With the arrival of the big data era, a lot of valuable data have been generated in the entire product life cycle. The gathered product data contain a lot of design knowledge, which brings new opportunities to enhance the production efficiency and product competitiveness. Data-driven product design is an effective and popular design method, which can provide sufficient support for designers to make smart decisions. This article focuses on a comprehensive review of the existing research in data-driven product design. Based on the product design process, this article summarizes the data-driven design methods into the following aspects: customer requirement analysis, conceptual design, detailed design, and design knowledge support tools. In the customer requirement analysis stage, through data mining and transformation methods, customer requirements are predicted and then mapped to obtain accurate requirement expressions for aiding designers to explore the design space. In the conceptual design stage, the intelligent algorithms and data warehouse technologies are discussed in detail for function reasoning and scheme decision-making to achieve the iterative mapping from customer space to solution space. In the detailed design stage, data modeling languages and methods are introduced to support the simulation verification of the design process. For the design knowledge support tools, the methods of extracting knowledge from product data are discussed in detail, and the realization of computer-aided conceptual design is assisted through the development of knowledge-oriented design tools. Finally, this article summarizes the key points of data-driven product design research and provides an outlook for future research directions.
Data-driven product design toward intelligent manufacturing: A review
