Why wait to catch quality issues at end-of-line testing?

Last updated on January 16th, 2023

Despite the coronavirus pandemic, global production output of motor vehicles increased by 3% in 2021, to almost 80 million units. To keep up with demand, OEMs and Tier-1 suppliers manufactured vehicle parts to the value of over $2.3 billion over the same time period, and that number is expected to increase by over 10% to $2.6 billion by the end of 2022. To ensure that parts are built to the appropriate specifications, end-of-line (EOL) testing has been considered one of the most crucial steps in automotive quality testing to date.

However, modern technologies, like machine learning and artificial intelligence (ML/AI), are making it possible for automakers and Tier-1 suppliers to catch failures in real time – before parts appear at the end of the line. By addressing manufacturing defects and failures on the production line as they happen, engineers can stop relying on EOL testing alone to detect quality issues after the faulty part has already been manufactured.

This blog explores the importance of current EOL testing methods and introduces alternatives that can supplement quality control programs for manufacturing in the automotive industry.

Why automotive end-of-line quality testing matters

To ensure the quality of complex products, like e-motors, axles, engines, and transmissions, OEMs and Tier-1 suppliers need to separate defective or out-of-spec units from those units that will ship to their customers. End-of-line tests are designed to pinpoint any visual, acoustic, or other imperfections that may have occurred during the manufacturing process, to ensure the quality of the products before the component reaches the loading dock.

Three main approaches used for EOL testing

Methods of EOL testing vary depending on the type of product or assembly and the characteristics  of interest. The three most used end-of-line tests are:

  1. Hydraulic testing which provides valuable data on the rotary and linear motion of components and valves. This methodology employs a range of sensors that are controlled by manufacturing analytics software to detect fluctuations in pressure, flow rates, and leakages on parts and assemblies.
  2. Roll brake testing which involves simulating real-world road conditions on a mechanical platform to measure a vehicle’s braking functionality. This dynamic verification and diagnostic system provides accurate and repeatable test results, in a controlled environment, to deliver vital braking data for EOL quality control analysis.
  3. Noise, vibration, and harshness (NVH) testing which is done using an array of sensors, including accelerometers, microphones, and force sensors, measures NVH on vehicle components and subassemblies. The information collected is uploaded to an automotive quality testing solution and converted into digital data for predictive quality analysis. For example, noises generated by geared assemblies can indicate manufacturing defects, which can lead to passenger comfort issues or assembly failures that can risk passenger safety. Using NVH testing at the end of the line can identify warning signals to predict such problems.

The goals of any EOL test are to pass units that can be used in the final assembly of a vehicle and to fail defective ones.

Challenges and limitations of current EOL testing

Unfortunately, even when OEMs and Tier-1 suppliers conduct rigorous EOL tests, some defects still slip by. Manufacturers in the automotive industry can experience significant costs associated with defective parts, like claims on components that fail before the end of their warranty period. These limitations can have a negative impact on an automaker’s bottom line and undermine the reasons for running EOL tests in the first place.

1. End-of-line testing is time-consuming and costly

While there will always be expenses associated with ensuring manufacturing quality, the costs of EOL testing in automotive manufacturing tend to be particularly high. These expenditures are incurred from extra staffing to execute and monitor the quality control processes, purchasing specialized equipment to conduct the actual testing, and additional administrative costs.

For example, hot testing, is an EOL test conducted on completed engines that are run on a test bench. The objective of the test is to check that all of the engine’s operating parameters are as they would function in a vehicle. Conducting such a test requires specialized manufacturing analysis tools and dedicated equipment that needs to be rigged and derigged – all of which take time and cost money.

Automotive End-Of-Line (EOL) Testing

2. End-of-line testing can diminish efficiency in a manufacturing plant

In an ideal world, maximum productivity for a plant would mean 100% of all parts produced met specifications and passed all quality evaluations. This hypothetical plant would be operating at the highest level of efficiency, where inputs equalled outputs. Optimizing throughput efficiency is an important goal for any manufacturing plant and could mean the difference between a viable operation or a closed plant.

