what you don’t want to do: see an impressive CMM and buy it based on a demo, possibly unrelated to your workflow or measurement objectives, then try to justify why it is the best solution for your organization.

That’s putting the cart before the horse.

This is why clear communications should start with your assessment and carry through the balance of your technology acquisition and implementation.  You might be surprised how many people in your organization can be positively impacted by data gleaned from measurement and inspection software. 

Investing in the right set of tools can mean using that data for quality inspections, reverse engineering, recreating data for missing features, support for tool making, and guided assembly of components, fixtures and weldments. Collected data can feed downstream applications, such as Product Lifecycle Management (PLM) or Statistical Process Control (SPC) databases.  Understanding who the stakeholders are and keeping them in the loop can add a great deal of value to your software. 

When selecting measurement and inspection software, make sure that it not only is compatible with CAD but is CAD-based at its core. A good question to ask is, “Does this software include intelligent 3D CAD modeling capabilities?” (Image: Verisurf.)

Tip #1 – Make a CAD Connection

In today’s business environment, every software platform must play well in the sandbox with already-established enterprise software or protocols. For business intelligence, this may be enterprise resource planning (ERP) software. For business communications, it might be Microsoft. Within the manufacturing enterprise, the standard is CAD. 

Finished products and components are typically conceived in a CAD environment and, for most manufacturers, they remain in this virtual state throughout the design-build process.  Data continues to be added to the file and its digital references throughout the lifespan of the product.

Model-Based Definition (MBD) is the term given to this commitment to an intelligent CAD model. The central concept embodied in MBD is that the 3D model provides all the detailed product information necessary for all aspects of the product life cycle. In the example of a machined component, this would include intelligent Geometric Dimensioning and Tolerancing (GD&T) annotations.

This removes the ambiguity, conflict and doubt that arises when drawings and models co-exist. By bestowing authority on the model, MBD eliminates errors that result from referencing an incorrect source and makes processes more efficient—no more searching to determine correct revision levels.

When selecting measurement and inspection software, make sure that it not only is compatible with CAD but is CAD-based at its core.  A good question to ask is, “Does this software include intelligent 3D CAD modeling capabilities?”

A truly CAD-based platform is the best way to ensure data continuity (digital thread) and maximize model-based measurement and inspection applications across your manufacturing enterprise.  Even if your organization is not at the point of fully embracing MBD, you want software extensibility and room to grow.

Tip #2 – Ensure Software Compatibility

If you make CAD-based measurement and inspection a requirement and do your homework, you’re already halfway covered with respect to compatibility.  The CAD connection will allow you to import, edit, save and export to and from virtually any CAD software. 

This is important when it comes to creating inspection plans that might have missing GD&T data that needs to be added, or when using reverse engineering to fill in missing features. 

Having an open CAD platform from which to operate and manage your measurement and inspection applications makes downstream integration and data sharing much easier with common file types and established transfer protocols. 

When evaluating measurement and inspection software, universal compatibility with all CAD software and all new and legacy hardware is essential for data continuity, repeatable process control and optimum resource utilization. (Image: Verisurf.)

Tip #3 – Don’t Forget About Hardware Compatibility

There are many types and brands of CMMs available on the market and if you remove the software from the equation, they all do one thing very well: collecting precision measurement points. 

The power behind all CMMs—contact and non-contact, fixed and CNC-type, portable arms, trackers and more—is the applications and workflow provided by the supporting software.  Most CMMs come with some sort of measurement and inspection software, but these are by CMM manufacturers, not software developers.

CMM manufacturers tend to be product-centric when it comes to software, focusing only on their machines.  It is for this reason that you need to keep your evaluation of software and hardware separate and stay true to your assessment plan. 

Consider your workflow and corresponding software applications first. This will lead you to the correct type of CMM you need before deciding on a brand of CMM.

When looking at measurement and inspection software from a hardware compatibility standpoint, it is important that the software can drive all CMMs and import measurement points in real-time.  This is especially significant when collecting and processing very large measurement sets encompassing millions of measurement points, typically associated with non-contact scanning of large complex surface profiles.  The software must be able to keep up with that influx of data or your productivity will grind to a crawl.

Universal device compatibility is critical when it comes to measurement and inspection solutions for a manufacturing enterprise. In the case of inspection, workflow routines should be able to be programmed once and then executed using any available CMM. This provides flexibility and consistency among operators and ensures repeatable process control is maintained, which is a key tenant of quality management.   

Tip #4 – Ask About SDKs for Integration

Selecting a measurement and inspection software that is based on an open platform with built-in compatibility and interoperability is critical to advancing the concept of the digital thread. 

To be effective, each step of the design/build process must play its part to stay connected.  Data in, data out, and the ability to pass information across all manufacturing applications and databases are important. A good question that will confirm a software’s commitment to interoperability is, “Do you offer a Software Development Kit (SDK).”

If the answer is no, integration might be harder than you think. 

Tip #5 – Remember Training and Maintenance

Training and compatibility go together. 

