Abstract As the popularity of adhesive joints in industry increases, so does the need for tools to support the process of selecting a suitable adhesive. While some such tools already exist, they are either too limited in scope or offer too little flexibility in use. This work presents a more advanced tool, that was developed together with a team of adhesive experts. We first extract the experts’ knowledge about this domain and formalize it in a Knowledge Base (KB). The IDP-Z3 reasoning system can then be used to derive the necessary functionality from this KB. Together with a user-friendly interactive interface, this creates an easy-to-use tool capable of assisting the adhesive experts. To validate our approach, we performed user testing in the form of qualitative interviews. The experts are very positive about the tool, stating that, among others, it will help save time and find more suitable adhesives.

Adhesive bonding is a joining process used in several industries such as aerospace, automotive, civil construction and manufacturing. Traditionally, the optimization of the parameters for this process is performed by adhesive experts via trial and error which is expensive and time-consuming. Therefore having a process model for optimization purposes is of great interest. In this study, we develop such process model which includes cost, visual quality and joint strength properties for Polyphenylene sulfide bonding use-case. We adopt analytical modeling approaches for those process properties that do not require extensive system knowledge and are not effected by large number of process parameters, namely cost and visual quality. Additionally, we use data-driven genetic programming approach to model the more nonlinear process property, meaning joint strength of the bond. Consequently, we employ a hybrid approach by combining available knowledge and experimental data. The process model can then be implemented for process optimization or to create a digital twin which predicts if the product quality is in scope.