Today’s advanced product development tools and processes go a long way in helping to ensure product quality. Finite Element Analysis (FEA) and simulation allow components, assemblies, sub-assemblies, and even entire systems to be analyzed for manufacturability, durability and quality in the early upstream stages of product development – well in advance of manufacturing. However, even the best designed and painstakingly manufactured products are not immune to problems. Consequently nearly all manufacturers have implemented quality safety nets to capture noise, vibration and other quality issues before products reach the customer.

This level of quality control often involves a manual parts inspection at the end-of-production. While some implement one hundred percent part inspection others are content to do random spot checks. So what’s the problem? Too many companies continue to rely on the discretion of a quality inspector leading to a subjective interpretation of acceptable quality – subjective decisions that are rooted in emotion and may or may not agree with the customer’s interpretation of quality. Too often quality is more of an art than it is a science; and standards can vary widely.

Real World – Real Challenges

While design and simulation tools have clearly taken the product development process and product quality to new levels, there remains a real need for physical science-based validation. Manufacturing, assembly, and design imperfections can manifest into expensive quality issues including mounting customer complaints, recalls, excessive warranty claims, and lost market share and contracts. It’s therefor increasingly important to verify that all final products meet quality standards prior to reaching the customer.

From automotive and appliance to medical, transportation and beyond, original equipment manufacturers across industry generally have an extended list of suppliers. And for OEMs, the quality of components and sub-systems their suppliers provide is absolutely critical. “Consumers rarely differentiate between the system and its components. Sub-system flaws generally create overall negative feelings toward the system as a whole,” said Christopher Kus, Project Engineer and NVH Expert for the Automotive Seating Business Group-NAO of Faurecia. “For example in the case of an automobile, a rattling window, unreliable ventilation fan or noisy seat motor will result in a reputation for poor quality for the entire vehicle; not just that particular sub-system.”

Because their reputation is at stake, along with market share, OEMs are extremely particular about suppliers. If you can’t meet quality standards and deliverability schedules, they will find someone who will. As a global leader in automotive technology, Faurecia Seating develops and produces seat systems that optimize the comfort and safety of occupants while offering premium quality to its customers. It develops innovative solutions for thermal and postural comfort, health and wellness and advanced safety for the use cases of today and the cockpit of the future. By quantifying and qualifying quality Faurecia is able to meet and exceed the expectations of its customers – and indirectly with their customers.

“Throughout manufacturing there’s no time for detailed laboratory quality testing. As a result companies generally rely on a Pass/Fail or Red Light/Green Light inspection process,” explained Kus. “To get to that point we take subjective, often vague, input from the customer to create objective quantifiable metrics. We begin with subjective jury evaluations to determine a rating of specific noises. From the results we can use statistical methods to choose what psychoacoustics best can be used to describe how the human ear perceives these sounds of the specific sound source. From there we use statistical methods to create an algorithm to correlate to the rating system of the human ear subjective ratings.”

Removing human subjectivity from the equation is critical. “For example, supplier requirements might call for the product to satisfy a number of specific design and performance parameters and be free of other objectionable noises. Noise and vibration quality levels should not be left to human emotion. What’s unacceptable one day might be accepted the next; depending on any number of circumstances or how the individual feels on that particular day.” All reputable manufacturers implement some sort of quality assurance. But if quality is open to interpretation they are headed down a dangerous road.

The Answer: Automating Objectivity

Manufacturers must have a proven process that allows them to come as close as possible to one hundred percent quality. Leaving such things to subjective measurements is a recipe for disaster. Quality inspection must be repeatable, consistent, metrics-based, and above all subjective. Faurecia, and a number of other suppliers, employ automated end-of-production quality inspection test systems to verify each part is defect-free before it’s shipped. Cincinnati Ohio’s Signalysis Inc. ( is a leading provider of such automated quality test systems. In order to ensure that each system meets the unique requirements of each of its customers, Signalysis employs a thorough, highly technical and systematic approach.

The Signalysis process begins by gathering customer specifications. This could be anything from specific failure modes – such as ticking in a motor, excessive noise or rattling, modal parameters for rotors or an OEM’s general dB specification to the non-descript ‘other objectionable noises’ catch-all. The next step is to acquire baseline data on a sample of parts to help quantify acceptable from non-acceptable. Ideally these test parts represent the full range for each aforementioned failure mode including those that do not meet quality standards, borderline parts, and acceptable parts.