Over the last 10 years, there has been a surge of shops adding portable measurement and inspection assets.  These newer devices are based on open software compatibility, but many of the legacy CMMs that are still in use are based on closed systems and require dedicated software to run them. 

This has created a dichotomy in some shops between new and legacy systems.  Certain quality inspectors are trained to operate legacy machines and others are versed in newer equipment.  This causes bottlenecks due to inefficient resource utilization.

Your software selection should be enterprise-based: one software to drive all your CMMs (regardless of type or age), manage workflow and provide your desired application results. This will help with resource utilization, reduce bottlenecks and lower software training and maintenance costs, since you don’t have to support more than one platform. 

Bonus Tip – Ask for Help

When it comes to selecting inspection and measurement software, getting it right is important, so don’t be hesitant to ask for help.

 Software and hardware providers alike should be willing to take an interest in your business and help you put together the right recommendation for your unique application.  Just try to remain objective and stick to your goals. 

There are many measurement and inspection solutions available, so hold out for the one that fits your needs.

Learn more at Verisurf Software.

About the Author

Nick Merrell is Executive Vice President of Verisurf Software, Inc.

The post 5 Tips for Sourcing Measurement and Inspection Software appeared first on Engineering.com.

Vehicle inspections are crucial for both quality control and maintenance, but they take time. And in the bustling environments of manufacturing or fleet management, every second counts. Finding ways to reduce inspection times without compromising standards or (worse) overlooking a serious defect is a difficult balancing act that companies spend hundreds of millions of dollars trying to get right.

New Jersey-based UVeye has spent almost a decade developing its own unique approach to vehicle inspection using computer vision. With funding from Toyota Tsusho, Volvo and Hyundai Motors, the company has developed tools for inspecting vehicle undercarriages, outer bodies and interiors. Now, Amazon joins UVeye’s growing list of customers, which also includes CarMax and GM.

According to a press release from UVeye, the startup will begin rolling out its vehicle inspection systems across hundreds of Amazon warehouses in the U.S., Canada, Germany and the U.K. This follows a trial phase, during which the UVeye system identified issues that are often overlooked during manual inspections, including damage to the undercarriage and nails in the tire treads.

“Each new feature and improvement has brought us closer to where we are today, and we are so proud of the system, which is now being scaled to administer tens of millions of inspections a year,” said UVeye co-founder and CEO Amir Hever in the press release. UVeye claims that its technology is currently used to scan over 2 million vehicles and 20 million tires every year.

Tom Chempananical, Global Fleet Director at Amazon Logistics, echoed Hever’s sentiment in the same release: “With over 100,000 Amazon-branded delivery vehicles on the road, bringing 20 million packages to our customers every day, we can automate most of the inspection process at scale,” he said. “This reduces the time spent on inspections by DSPs and delivery associates, ensuring packages reach customers faster while improving road safety.”

According to Amazon, a typical manual inspection takes approximately five minutes, while the UVeye system takes less than a minute to complete the inspection process. The ability to shave those valuable minutes from each inspection makes this partnership between the two companies a natural one, especially since the data that UVeye’s system collects and the AI software that processes it both run on Amazon Web Services.

The post Amazon to Deploy Automated Inspections for 100,000 Delivery Vehicles appeared first on Engineering.com.

VXinspect enables you to validate the quality of manufactured parts and generate reports based on thorough engineering requirements. (Image: Creaform.)

As the manufacturing world pushes towards the goal of zero-defect production, part inspection is critical. Sheet metal’s thinness, coupled with its susceptibility to warping, demands precision inspection—even the most minor deviations have significant implications down the line. This underscores the need for exhaustive and precise quality control reports.

Sheet metal components have certainly evolved along with other technological advances in manufacturing to handle complex and intricate parts that traditional inspection methods now struggle to measure. The pursuit of zero-defect perfection has driven the development of quality control methods with cutting-edge technology that makes use of the vast amount of available data to navigate complex designs, quickly becoming the standard in the manufacturing world.

The post How to Improve Sheet Metal Inspection appeared first on Engineering.com.

This summer, the legendary Danish toy manufacturer LEGO announced a breakthrough: lasting bricks made from recycled PET, derived from soda bottles. The research project was considerable, requiring a staff of 150 working through 250 different plastic compositions to create a recycled plastic brick that met LEGO’s tough quality standards. Although an engineering success, on September 25, the company announced the suspension of the project, as an analysis of the carbon footprint of the recycling process showed no net benefit compared to oil-derived plastic resin. The company is switching research efforts to versions of resin derived from e-methanol, feedstock made from waste CO2 and hydrogen. Made from hydrogen derived from electrolysis powered by clean energy, this could prove to be truly zero carbon polymer resin.  

Access all episodes of This Week in Engineering on engineering.com TV along with all of our other series.

* * * 

Episode Transcript:

For decades, plastics engineers working in the injection moulding industry acknowledged that one of the global leaders in advanced technology and efficient production is a very familiar Danish company: LEGO.  

