A reliability technique for parametric accelerated life testing (ALT) is suggested to increase the reliability of mechanical parts that have failed in the field. It consists of: (1) a parametric ALT plan, (2) a load review, (3) a tailor-made set of action plan parametric ALTs, and (4) an assessment of the final designs to ensure that the design specifications are met. This parametric ALT can allow an engineer to replicate the fractured or failed parts in a repeated loaded product and correct the defective designs. The helix upper dispenser of a field-fractured refrigerator icemaker has been studied as a test case. Using a load analysis , we found that the fracture of the helix upper dispenser was due to repeated loads and a flawed design with a distance of 2 mm between the blade dispenser and the upper helix dispenser. We also proposed a reliability approach that includes: (1) a parametric ALT plan, (2) a load review, (3) a customised sequence of parametric ALTs with action plans, and (4) an assessment of the final design specifications of the mechanical part to ensure that the requirements are fulfilled, in order to increase the reliability of a mechanical part that fails in the field. The fracture in the helix upper dispenser was repeated during the first and second ALTs. The modes and mechanisms of failure observed were identical to those of the failed field sample. The configuration of the helix upper dispenser has been updated as an action plan, by removing the 2 mm gap and inserting implemented ribs. No issues existed in the third ALT. The reliability of the helix upper dispenser was assured after three rounds of parametric ALTs as a 10-year life with an accumulated failure rate of 1 percent. For other mechanical systems such as cars, aircraft, construction machinery, washing machines, vacuum cleaners and civil structures such as bridges, this reliability design approach should be appropriate.
Addis Ababa Science and Technology University, Addis Ababa, Ethiopia.
Dennis L. O’Neal
Department of Engineering and Computer Science, Baylor University, Waco, TX 76798-7356, USA.
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