Development of a Digital Twin of a Flexible Manufacturing System for Assisted Learning
David, Joe Samuel
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Learning Factories provide a propitious learning environment for nurturing production related competencies. However, several problems continue to plague their widespread adoption. Further, assessment of attained competencies continue to remain a concern. This study proposes the use of digital twins as an alternative learning platform for production engineering courses. It is proposed that in the context of manufacturing pedagogy, digital twins of manufacturing processes can play a signiﬁcant role in delivering efﬁcacious learning experiences. The high-ﬁdelity replication of the physical system aids with reﬂective observation of the entailed processes in the greatest possible detail, fostering concrete learning experiences. An iterative research methodology towards modelling a pedagogic digital twin is undertaken to build a learning environment that is characterized by ontologies that model learning objectives, learning outcomes and assessment of the said outcomes. This environment facilitates automated assessment of the learner via ontological reasoning mechanisms. The underlying schema takes into account the learner’s proﬁle and focuses on competency attainment through reasoning of behavioural assessment of aligned learning outcomes. The thesis presents also a case study that demonstrates how the learner’s competency level may be evaluated and compared with other learners thus warranting its use a learning tool that proves beneficial in an academic setting.