An ontology-based modelling and reasoning framework for assembly process selection

Assembly joining process selection is a knowledge-intensive task that needs an efficient tool to capture, represent, reuse, and share knowledge related to various joint requirements. This paper presents an ontology-based knowledge framework for identifying the appropriate assembly joining process to support designers and process planners effectively. A joining process selection (JPS) ontology is developed to represent different core concepts like feature, material, product, joint requirement, and joining process. Semantic Web Rule Language (SWRL) is used for ontology mapping of joining process selection concepts to retrieve the required knowledge for process selection that integrates several instances and knowledge rules. Further, a five-step sequential procedure is established to select the joining process from the CAD model automatically. The proposed approach automatically infers the possible, probable, and most probable joining processes through rule-based reasoning. Based on the evaluation of the ontology, the precision, recall, and F-measure obtained are 89.4%, 85.7%, and 87.5%, respectively. Finally, the efficacy of the ontology is evaluated using industrial case studies from the automotive and aerospace industry.