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The justification of accelerated endurance testing of heavy duty trucks and buses / by James Roger Lackore, Jr.

by Lackore, James Roger Jr.

Reliability (Engineering)

Risk assessment -- Mathematical models

1 v. (various pagings) : ill. ; 29 cm.

Committee members: Dr. Bruce Thompson, James Spindler.

Introduction -- Background -- Reasons to test -- Risk assessment theory -- Predicting risk avoidance by assessing the design -- Predicting risk avoidance based on historical data -- Using risk values in test justification -- A general guideline for determining when to test -- Opportunities for further study -- Conclusion -- Disclaimer -- Appendix A- Risk estimation matrix example B- Confidence probabilities using a beta distribution C- Product recall data for select heavy truck manufacturers D- Testing benefit calculations for varying product life-cycles.

The purpose of this study is to create an understanding of when and how endurance testing can be financially beneficial to manufacturers in the heavy duty truck industry. The author has been employed by two truck manufacturers of medium size over the past eleven years, and has found it difficult to financially justify such testing.

This paper begins by reviewing the theory of accelerated endurance testing. Most of the major truck manufacturers use endurance testing routinely, and several operate their own facilities. The benefit of this study is therefore directed primarily at the small to medium sized manufacturers, as well as body builders. Since companies in this size range wil typically contract with established facilities, a description of those facilities capable of testing heavy duty vehicles and willing to contract with private corporations is included.

As an aid to manufacturers who cannot justify full vehicle testing, a section is included which describes alternatives such as finite element analysis and shaker testing. These methods can be considered in addition to endurance testing, but will never provide the real-world simulation possible with a well-conceived accelerated endurance test.

The reasons for considering endurance testing include financial payback from reductions in warranty, recall, and rework expenses. It is also possible that the increased product reliability produced by an endurance tested vehicle can reduce the risk of product liability litigation. Further benefits accrue from the use of test data in advertising and the creation of a positive company image. Finally, it is useful in developing a basis for the safety certifications required by government regulations.

To justify the cost of testing, corporate management must be convinced that the project will produce a financial payback with an atttractive rate of return, and the payback must be measurable. Since the cost of warranty and recall expense can be tracked by product model fairly easily, the reduction of these expenses can be used as this measure. The difficulty in this approach is created by the need for risk assessment during the early stage of product design. The combined costs of quality must be estimated for the product both with and without the endurance test. The real benefits of the test will not be apparent until after the money is spent, and any costs of warranty that would have been caused by design errors, and were revealed by the test, have come to light. The process of estimating these costs beforehand is referred to as risk assessment, and an overview of this theory is presented as well.

A talented and fortunate engineer might occasionally design a product design a product that works perfectly the first time. SInce the justification of a test program involves assessing the risk that the new design will not work perfectly the first time, the justification must involve estimation - and estimation is always suspect. To improve the credibility of the estimation process, this paper presents a method of organizing the estimations of risk and translating them into a monetary value. The detailed nature of this method should increase the accuracy of the estimation at the same time that it lends credibility to the estimator.

This proposed approach involves dissecting the elements of a new design into the smallest practical parts. Each bracket, component, and system must be considered individually, and then labeled with the degree of confidence it its reliability. The cost associated with the failure of each detail, along with the probability that an endurance test would reveal the failure, can then be combined to create a bottom-line monetary value associated with the test.

A second approach to justification can be obtained by considering past projects, and estimating whether the known costs associated with actual design flaws could have been avoided if the product had been tested. Research into this method involved extensive searches through the product recall files at the National Highway Traffic Safety Administration (NHTSA) library in Washington, D.C. Recall campaign data for six truck manufactureres is presented in Appendix C. This data includes all the particulars surrounding the extent of each recall as well as summary descriptions of each failure, and the corrective action taken. For the vehicle design engineer, this section makes for informative reading aside from its usefulness in test justification.

The original intention of this thesis was to develop a simple formula by which any heavy duty truck manufacturer could determine the probability of whether endurance testing would prove to be financially beneficial. After much study and consideration, it became evident that the factors involved are more complex than is practical for a simple formula to define. These complexities include, among others, model mix, degree of product customization, engineering department sophistication, customer expectations, and details of the company's warranty policy.

Although the construction of a formula seems impractical, a general trend can still be established. Risk assessment modeling using broad assumptions indicates that it is unlikely that small, or diverse, manufacturers (less than 100 units per model year) will ever realize testing paybacks, while larger more stndardized manufactureres (over 400 units per mode per year) probably always will. Analysis of the historical data supports this conclusion as well. This leaves most medium sized producers caught in the gray area where each project must be considered on its own merit.

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