## 1 Introduction  
The design of industrial products exploits novel approaches for material selection, manufacturing and assembly processes, simulation, and life cycle management, however, the design is still heavily based on the experience of designers. Knowledge transfer from experts to young designers is pursued with conservative approaches and unstructured communication, with many limitations even within the same company. On the one hand, the lack of formal design knowledge limits access to prior experience and knowledge by designers. On the other hand, knowledge management is critical in industry, since it is highly expensive nevertheless ephemeral due to staff turnover and the increasing use of outsourcing. Therefore, knowledge management methods have been developed by researchers to better exploit individual and collective knowledge [1; 2; 3; 4; 5]. Engineering design is described in the systematic approach of [PERSON] and [PERSON] [6] as a sequence of the following four phases: (1) task Clarification, (2) conceptual design, (3) embodiment design, and (4) detail design. Following a top-down approach, the system is broken down into its compositional subsystems and, similarly, the knowledge of the whole design problem is refined into more detailed subproblems. Therefore, in the design archetype (DA) implementation, the design knowledge management approach follows the same systematic structure to capture, store, and reuse knowledge models of products, processes, and manufacturing resources. In order to save costs and time, most industrial products are designed by adapting existing ones to meet new requirements [7; 8; 9]. In the embodiment design phase of automotive subsystems, for instance, designers have to reuse the already available computer-aided design (CAD) models, by simply adapting the parameters of their features to new requirements [7]. As a result, the same parametric models are used in different product variants, with the collateral effect of increasing the complexity of the models themselves in the case of specific adjustments. Therefore, design knowledge management needs to specifically consider the variant design process. In variant design, not all the design phases have to be carried out again. The constraints of costs, carry over, reuse, and supply chain policies force the implementation of specific design solutions. In variant design, the designers have to enter the top-down design process in the embodiment or even the detail design phases. Moreover, the adaptation to existing subsystems can cause many bottom-up issues, so that problems of optimization are likely to arise. Designers often are required to deal with products with similar structures, with only minor differences related to specific requirements [2; 10; 11; 12]. Therefore, most knowledge-based engineering (KBE) applications for mechanical design follow three main approaches, i.e., the automation of complex design tasks, the reuse of design solutions, and knowledge-based tools [13; 14; 15]. The feature design can be automated, also taking into account technology specific data. Many case studies can be retrieved from the literature such as, describing a tool for designing hot forging dies [16], a tool for developing virtual prototypes of multifunctional agricultural machines based on real component behavior [17], a web-based tool for identifying the undercut feature of models for injection molding and for the automatic design of parting surface [18], a configuration tool for the generation of conceptual train geometry for design automation and reuse [19], an agent-based model representing product development as the learning and application of knowledge coming from product and development process data [20], an ontology representing the information and design knowledge in product design process [21], or a library of components for the automatic definition of the finite element model of an automotive structure [22].