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When developing new products, brand designers must analyse related products, which is a complicated and time-consuming process. Modern product design often requires complex engineering processes; product development requires extensive knowledge but there is also a demand for shorter product design cycles. Therefore, we propose a method based on extension theory and the analytic hierarchy process for identifying product-related knowledge, to aid the development of new products. First, based on our understanding of extenics, matter-element and relational meta-models of product form, function, and structure are established. Then, we define different primitives of brand identity. Finally, using an "extensional analytic hierarchy process" (EAHP), a hierarchy is established and the weights of different primitives are calculated. Various combinations of primitives are used to facilitate knowledge transfer for computer-aided intelligent design. Design data for multiple cases are analysed to verify the feasibility and effectiveness of the method. The method was verified in a physiological signal experiment, and the results show that the method can effectively accumulate product knowledge. Rapid data mining is important for market competitiveness.

Additive manufacturing technologies are positioned to provide an unprecedented innovative transformation in how products are designed and manufactured. Due to differences in the technical specifications of AM technologies, the final fabricated parts can vary significantly from the original CAD models, therefore raising issues regarding accuracy, surface finish, robustness, mechanical properties, functional and geometrical constraints. Various researchers have studied the correlation between AM technologies and design rules. In this work we propose a novel approach to assessing the capability of a 3D model to be printed successfully (a.k.a printability) on a specific AM machine. This is utilized by taking into consideration the model mesh complexity and certain part characteristics. A printability score is derived for a model in reference to a specific 3D printing technology, expressing the probability of obtaining a robust and accurate end result for 3D printing on a specific AM machine. The printability score can be used either to determine which 3D technology is more suitable for manufacturing a specific model or as a guide to redesign the model to ensure printability. We verify this framework by conducting 3D printing experiments for benchmark models which are printed on three AM machines employing different technologies: Fused Deposition Modeling (FDM), Binder Jetting (3DP), and Material Jetting (Polyjet).

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