The iconic toy resin bricks have been a staple under Christmas trees for decades, and the relative handful of simple shapes that emulated building bricks have been joined by a stunning array of complex, moving parts. Lego is as much a culture as a toy, and like many European manufacturing concerns, the company is addressing environmental issues. A net zero carbon footprint is promised by 2050, and the company has a realistic action plan to achieve this goal. 

Another angle toward sustainability has been the company’s experiments in using recycled resins for its plastic bricks. On the 23^rd of June, LEGO developed a brick made from recycled PET resin, derived from soda bottles. The research effort was considerable, involving a team of 150, and testing over 250 different plastic resin compositions.  

The recycled resin used was sourced from U.S. suppliers and was approved by the FDA and the European Food safety Authority. A single 1L PET soda bottle contained material for about 10 small Lego bricks.  

But in a surprise move announced on September 25, Lego is suspending the recycled plastic initiative. From an engineering perspective, the project was a success, producing plastic parts with the necessary durability and physical properties to match LEGO’s standards. Although an engineering success, the overall carbon footprint of recycling the PET bottles, then reprocessing them into clean, injection-mould-ready regular resin, was no better than that of the current petroleum-derived version thermoplastic.  

The company announced that it still intends to make Lego bricks from sustainable materials by 2032. The LEGO experiment was an engineering success, but failed to meet its objective of lowering carbon emissions.  

Well, that’s a failure in one sense. It represents a sea change in the way environmental issues are handled in the manufacturing of mass production consumer goods. The carbon footprint of recycled products must represent the entire production process, and when selection, shipping, sorting, cleaning and reprocessing are factored into the equation, it’s possible that recycling is not only not environmentally more sustainable, but may be a net negative if the oil-derived version resin is recycled at the end of its lifetime, and repurposed into products where the carbon balance is net negative.  

LEGO is an especially demanding user of plastic resin, operating some of the world’s most advanced injection moulding equipment to produce low price consumer goods with consistently outstanding surface finish and dimensional stability. For firms that don’t require the quality attributes necessary at LEGO, recycled resin may still provide a carbon benefit.  

In the meantime, LEGO is investigating thermoplastics derived from e-methanol instead of crude oil. E-methanol is generated by reacting waste carbon dioxide with hydrogen, which itself can be generated through electrolysis, powered by green energy. These e-resins are definitely carbon neutral, but cost remains a concern.  

LEGO is a toymaker, but in the plastic injection moulding sector, when they talk, people listen. 

The post LEGO Abandons Recycled Plastic Project. Why? appeared first on Engineering.com.

3D computed tomography (CT) is essential in nondestructive testing for quality engineers in many industries. Creating a 3D image of a sample by compiling multiple X-rays from different directions around a rotational axis results in highly detailed representations of both internal and external features. This can reveal otherwise hidden flaws, and is also useful for geometric dimensioning and tolerancing analysis.

However, one of the drawbacks of using CT is that the images it produces can have limited resolution because the axis of rotation must be orthogonal to the x-ray beam. As a result, images of samples with an area of interest at their center may lack the necessary level of magnification to improve resolution because moving the sample closer would cause it to collide with the x-ray source as it turns.

Computed laminography is a subtype of x-ray tomography in which the rotation axis of a sample is titled at an oblique angle to the x-ray beam. It’s sometimes referred to as “2.5D inspection” because it lies between 2D x-ray radioscopy on the one hand and CT on the other. While it’s most commonly applied to flat components, such as printed circuit boards (PCBs) and microchips, CL is useful for any component with a high aspect ratio, with long x-ray path lengths along the object plane or where the size of the component requires the rotation axis to be positioned further from the x-ray source to avoid collision.

Tilted CL gets around this issue with CT by allowing the axis of rotation to be adjusted up to 30 degrees, enabling the sample to rotate fully. This results in both higher magnification and image clarity, as well as faster scanning times. A company press release claims that, “in one comparative test, a scan that took more than seven hours using an X-ray microscope was completed in less than one hour with Tilted CT on a SEMI S2/S8 compliant XT H225 2x system.”

The technology can also eliminate artifacts caused by high-density features that lower x-ray attenuation. Tilting the axis of rotation helps position areas of high-attenuation so that they rotate above or below a lower-density area of interest, rather than in front of it.

As a result, Tilted CT can aid the inspection of additively manufactured metal parts before they are separated from the build plate. Tilting the entire structure, including the plate, ensures that the denser plate is rotating at the same angle as the x-ray cone beam, thereby eliminating it as an obstruction.

In addition to the XT H225 2x system, Tilted CT is also available for Nikon’s XT H225 and large-envelope M2 X-ray CT systems. While it’s technically applicable to any size of component, users are still restricted by cabinet size and the requirements for holding a stationary sample at an oblique angle. As such, samples more than half a meter in length will likely be difficult to hold still during scanning.

