Development of a Casting Process Database for Rapid Process Design Using Case-Based Reasoning

Abstract

Casting process design is crucial in manufacturing; however, traditional design workflows are time-consuming and seriously reliant on the experience and expertise of designers. To overcome these challenges, database technology has emerged as a promising solution to optimize the design process and enhance efficiency. However, conventional database storing process cases often lack adequate parametric information, limiting their ability to support intelligent and automated design. Thus, this study has developed a casting process database based on parametric case modeling, enabling the rapid design of casting processes for new parts using case-based reasoning (CBR). The database framework was designed to organize process cases into four distinct information modules, allowing for structured and separated storage. Data unit associations were established between these modules to ensure the completeness and scalability of process information. The database stores sufficient parametric information to describe key process characteristics and multidimensional elements. This includes the detailed structural parameters of parts, calculated based on the accurate analysis of their structural features. A process cost estimation model was incorporated to calculate and record direct process costs, enabling the effective comparison and ranking of various process plans for the same part. Additionally, the parametric model of the gating system is stored to support the transfer of processes between similar parts. The functionality and effectiveness of the proposed database were visually validated through a case study on the process design of an actual casting part. The results indicate that the database significantly improves efficiency and ensures the accuracy of CBR-based process design while optimizing the reuse of design knowledge and expertise. The developed database achieved a 90% reduction in design time compared to conventional methods.