Automated joining element design for high product variety in the manufacturing industry

Product variety and the manufacturing complexity that it induces are continuously increasing. This poses a challenge in the product development process and, consequently, the design of joints. Joining element design is an ambiguous manual task with limited automation solutions. Thus, it can lead to long, iterative, error-prone development trajectories that may result in costly rework. Hence, automation solutions for joining element design must be intelligent. However, simply developing some intelligent automation of joining element design is insufficient. Modular design, through the approaches of modularization and commonalization, enables manufacturers to cope with the complexity induced by product variety. Unfortunately, modular design approaches have not yet considered joining elements. Hence, this dissertation study answers the following research question: How can joining element design be automated for high-variety products?

The study presents a framework for automating joining element design. The framework first describes design problems for automating the various aspects of joining element design, including modular design. These design problems can guide designers in practice. Moreover, they enable organising state-of-the-art design methodologies for each design problem. Furthermore, the design problems help to identify, evaluate and assess the application of novel artificial intelligence techniques. The study conceptualizes several techniques in automating joining element design.

Furthermore, this study validated several proposed concepts, including joining technology prediction with decision trees, joining location prediction with a random optimization algorithm, and several neural network implementations. Moreover, this study explored the commonalization of joining elements. In short, the validation experiments produced promising findings, which can be used in the automation of joining element design in industries with high product variety. However, further research is required to optimize and implement this study’s findings into a productive environment.