Watch the introduction video below for more information:

The post Nikon’s Tilted CT Takes a New Angle on Nondestructive Testing appeared first on Engineering.com.

Although there is no universal certification body, there are dozens of organizations that offer training and accreditation for Six Sigma experts, including the American Society for Quality, The Chartered Quality Institute and more than fifty universities worldwide. Then there are the consultants and advisory services, from independent specialists on Upwork to industry titans like Bain and Company.

The benefits of Six Sigma tend to be framed in startling figures: billions of dollars in cost reductions supposedly achieved through double-digit gains in production efficiency. And yet, for a management philosophy so focused on statistics, independently verifiable evidence for Six Sigma’s effectiveness seems sorely lacking. What we have instead is mostly anecdotal and, as it’s been observed: the plural of anecdote is not data.

Given the rise of Industry 4.0 and resulting changes in the landscape of manufacturing, it’s worth asking whether Six Sigma’s principles and practices are still relevant today.

Six Sigma in 100 Words

Six Sigma refers to the fraction of a normal distribution curve that lies within six standard deviations of the mean. This represents the target defect rate for a manufacturing process, which works out to 3.4 defects per million opportunities (DPMO). This target is achieved through a combination of various tools and methodologies, such as DMAIC (Define, Measure, Analyze, Improve and Control) for existing processes and DMADV (Define, Measure, Analyze, Design, Verify) for new ones. There are also tools that can be found in other quality management systems besides Six Sigma: the 5 Whys, fishbone diagrams, control charts and cost-benefit analyses.

Who Uses Six Sigma?

Although it began at Motorola, Six Sigma quickly spread to many other large American companies, including Johnson & Johnson, Texas Instruments and, perhaps most famously, GE under the leadership of Jack Welch. Welch is often credited with spreading enthusiasm for Six Sigma after implementing it in GE’s manufacturing operations in 1995.

While the list of companies that claim successful Six Sigma implementations seems impressive at first blush, it’s worth noting that industry winners like Amazon and Google are balanced out by losers like Sears and Computer Sciences Corporation. This raises the question of whether Six Sigma advocates are guilty of a kind of survivorship bias. After all, even the originator of Six Sigma—Motorola—still ended up with staggering losses that led to the company splitting.

Nevertheless, Six Sigma companies do share certain features in common. Chief among these is being relatively large in size, with even the most diehard Six Sigma fans admitting that the philosophy is best applied by organizations with more than 500 employees. Of course, they’ll also add a caveat that there are still many Six Sigma tools and techniques that can work for small- and medium-sized enterprises (SMEs). Why waste a chance to recruit more acolytes to The Six Sigma Way?

Criticisms of Six Sigma

While there have been numerous complaints about Six Sigma over the past few decades, they can be roughly grouped into three broad categories: Applications, Effectiveness and Innovation. In each case, these critiques boil down to questioning whether Six Sigma is really worth all the trouble involved in implementing it, though they take different perspectives on where the trouble lies.

Applications of Six Sigma

In a book published the year after Jack Welch deployed Six Sigma at GE, quality consultant Philip B. Crosby pointed out that Six Sigma’s 3.4 DPMO standard was woefully inadequate for semiconductor manufacturing. Since semiconductors contain more than a million components, each of which needs to be defect-free for the entire unit to function, 3.4 DPMO simply won’t cut it. That was in 1996, and semiconductors have only gotten more intricate and complex in the decades since.

Many other complex products have entered the market since Six Sigma was first conceived, including those which use semiconductors as essential components. Given the growing complexities of manufacturing, it’s worth considering whether a quality management philosophy that predates the fall of the Soviet Union is still applicable in the age of Industry 4.0.

Six Sigma’s Effectiveness

Given all the enthusiasm for Six Sigma, one might expect that there would be plenty of evidence for its effectiveness as a quality management strategy. As noted above, there are assertions that companies which have deployed Six Sigma have seen hundreds of millions or even billions of dollars in cost savings as a result—but there are several reasons to be skeptical of such claims.

Satya S. Chakravorty, a professor of operations management at Kennesaw State University, and Praveen Gupta, director of quality for the Stephen Gould Corporation, have separately claimed that over 60 percent of corporate Six Sigma initiatives fail. According to Chakravorty, this is typically due to external Six Sigma experts who initially spearhead these projects but leave before they’re completed. Charles Holland (who, it should be noted, advocates for a competing quality management system) has argued that 91 percent of large companies that announced Six Sigma programs have trailed the S&P 500 since doing so.

The trouble is that the success (or indeed criticisms like Holland’s) of Six Sigma projects may suffer from the old informal fallacy of post hoc ergo propter hoc: after this, therefore because of this. As Yasar Jarrar and Andy Neely put it in their 2003 critique: “[A]re we making a true improvement with Six Sigma methods, or just getting skilled at telling stories?”

Does Six Sigma Stifle Innovation?

