Adhesive selection via an interactive, user-friendly system based on Symbolic AI

Adhesive joints are increasingly used in industry for a wide variety of applications because their favorable characteristics such as high strength-to-weight ratio, design flexibility, limited stress concentrations, planar force transfer, good damage tolerance and fatigue resistance. Selection proper adhesive particular application depends on many often conflicting product manufacturing process requirements is therefore cumbersome task. Traditionally, selection done by an expert based experience, prior knowledge trial error. Adequate tooling to support bonding experts the processes lacking, generally yielding suboptimal results. This research presents interactive selector tool, aimed at supporting joints. Knowledge gluing its associated constraints captured from represented formally Base (KB). The inside KB then processed using Imperative Declarative Programming (IDP) reasoning engine order selection. Through intelligible, interactive, interface (such use conditions, materials, requirements, etc.) entered, which IDP system reduces search space potential adhesives. tool has been tested industrially relevant case had be selected composite panel steel frame automotive application. Using was able select appropriate 10 times faster than without.