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Functional testing of prototypes is a critical step in the development of many of today’s products. Results of functional tests allow for verification of proper performance before a product is introduced into the market. The advent of rapid prototyping technologies offers engineers the potential to dramatically reduce the prototype-test-verify cycle and get products to market quickly. However, dimensional and material property limitations of rapid prototypes often prevent them from being used for functional testing without the use of similitude methods to correlate measured prototype behavior with predicted product behavior. The traditional similarity method (TSM), which is based on the Buckingham Π theorem, requires that the dimensionless parameters of the prototype and product systems be identical in order to correlate their states and accurately predict product performance. The requirement of identical dimensionless parameters which is inherent in the TSM is often impossible to realize with the limited properties available from rapid prototyping technologies. In order to overcome this limitation, an empirical similarity method (ESM) has been developed. The general concept of the ESM is introduced along with an implementation procedure. Numerical and experimental examples are presented which demonstrate the feasibility and industrial impact of the ESM in the context of product design.
Contemporary industries are devoting increasing attention to the product development process, due to tight market shares and the abridged product life cycle. Reliable scaled product testing with rapid prototypes has the potential to improve these processes by replacing traditional costly and time-consuming product tests. In this context, rapid prototypes provide visual, ergonomic, and functional information with minimal time delay. Among the information classes, reliable functional information is least realized because of several features of rapid prototypes: (1) limited material choices and part size; (2) distinct material structure; (3) restrictive loading conditions; and (4) state-dependent material properties. To develop reliable functional tests, an improved similarity method is needed to overcome these limitations. The traditional similarity method, based on a Buckingham П approach, is commonly applied to perform scaled tests. In contrast to this method, wherein the state transformation between two similar systems is derived from dimensional vectors, we present a new similarity method that empirically derives the transformation from a geometrically simple specimen pair. The primary advantage of the new method over the traditional method is the capability to relate highly distorted systems. In this paper, the concept and theoretical framework of the novel similarity method are introduced, and two numerical examples demonstrate the new method.
During product development, testing of models and prototypes offers significant advantages over direct product testing, including easier, cheaper, and faster fabrication. However, two issues prevent effective functional testing with prototypes: prediction accuracy and confidence in scale testing results. The traditional similarity method, which is based on dimensional analysis, is commonly applied to perform scale testing. However, the method may not provide accurate scale testing results, especially when available model materials are different from the final product materials. The authors have developed a new empirical similarity method, wherein specimen pairs and partial knowledge of systems are systematically utilized, to improve the prediction accuracy. In this paper we describe the construction of error measures to utilize scale testing results with confidence. In practice, scale testing results are validated based on experiences with previous testing results. This approach to predicting accuracy is difficult to formalize. We develop and simulate a systematic two-level error estimation procedure. Realistic numerical examples demonstrate the feasibility of the approach.
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