Another of Holland’s (admittedly biased) criticisms of Six Sigma is that it was narrowly designed to fix existing processes and, as such, allows little room for new ideas. Jarrar and Neely make similar claims, noting that most organizations are neither designed nor led to allow the kind of statistics-driven management that Six Sigma requires. As a result, incorporating Six Sigma into a company’s culture may inhibit innovation, either because its tools and methods are too rigidly applied due to a lack of internal expertise, or because the bureaucracy of Six Sigma forces all projects to conform to its particular format.

Chakravorty’s research has found that Six Sigma programs can end up adding more work—in the form of statistical analysis—to teams that are supposed to be eliminating waste, not squeezing more tasks into their daily routines. To be fair, this is more of a risk for companies that try to use Six Sigma by enlisting the temporary aid of consultants, rather than relying on internal experts or champions. Nevertheless, given the abundance of companies and specialists offering Six Sigma services, the likelihood of this outcome is certainly nonnegligible.

Is Six Sigma Worth It?

Although it’s obviously different from purchasing a new piece of equipment, such as a coordinate measuring machine (CMM) or a laser scanner, implementing Six Sigma does require a significant capital investment. Whether contracting with external advisors or consultants or building the expertise internally by hiring candidates with Six Sigma certifications or paying for training, Six Sigma doesn’t come cheap.

Is it likely to deliver a sufficient return on investment to justify that upfront cost?

Ultimately, the answer to that question depends on what you’re trying to achieve with Six Sigma and what kind of guarantees its practitioners can offer that isn’t laced in jargon and carefully curated statistics. As Terry Pratchett once wrote: “Always be wary of any helpful item that weighs less than its operating manual.”

The post Is Six Sigma Still Relevant in Modern Manufacturing? appeared first on Engineering.com.

IMAGE: Sandia National Laboratories

They may be in less demand today than during the COVID-19 pandemic but N95 respirators continue to be a crucial piece of personal protective equipment (PPE). For millions of people worldwide – from healthcare and construction workers to the immunocompromised and their families —having access to high-quality masks is simply nonnegotiable.

Given those stakes, N95 mask tests must be not only reliable but also quick enough to keep up with demand – which isn’t the case with existing systems. But this month, a team of engineers and scientists at Sandia National Laboratories revealed a promising new approach. Combining creative design with 3D printing, it could be the foundation of an all-in-one testing system that produces safer masks, faster.

Problems with the Current Approach to N95 Mask Testing

The core of an N95 respirator is a fine mesh of nonwoven polypropylene fabric produced by melt blowing, which forms an inner layer to filter out hazardous particles. Traditional testing of this core is “very time-consuming and not as efficient as it could be,” Michael Omana, an aerosol scientist at Sandia National Laboratories in Albuquerque, New Mexico, said in a press release. It involves using hot wax or putty to attach a mask to a flat plate enclosed in a box, introducing a test aerosol, and then measuring penetration levels. Because National Institute for Occupational Safety and Health requires 20 masks in each batch to be tested before the batch can be approved for use, the testing phase represents major bottleneck in the supply chain.

Beyond being time-consuming, this approach only tests the mask’s inner layer. “It doesn’t test geometry, how the respirator fits on a face, how it’s moved on and off multiple times, how the straps perform, how the nose bridge performs, how the mask can wear over time,” said Todd Barrick, one of the engineers on the research team.  Added Omana: “I think there were a lot of lessons learned with everyone suddenly looking at what the industry standards were.”

Testing N95 Respirators Holistically

To begin, the Sandia team replaced the flat plate with a 3D-printed model of a human face that can be loaded into a commercial filter test system. According to engineer and team member Brad Salzbrenner, the idea is to test more performance factors at once, such as how the mask fits. Using 3D printing enabled the team to make the model pliable to more closely resemble human skin.

This was a good start, but it didn’t fully account for real-world usage, so the team developed a more complete model of a human head that can be placed in an airtight box and then loaded into the testing machine. Salzbrenner and Barrick wanted to go even further, creating a way to test N95 masks for reuse—a common practice during the height of the pandemic but for which there is no testing standard.

IMAGE: Sandia National Laboratories

“We developed the chamber version to automate donning and doffing (putting on and taking off an item) to test respirator function over time, a predominant factor in wear on a mask,” Salzbrenner said. “It also mimics how a mask is set on the face and shows you any gaps that air and particles can get past.”

The hope is that these two methods can be combined into an all-in-one tester that accounts for all the aspects of the mask. As Omana explained, the current approach to testing doesn’t account for all the other potential points of failure besides the filter media. This new holistic test gets much closer to how N95 masks are used in the field.

The Sandia team is currently working on further refining their approach, with the goal of licensing the science to a company that can produce their all-in-one testers on a commercial level.

The post A Better Way to Test N95 Respirators appeared first on Engineering.com.

But we shouldn’t let these fancy new versions of predictive text obscure the fact that AI has been around and in development for decades, with numerous productive advances that predate ChatGPT and its ilk. There’s no better way to separate hype from reality when it comes to AI than by taking a look at some of the practical ways it’s being used in manufacturing, and more specifically in quality control (QC).