When we are evaluating the effects of EOL testing on efficiency, we must look at the larger  impact of what producing lower quality parts can have on downstream costs, like warranty issues and recalls. If the sole objective of EOL testing is to minimize the risks of warranty issues, then the test only needs to cost less than what the warranty issues would, to make the test worthwhile. The problem with this scenario is that it doesn’t take into account all the other potential outcomes of EOL testing.

For example, when an EOL test identifies a defective unit, the manufacturer faces a choice of either reworking, scrapping, or testing the part again and hoping for better results. With the first two options the result is decreased efficiency, as the end of the line is the costliest point in production at which a manufacturer can scrap or rework a part or assembly. The third option is not that uncommon, but it introduces even more uncertainty into the quality control mix, not to mention production delays and increased costs.

3. End-of-line testing does not prevent failures

Even when taken together, one might think that the cost of EOL testing and the potential for reduced efficiencies are outweighed by the value of having a final check that minimizes the risk of shipping defective products. However, end-of-line testing is not the only way to minimize such risk, and the fact that it does not prevent failures can be reason enough to seek alternative methods.

In an AIAG & Deloitte Report, OEMs and Tier-1 suppliers in the automotive industry ranked problem solving as one of the most critical issues impacting quality. Additionally, both parties cited the lack of root cause analysis as one of the main reasons for this issue. While EOL testing can point to the existence of a problem, it cannot explain why it occurred or how to fix it. In other words, EOL testing may identify defective parts, but it cannot improve first time yield on its own.

Many manufacturers in the automotive industry take a reactive approach to quality control analytics. Take the hot test example, where an engine is run and then classified as either having passed or failed, based on how it performed. This “wait and see” method can be costly, especially for those manufacturers who build complex components that are expensive to process. Rarely is it cost-efficient to scrap a failed engine, and it is usually quite challenging to rework. Even if only a few engines are scrapped or reworked, testing every unit that comes off the line slows down the entire quality control process because the tests take so long to run.

What if there was an automotive quality testing solution that could deliver insights into why these engines are failing the EOL test in the first place and could pinpoint at which stage of production the problem presented itself?

Identifying quality issues before parts reach the end of the line

In essence, manufacturers in the automotive industry expect some components to fail, and then rely on EOL testing to catch these failures. But there are questions that engineers need to ask that cannot be answered at the end of the line, including:

  • Which operations are causing failures?
  • Which signals from the operations are relevant?
  • Where is the best place to intervene to avoid potential future failures?

There are quality control programs for manufacturing that can address quality issues throughout the manufacturing process to avoid having to rely on EOL testing alone. By supplementing EOL testing with manufacturing analytics solutions empowered with machine learning, automakers and Tier-1 suppliers can reduce their reliance on EOL testing as a quality control for every unit they manufacture. They can accurately identify which units would pass quality control and have them bypass the EOL test altogether, while units identified to fail could be scrapped or reworked immediately, without having to wait for EOL confirmation – saving time and effort.

Preventing quality issues before the end of the line

End-of-line testing is still used because most automotive assemblies are complex, with many potential points of failure. With an EOL test, parts pass if they fall within spec, or fail if they don’t. But, if a manufacturer collects information on what went into making any given part, they can use manufacturing data analysis tools to predict how likely it is that a component will pass or fail an EOL test. This eliminates the need to test every part coming off the line.

Imagine a production line with three operations, namely operation one, operation two, and operation three. After a unit comes off the line, it gets tested on how well it performs. A few of those units will fail the EOL test. Because the manufacturer knows that some parts will fail, it has no choice but to test every assembled unit to ensure inevitable defects are caught. This creates inefficiencies on the line.

