Conceptual Ontology-Based Context Representation for Human and Two Heterogeneous Cobots Collaborative Mold Assembly

Plastic injection molds are assembled manually because of the high variety and low volume operation that require frequent change and comprehensive mold knowledge. The lack of skilled workers and ergonomic problems in the mold assembly requires a certain level of automation to improve the working condition and operating performance. However, the knowledge within mold assembly application is not systematically organized to automate the operation. The use of two non-identical collaborative robots is considered to assist and collaborate with a human worker during the mold assembly operation. Data containing information on components in the collaborative assembly must be collected and represented in a form that can be interpreted and understandable by all resources. Besides, relations of entities within a specific context also must be included to extract correct and useful information for every decision-making step. This study aims to provide a systematic ontology model to acquire and represent knowledge of mold assembly operation with human-robot collaboration implementation that enables expansion, learning, and generalization of created ontologies in future practical applications. A simple trial use of created ontology for positioning parts into regions in the workspace is included. This proposed conceptual context modeling acts as a stepping stone to developing a context-aware system for human-robot collaborative mold assembly operations using multiple cobots.