From machine vision to predictive analytics, here are three ways to use AI for QC. These are presented in descending order of maturity, i.e., the first example is already being deployed in practical applications, the second is mostly still in the pilot phase and the third is, at this point, mostly conceptual.

Unlike LLMs, which have an uncertain future at best, artificial intelligence in quality control has already proven to be productive, with many more applications to grow into. Whether and what effect it will have on the future of the manufacturing workforce remains to be seen but, whatever happens, it’s certainly an interesting time to be a quality professional.

#1 Machine Vision

Machine vision is a form of industrial automation utilized for inspection, sorting and robot guidance. The idea is to use a combination of lighting, cameras and software to extract information from a captured image. This information can be as simple as a go/no-go signal or as complex as the identity, orientation and position of each object in the image.

While machine vision doesn’t involve AI per se, the two technologies are becoming more closely intertwined as developers turn to neural networks in order to augment machine-vision algorithms and improve their accuracy. Audi, for example, has begun using AI for quality control of spot welds at its Neckarsulm plant in Germany. Prior to deploying machine vision, employees had to check the quality of welds manually using ultrasound, with samples drawn randomly.

Audi’s head of production planning, automation and digitization has claimed elsewhere that using machine vision with AI reduced the labor costs associated with these inspections by 30-50 percent. As such, it should come as no surprise that Volkswagen Group is rolling out the technology in Audi’s Brussels factory, the Volkswagen plant in Emden and Audi headquarters in Ingolstadt. All that’s required is to retrain the AI model for the different weld settings in each of these locations.

#2 Root Cause Analysis

The goal of improving manufacturing quality can be fulfilled in two complementary ways: by detecting defects in products and by identifying the source of those defects in the production process. As the example above makes clear, AI can assist in the first task by augmenting machine vision. The second task, more formally known as root cause analysis (RCA), can also benefit from the use of artificial intelligence.

Traditional RCA methodologies include Pareto analysis, fishbone diagrams and the five whys, among others. While these remain essential tools for quality professionals, they also require considerable knowledge and expertise in order to be used most effectively. This presents a problem for manufacturers staring down the barrel of the skills gap, worrying that their best quality people (in both senses of the term) will soon be retiring. When this risk is combined with the fact that manufacturers are producing more data than ever, an opportunity to deploy artificial intelligence as a solution seems only natural.

Here’s the basic idea: take all of your manufacturing process and product data and feed it to a machine learning model (typically, more than one). Through training, the model(s) will eventually recognize the correlates of product defects in the process data (e.g., a variation in the speed or feed of a CNC lathe that correlates with a gear not fitting properly into an assembly). Ideally, this entire procedure will happen in real time, giving manufacturing engineers enough of a heads-up that they can intervene in the process and avoid have to scrap or rework expensive parts.

#3 Supply Chain Management

Precipitated by the COVID-19 pandemic, the ongoing struggles of the global supply chain are continuing to impact manufacturers at virtually every stage of the product lifecycle. Amidst natural disasters brought on by climate change and geopolitical upheavals like Russia’s invasion of Ukraine, the availability of essential parts and materials seems less predictable than ever.

The recent shortage of microprocessors and its effect on the automotive industry is a prime example of how disruptions at one point in the supply chain can bring about serious consequences downstream. However, as with the other examples discussed in this article, managing supply chains is a task to which AI is particularly well suited. The combination of large volumes of data and a need to optimize across multiple parameters is what makes supply chain management such a tractable problem for AI.

By integrating the data from manufacturing execution systems (MES) and enterprise resource planning (ERP), machine learning models can forecast product demand and the availability of raw materials. There are myriad ways this integration can benefit manufacturers, from better data quality to enhancing cybersecurity. However, in the context of manufacturing quality, by putting this information together with supplier performance data, including delivery times and pricing, manufacturers can use AI to ensure consistent product quality across their entire supply chain.

The post How to Use AI for Quality Control in Manufacturing appeared first on Engineering.com.

That level of candor is refreshing in a world where every new piece of software is marketed as a “solution” (often one looking for a problem, rather than the other way round). You won’t hear many global organizations admitting that they have a mess that needs to be cleaned up but, in this case, Zinke’s description is apt.

Hexagon is a sprawling organization, with divisions encompassing everything from agriculture to mining to infrastructure and, of course, product design and manufacturing. That means a lot of data: equipment statuses, building codes, product specifications—all of it scattered across various formats and siloes. But if engineers have learned anything about data, it’s that extracting its full value means being able to leverage it in the right ways by making it available at the right times.

That’s the idea behind Hexagon’s digital reality platform in a nutshell: make data more accessible within and across organizations. And what better place to start than inside Hexagon itself?

According to Zinke, Nexus began as an internal tool for three reasons. “It’s about the gravity of the platform—getting buy-in from other parts of the organization. It also demonstrates commitment, because if you’re using it internally then customers know it’s not just an experiment. And finally, it’s also about maturity: eating your own dog food. If you never use the platform because it’s not mature enough, why would you turn it over to external customers?”

