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Non-expert practice application of an AI vision systems in design engineering projects.

Garland, N., Wade, R. and Palmer, S., 2023. Non-expert practice application of an AI vision systems in design engineering projects. In: E&PDE 2023: 25th International Conference on Engineering & Product Design Education, 07-08 Sept 2023, Barcelona, Spain, 1-6.

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EPDE23 Final Paper 1105- nigel garland.pdf - Published Version
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Design projects units for BSc (Hons) Design Engineering students at Bournemouth University integrate and apply knowledge from a range of taught units together with self-directed learning and towards solving design problems. Recently, level 6 (FHEQ) project students have proposed and designed solutions that require AI vision-systems. These projects presented a problem for supervision, with limited, or no expertise in the technology or available equipment; students therefore treated these subsystems as a “black-box” exercise. To address these issues a set of technical requirements were compiled, a range of AI technology solutions were identified before selecting the Nvidia Jetson Nano. From the literature, a stream-lined practical program was developed to introduce the technology to level 5 and level 6 project students as part of their design education. This provided hands on experience through familiarization with the interface and the use of pretrained models before students re-trained networks with their own datasets. Level 5 students utilised the technology to develop a scratch detection machine for sorting damaged components. Level 6 students were provided with the opportunity to integrate the technology into projects where appropriate and two students did so; one developed a device to identify people trapped in buildings after an earthquake, the second developed a device for monitoring chili-plants when grown under polytunnels. Developing and delivering the introductory programme as a non-expert learning pathway has enhanced the student experience within design education, provided a simple workflow that students can utilise and build upon, and led to successful student outcomes.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:AI; projects; machine learning
Group:Faculty of Science & Technology
ID Code:39679
Deposited By: Symplectic RT2
Deposited On:09 Apr 2024 11:51
Last Modified:09 Apr 2024 11:51


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