Signalysis quality inspection test systems are programmed within the company’s proprietary SigQC™ software. As you might imagine, sophisticated software plays the key role in the process. The old adage garbage in - garbage out certainly holds true; and hardware and software are equal partners in creating the optimal solution. “The measurement hardware (data acquisition devices, sensors, triggers and timing) along with the proper setting of measurement parameters drives the quality of the data,” said Signalysis Solutions Engineer, Robert Cagle. “Having a robust and versatile software toolbox of functions and data management is the key to designing the perfect objective quality testing algorithm.”

The sequence of actions is implemented with drags, drops, and line connections to specify the order. A database of acceptance tests with complete description of products, test setups, and pass/fail specifications is available for automatic access. As a product arrives to the test station, a bar code scanner or keyboard entry provides the information needed for automatic selection and initiation of the acceptance test. Upon test completion, the unit automatically receives a pass/fail quality grade. The data for the unit under test are displayed in a separate window.

Implementation Challenges

“The biggest challenge,” explained Cagle, “is that the lab/theoretical data is never the same as end of line testing and everything else is a symptom of that. First of all, what we can calculate on paper is never seen in experimental data exactly as we think it will come to fruition,” he explained. “So first we have to adapt to that. Sometimes lab data might have been collected in an anechoic chamber with microphones.

Because this isn’t conducive to the production line tester that is being developed without a sound chamber, this can lead to the issue of buy-in from suppliers and OEMS. There may be some concern that what we hear in microphone data is what we are feeling from vibration data taken with an accelerometer or laser vibrometer.” Because automated objective testing is a break from the norm for some, there is an adjustment period for many who choose to implement objective quality testing. The massaging of metrics and tolerance limits in that transition can sometimes cause headaches and frustration. So much so that the user may turn the machine off and revert to more subjective testing methods. “However, those machines always need to be turned back on,” said Cagle. “In the long run and we inevitably get the call to get the system back up and running. The key to long term success is the understanding and trusting the process and a little patience at the beginning will yield measurable quality improvements.”

The Product Lifecycle

So do quality testing solutions differ depending on where they might be injected throughout the product lifecycle? “We find that we typically use similar solutions throughout the entire lifecycle,” explained Cagle. “The hardware and system configuration might change some. For example, design and prototype testing are normally more lab-based and do not require the automation of a production test, at the opposite end, the field testing process is normally more portable. Across the board I would say the algorithms are mostly the same except for structural changes due to mounting and difference in boundary conditions. For example, mounting the unit under test on a shake table is different than how it is mounts on an assembly line pallet and definitely different than being installed in a vehicle. The goal is to get all of the boundary conditions the same but this might not be practical for complex structures.”

End of production testing is important; but it’s just one area when quality should be assessed. Implementing quality testing from concept through production helps to ensure that flaws are identified as the product passes through each product development stage gate. This significantly reduces the number (and associated cost) of rejected parts being tossed on the scrap pile. But what about products with inherent flaws that do not surface until the product is in use? Here again there are a number of solutions in play.

Signalysis offers a portable test system that allows users to isolate problem areas after the product reaches the final customer. This enables the problem in hand to be quickly identified and corrected while relaying vital information back to the supplier for the design of next generation products.

Field troubleshooting is another opportunity to correct quality issues and communicate findings back to the manufacturer. This is useful in circumstances when quality testing systems may not be the best option. Such is the case when all components check out but the system as a whole is failing. Milford, Ohio’s Six D Testing & Analysis ( has pioneered many of today’s testing and analysis/simulation best practices.

“When troubleshooting manufacturing equipment, for example, it’s critical to quickly and accurately determine the root cause of a problem,” said company President, Mike Carlier. “A vibration issue can be the cause of poor quality. The culprit could be vibration originating from the unfavorable dynamic interaction of otherwise good systems or components. Diagnostic testing is used to determine the root cause and correct the problem as quickly and effectively as possible.” Again, this allows vital information to be communicated back to motor, controls, cooling system, or other system component(s) suppliers can make adjustments.

In-production manufacturing equipment issues are often critical as downtime is lost revenue. As a result, field testing also offers the option to implement corrective action on-site, such as adding damping or stiffening to solve the problem, without involving the supplier.

No Place for Guesswork

Today’s competitive supply chain landscape is crowded with would-be suppliers just waiting for an opportunity to pounce. And there is nothing more dangerous than to have the quality of one’s work brought into question. When it comes to product quality, there’s no place for guesswork, subjectivity, or trial-by-error engineering. Objective quality testing is rooted in science and generates results that are measurable, consistent and repeatable.