An Introduction to Automotive End-of-Line Testing
End-of-line (EOL) testing is a crucial step in the automotive manufacturing process.
In order to ensure the quality of complex products, such as axles, engines or transmissions, automakers need a method for separating defective or out-of-spec units from those shipped to customers. Methods differ, depending on the type of product and the relevant features of interest, but they include hydraulic testing, noise, vibration, and harshness (NVH) testing and roll brake testing.
In the case of geared assemblies, for example, the noise those assemblies generate can indicate manufacturing defects which can lead to both in-passenger comfort issues or even failures which risk passenger safety. Using NVH testing on geared assemblies at the end of the line can identify the early warning signals indicative of such problems.
Why Conduct EOL Testing?
End-of-line testing is a necessary part of the manufacturing process, since it is the last checkpoint before a product leaves the factory.
Millions of auto parts are manufactured every day, representing hundreds of millions of dollars of potential revenue. EOL testing ensures those parts have been manufactured to the appropriate specifications, as well as giving engineers an indication of when to start troubleshooting to reduce failure rates.
EOL testing also tends to benefit companies as a whole, since fewer defects mean fewer warranty claims or recalls in the future.
End-of-Line Testing - Limitations
Despite the obvious benefits of end-of-line testing, like any quality process, it’s also limited in several important ways. Under the wrong circumstances, these limitations can negatively impact an automaker’s bottom line and undermine the very reasons for having EOL testing in the first place.
Three major issues with end-of-line testing are cost, accrued inefficiency, and lack of impact.
1. Cost of EOL Testing
While there are always costs associated with manufacturing quality, the costs of end of line testing in automotive manufacturing tend to be particularly high.
Consider hot testing, in which a completed engine is run on a test bench with the aim of checking all the engine’s operating parameters just as they would function in an actual vehicle. Obviously, conducting such tests requires dedicated equipment and between rigging, derigging and running the test itself, the entire process can take anywhere from 18-45 minutes.
2. End-of-Line Testing - Accrued Inefficiency
The obvious response to the preceding issue is that even if end-of-line testing isn’t perfect, no quality process is and it’s better than nothing. If the point of EOL testing is to minimize the risks of warranty issues, testing only needs to cost less than those issues to be worthwhile. The problem with this response is that it doesn’t consider all the potential outcomes of end-of-line testing.
When an end-of-line test correctly identifies a defective unit, what then?
The manufacturer is faced with a choice between re-working the part, scrapping it, or in some cases just testing it again and hoping for better results. Though not uncommon, the last option should give any quality engineer pause, since it inherently introduces more uncertainty into their measurements. For the first two options, the result is decreased efficiency, since the end of production is the costliest point at which a manufacturer can scrap or rework an assembly.
Simply put: the sooner one catches a problem, the less costly it is to fix it.
3. Lack of Impact in EOL Testing
Even when taken together, one might think that the cost of EOL and the potential for accrued inefficiencies are outweighed by the value of having a final check at the end of the line which minimizes the risk of shipping defective products. However, end-of-line testing is not the only way to minimize such risk, and its third major limitation can be reason enough on its own to seek alternative methods.
In AIAG & Deloitte’s Quality 2020 Report, automotive OEMs and suppliers both ranked Problem Solving as one of the most critical issues impacting quality. In addition, both cited Lack of Root Cause Analysis as one of the main reasons for this issue.
While end-of-line testing can point to the existence of a problem, it cannot explain why the problem is occurring or how to fix it. To put it another way: end-of-line testing may catch defective parts, but it cannot improve first time yield on its own.
Evaluating End-of-Line Testing
The issues with end-of-line testing cited above are significant, but they are not inevitable. By supplementing EOL testing with advanced analytics and machine learning, automakers can reduce their reliance on the end-of-line test as a necessary step in the production of every single unit they manufacture.
Think of this way: Most assemblies will pass their EOL test, so if machine learning can accurately predict which units will definitely pass, those assemblies can safely bypass the test and thereby save quality engineers time and effort. On the flip side, if machine learning can accurately predict which units will definitely fail the end-of-line test, those assemblies can be scrapped or reworked immediately without having to wait for confirmation. Only the units for which the predicted result is uncertain need to actually undergo the EOL test procedure, and that’s a much smaller group.
Check our white paper on improving efficiency by eliminating EOL to learn more.
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