If the manufacturer were to introduce a predictive quality analytics solution, an EOL test would only need to be conducted when there is reason to believe that a part is defective, or when it’s likely that the EOL test will identify a missed defect. Empowered with technology like machine learning and AI, a predictive quality management solution can analyze data across multiple systems to quickly recognize signals that fall outside of normal limits, that are known to lead to quality errors.

The reason that a part is flagged as potentially defective could be an anomaly in operation two, or signals received are within current specifications individually but are a little too high or low across all three operations. Different operations impact the product in different ways, so the specific indicators of a defect vary from line to line as the series of operations changes.

If a manufacturer can analyze information from all operations using manufacturing analytics software, they can predict which parts are likely to pass or fail an EOL test and only test parts that have a high likelihood of failing. Beyond eliminating the need to test each part coming off the line, collecting data from all upstream operations can also help engineers to identify where problems occur. This provides context when a defect is detected in EOL testing, enabling engineers to locate and understand the root issue.

Benefits to reduced EOL testing

Detecting potential quality issues in a part while it is still on the production line can help manufacturers reduce the need for EOL tests, which would benefit them in a number of ways, including:

1. Minimizing warranty claims

In 2019 automakers paid out $45.9 billion on warranty claims, which equates to approximately 2.4% of their production revenue. Manufacturers in the automotive industry lose hundreds of millions of dollars to escaped warranty units every year. By implementing an integrated manufacturing data analytics platform they can ensure that fewer defects slip through the cracks.

Since machine learning models can map the underlying structure of data and analyze the relationships between signals coming off a line, they can detect issues that other quality control programs for manufacturing miss. For example, machine learning is predictive and can improve anomaly detection. Instead of evaluating signals in isolation, variations across many signals are correlated to predict failures. This enables manufacturers to detect anomalous behavior, even when individual signals fall within a manufacturer’s current test specifications.

2. Lowering operating costs

Usually, a manufacturer’s production schedule is designed with lost time and money in mind. This is due to the inevitable retesting, scrapping, or reworking of parts, as a result of testing every single unit coming off the line. If every assembly is being tested, a full-time person must be assigned to the testing station. This individual needs to maintain a high level of expertise that requires ongoing training to ensure up-to-date skills, especially when producing complex products that require sophisticated testing.

Maintenance and unforeseen downtime, due to unexpected calibrations or maintenance overhauls associated with EOL testing, can also slow down production. By supplementing EOL testing with predictive quality analytics, manufacturers can free up human resources and reallocate them to areas of the business that drive profitability.

3. Improving throughput and first time through yield (FTT)

End-of-line testing creates a bottleneck. When every unit is tested, all upstream operations on the line have to be stalled. But, if fewer units need to undergo EOL testing, less time is required for the entire end-to-end assembly process. Manufacturers can increase throughput and FTT by supplementing EOL testing with predictive quality analytics.

Furthermore, predictive analytics can help increase the overall quality of production, resulting in fewer EOL test failures. Since manufacturers can forecast when a unit is likely to pass, high-quality units can bypass the end-of-line test, reducing the number of false positives.

Although there are certainly benefits to reducing EOL testing, it shouldn’t be removed from the manufacturing process altogether. It makes more sense to cut the number of components that need to be filtered through EOL tests, while also improving operations on the line to make better quality parts in general.

The future of automotive quality testing

Manufacturers can now reduce their reliance on EOL testing and improve their product quality and throughput efficiency with advanced technology. An ML-powered solution, like Acerta’s LinePulse, can:

  • Collect data from multiple systems and sensors on the production line and analyze them in real time to provide actionable insights.
  • Enhance the understanding of data across the manufacturing line to accelerate and improve first time yield.
  • Decrease the need to put units through EOL testing, by predicting outcomes before the testing is conducted.

Quality is key, so why leave it to the end of the line? With real-time visibility into production and quality data, you can build better parts and assemblies more efficiently, all of the time. Check our eBook “End-of-line testing is good… but predictive quality analytics is better” to learn more.

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