Openness and Transparency

Zinke frames Nexus as being data-centric, designed to be open, transparent and accessible. “If I want to share a PDF with you,” he explains, “I don’t care what you’ll be using to read it and you don’t care what I used to produce it. We think it should be exactly the same with engineering data.”

In practical terms, that means users can read and write data using Nexus without needing a proprietary application programming interface (API). This is epitomized in the recently announced Nexus for Developers, which is intended to accelerate third-party app development for Nexus by providing the software development kit (SDK) building blocks to connect other products and services to Nexus.

The goal is a kind of networking effect: the more products and services that are connected to Nexus (and to each other), the more valuable the platform becomes, not just for Hexagon but for manufacturers as well. Most manufacturing engineers have heard the familiar complaint about data siloes, where the design and production teams practically speak different languages because of the diversity of their toolsets.

Nexus could be the Rosetta Stone for engineering, if it can deliver what Hexagon is promising.

The Limits of Interoperability

For a platform built to connect innumerable engineering workflows, files and formats, ‘interoperability’ is clearly the rallying cry for Nexus. Asked about the limitations of Nexus’s interoperability, Zinke points out that it’s important to distinguish between syntactic interoperability and semantic interoperability.

The former is purely concerned with reading data but this is still a powerful capability on its own, because it enables engineers to do computations, perform statistical analyses and so on.

In contrast, semantic control requires understanding and preserving information across different applications as well as different programming languages. “Semantic interoperability requires working with different bodies that focus on the standardization of data or leveraging existing standards,” Zinke says. “The other aspect of it is ease of integration, which is more on the API/SDK side. We embrace the web ecosystem so that it’s easy to integrate with web apps and cloud apps. These days, web technology can be easily integrated with desktop systems, which is what we do ourselves.”

Hexagon’s approach here is based on open-source technology, specifically Microsoft’s Fluid Framework. This is another example of a development decision that would have been unusual from a large corporation such as Hexagon—or, for that matter, Microsoft—even a few years ago.

However, as Zinke points out, there are clear benefits to taking an open-source approach and avoiding proprietary technology wherever possible: “If you build proprietary tech, you now need to maintain it forever and it’s harder to adopt because your customers need to think about licensing and proprietary contracts. It doesn’t help you scale and it’s also not differentiating.”

What’s Next for Nexus?

While the scope of Nexus is undeniably ambitious, its rollout is proceeding at a more measured pace. This is reflected not only in its early development as an internal product, but in how it’s being delivered to Hexagon’s customers, subsidiaries and partners. “We’re currently in the preview phase, if you will,” says Zinke. “So we’re intentionally restricting the number of users until we get to the pilot phase in Q4 of this year.”

By that time, the Nexus ecosystem will include not only Altium, the electronic design automation software company which was the first to connect its cloud platform, but Nvidia’s Omniverse as well. And with every additional cloud and platform that connects, Nexus becomes that much more useful to designers and engineers, even though they may not realize it on a day-to-day basis.

“The idea is not to disrupt any workflows,” Zinke says, “because many of our customers are using desktop apps, so we don’t want to break anything but just, step-by-step, make those more powerful. In those cases, they’ll just see the new capabilities enabled by Nexus. For developers, there will be those building blocks to connect the data fabric, with visual collaboration, compute capabilities and so on.”

If data is the lifeblood of manufacturing, then Nexus is aiming to become a new circulatory system to invigorate its vital organs—CAD/CAM, PLM, and so on—rather than replace them.

The question is: Will the host accept this transplant?

The post Hexagon’s Nexus Aims to Be The Rosetta Stone for Engineering appeared first on Engineering.com.

A lot has been said about quality by various researchers, practitioners and professionals in terms of its definition, development and applicability in diverse backgrounds from almost every aspect of product-based businesses to service-based businesses. Different industries have defined quality in their own way based on their understanding of and experiences with it.

As a summary of the literature available in the field of quality, quality can be explained in terms of one or more of the following aspects:

1. Quality of design.
2. Quality of conformance.
3. Quality of performance.

That means how well the designer has conveyed his or her understanding of the designed product or service with utmost clarity. Greater design clarity makes it easier for product manufacturers or service providers to ready the product or service for a great experience. When the experience is integrated from design to conformance, the product or service performance is exceptional and appreciated by the community.

Such narratives and fundamentals of quality are used mostly by industries and organizations via various quality tools, techniques and systems—such as check sheets, histograms, Pareto charts, control charts, fishbone diagrams, quality circles, design of experiments and Six Sigma—to improve the quality of their organizations for a greater customer experience.

But these systems and tools weren’t built only to improve industry and organizational culture. They also can be used to improve our lifestyles. Using various aspects of quality improvement, you can create a culture of quality around yourself to live an extraordinary life.

“Quality is a way to live, plan and grow through your life, career or business.”

