Development of a Digital Twin of a Flexible Manufacturing System for Assisted Learning
David, Joe Samuel (2018)
2018
Automation Engineering
Teknisten tieteiden tiedekunta - Faculty of Engineering Sciences
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Hyväksymispäivämäärä
2018-12-05
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tty-201811222741
https://urn.fi/URN:NBN:fi:tty-201811222741
Tiivistelmä
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.
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.