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Three-Dimensional Tolerance Stack Utilizing CETOL
Technical Paper
2020-28-0495
ISSN: 0148-7191, e-ISSN: 2688-3627
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Abstract
When we see a tangible creation and our inquisitive nature crave to have touch and feel of the same, many questions hover our mind like what it is, what does it do and how it was made. All man-made creations which first get conceived in human mind pass through several processes and evaluation in order to be eventually ready for usage and application. The evaluation and reviews are required so that the final product which is a part of the creation, functions the same as it was intended to do. The analysis which could be limited to part level or system level gives us a foresight into any possibility of pre-production or pre-release failures which might be barriers in the success of the product. Tolerance stack up is one of such analysis approaches used today in product design to understand how imperfections in parts as they are manufactured, and later assembled as products, affect its capability to meet customer requirements. Tolerance stack helps engineers to study any accumulated variation in parts in a product assembly. It is a way of understanding how sources of variation in part dimensions and assembly constraints propagate across parts and assemblies, and how this variation affects meeting the design requirements within process capability of manufacturing organizations. We get these dimensions and tolerances from the engineering drawings. If we consider the most common techniques used for tolerance stack analysis like Worst Case method (WC) and RSS method (Root-Sum-Squared), they have limitations in attending complex architectures, allowing flexibility to designers and cost. CETOL 6 Sigma utilizes a method called Method of System Moments which disregards these limitations. An example of this approach utilizing CETOL 6σ software application will be presented in this paper.
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Citation
Sarma, A. and K, Y., "Three-Dimensional Tolerance Stack Utilizing CETOL," SAE Technical Paper 2020-28-0495, 2020, https://doi.org/10.4271/2020-28-0495.Data Sets - Support Documents
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- https://www.sigmetrix.com