Create a worthy lifestyle

The worthiness of your life is defined by the experiences you have created for the people around you through your skills. Use these three aspects of quality to create a worthy lifestyle:

1) Quality of Design – Quality Management

Just as a designer tries to clarify every aspect of a product or service in the design stage, you also must  define your goals with utmost clarity for exemplary performance during your life. Breaking down your goals into various categories and timeframes helps build a firm understanding of the expectations you have for yourself. If your expectations are defined clearly, you will deliver on your efforts and achieve them. If they aren’t defined clearly, you also know that you aren’t going to live up to your expectations.

First, divide your objective into goals that are long term (five to 10 years), midterm (one to five years), short term (one year) and very short term (days, weeks or months). Next, categorize your goals—such as spiritual, financial, personal or professional—to plan and execute them accordingly. Also create a vision board to realize your goals every day, every hour and every minute.

Dividing your goals into various categories helps provide a holistic view of your life because, to achieve exemplary performance in life, you must balance all necessary aspects of life. Similarly, dividing your goals into various timeframes helps you understand the relationship between your efforts and results, even for small periods, such as what you are going to get after one month or one year of effort. If you have defined aspects of your goals clearly and get results accordingly, this keeps you motivated.

It’s also important to define standard procedures or processes to follow while making your goals a reality. Next, you must understand, define and write down a list of skills or resources you’ll need to realize your goals, and define or understand the process of acquiring, nurturing and mastering those skills or resources.

Preparing the list of skills and appropriate resources is not as easy as it may seem. You must have a thorough understanding of and conduct a self-analysis around questions such as: “What am I the best at? What do I love to do? What skills are in demand currently? What skills could I be paid well for having?”

Doing self-analysis around these questions is known as identifying your ikigai, which is popular as the Japanese secret to a long and happy life, or your reason for being. Various tools—such as psychometric assessments and brain profiling—help to decode your ikigai.

2) Quality of Conformance – Quality Assurance

After the aspects and their respective processes are well-defined and chosen in the design/quality  management stage, those defined parameters must be followed to get the desired product or service. Similarly, after setting your goals, creating vision boards, identifying necessary skills and resources, and clearly defining the process by which to achieve them, you must practice them to see the desired results.

After you identify what you are best at, what you could be best at or what could give you a cutting-edge advantage over others, the next step is to nurture and strengthen your skills. Begin by taking a professional course in the desired area. Remember that taking and completing a course just for the sake of getting good academic marks isn’t truly beneficial. You also must apply that knowledge practically.

To get practical exposure in your field of study, consider joining an internship program as a start. To achieve greater heights and gain exemplary recognition, think about how you can solve problems in your surroundings through your skills and knowledge, or by collaborating with someone with complementary skills.

Technology is evolving rapidly due to the integration of digital technologies with other core  technologies—such as electric vehicles, self-driving cars, data science and Quality 4.0, for example—and you must collaborate with these technologies to grow your expertise and remain relevant. This will give you a broad view of the applicability of your skills.

3) Quality of Performance – Quality Control

After the product or service becomes ready based on the designed specifications/processes, its actual performance must be checked and verified for its intended performance. You can’t guarantee that a product or service’s performance will align with the intended purpose all the time. Therefore, if any deviations or variations are too outside the practical consideration limits, the causes of such deviations must be identified and eliminated.

Similarly, if you’re not getting the exact results for your goals after following all the required processes, don’t be disappointed. Perform a thorough analysis of the different aspects to understand what isn’t working and improve accordingly.

Whether it is to improve the performance of a product or service, or to improve your skills in a particular area to help achieve your goals, the best way to do it is to connect with individuals from areas of similar and complementary expertise. This will give you a better understanding of the problem areas by way of appropriate feedback mechanisms. Appropriate feedback mechanisms give an individual or organization a deeper understanding of their concerns, the scope of the necessary improvements and the way forward to achieve those improvements.

You are Worthy

You can live a worthy lifestyle by creating a culture of quality through three aspects: design, conformance and performance, or management, assurance and control. Quality improvement is a lifelong journey with small milestones. It’s not a destination. You must understand that, whether it is life, career, business, industry or technology, everything is growing and evolving.

In this ever-growing and evolving technological world, always remain aware of the fact that you are replaceable if you are not constantly upgrading yourself irrespective of where you are in your career. Therefore, constant evaluation and execution around these three quality aspects are needed to have a worthy and exponentially growing lifestyle.

Raunak Gupta is the creator and crafter of Gyaannirudra, a career counseling initiative in India. He is pursuing the fellow program in management at the Entrepreneurship Development Institute of India in Ahmedabad. Gupta received a master’s degree in computer-integrated manufacturing from the Samrat Ashok Technological Institute in Vidisha, India.

Reprinted with permission from Quality Progress© 2023ASQ, www.asq.org. All rights reserved. No further distribution allowed without permission. Read more content from ASQ here.

The post Living a Quality Life appeared first on Engineering.com.