Establishing a knowledge graph-based recommendation system for product family reconfiguration and redesign for a robotics-based use case - Malhotra, Parth (Nanyang Technological University, 2024)
This research investigates the integration of modular product design with generative design to enhance the reconfiguration and redesign of robotic arms within various industrial applications. By leveraging a knowledge graph-based system, this study aims to facilitate rapid and precise modifications of robotic arms to meet specific customer needs without having to create new products from the ground-up. A case study in the Electronics Industry is created utilizing the Openmanipulator-X robotic arm to illustrate the implementation of this hybrid system. The methodology adopted involves constructing a comprehensive knowledge graph that encapsulates customer preferences, component relationships, and engineering constraints, allowing for efficient recommendation, and prototyping of reconfigured robotic arms with optimized generative designs for unmatched modules from the knowledge-graph based reconfiguration approach. This approach promises to minimize production costs and time while maximizing the adaptability and efficacy of robotic manufacturing systems.