Data-driven technologies have found their way into all areas of engineering. In product development they can accelerate the customization to individualized requirements. Therefore, they need a database that exceeds common product data management systems. The creation of this database proves to be challenging because in addition to explicit standards and regulations the product design contains implicit knowledge of product developers. Hence, this paper presents an approach for the semantic integration of the engineering design (SeED). The goal is an automated design of an ontology, which represents the product design in detail.SeED fulfils two tasks. First, the ontology provides a machine-processable representation of the products design, which enables all kind of data-driven technologies. Among other representations, the ontology contains formal logics and semantics. Accordingly, it is a more comprehensible solution for product developers and knowledge engineers. Second, the detailed representation enables discovering of intrinsic knowledge, e.g. design patterns in product generations. Consequently, SeED is a novel approach for efficient semantic integration of the product design.

Mass customization aims to meet individual requirements and, therefore, is one way to attract and retain customers—a key challenge in the design industry. The increase in design automation has offered new opportunities to design customized products at high speed in a way that is cost equivalent to mass production. Design automation is built upon the reuse of product and process knowledge. Ontologies have proven to be a feasible, highly aggregated knowledge representation in engineering design. While product and process knowledge from other lifecycle phases are represented in multiple approaches, the design process of the product as well as the adaption process of product variants is missing, causing breakpoints or additional iterations in design automation. Therefore, suitable knowledge representation tailored to design automation is still missing. Accordingly, this contribution proposes a novel knowledge representation approach to enable design automation for mass customization. Methodically, this novel approach uses semantic enrichment of CAD environments to automatically deduce information about a design task, design rationale, and design process represented by a formal ontology. The integration of the design process significantly differentiates the approach from previous ones. The feasibility of the approach is demonstrated by a bike crank customization process.

Due to increasing market transparency, companies are mainly focusing on development of customized products. In this course, a competitive advantage should be achieved through the automated adaptation of product characteristics. In order to achieve this, development engineers focus primarily on modifying existing designs and refrain from re-running the entire product development process (PDP). However, this approach is primarily limited to the CAD environment. Previous phases of the PDP are not integrated, leading to a lack of traceability and continuity. Media breaks and API conflicts arise which increase costs. In order to make use of this potential, this article shows a concept for integration of all phases of the RFLP approach into a tool for customization of adaptation constructions.

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