Kailun had his MSc degree at Cranfield University from 2017-2018 majoring in Engineering & Management of Manufacturing Systems. Before this, he had his BEng degree from Beijing Institute of Technology. During his studying for master degree in Cranfield University, Kailun focused on manufacturing process optimisation and management improvement for manufacturing enterprises. In his individual project of MSc study, he studied the changes to manufacturing process and systems with an aging workforce under the background of an aging population and proposed various suggestions such as more specific job assignment and work schedules should be worked out based on different health conditions and work capabilities of each old employee, more suitable tools and equipment should be designed for aging workers considering their special ergonomic characteristics which are quite different from young employees.
Kailun is currently doing a PhD research on Improvement of through-life degradation assessment for high value components using Artificial Intelligence. Aiming to advance NDT techniques using AI technology through a number of innovations such as smart parameter selection, inspection automation, multiple-scale and multi-resolution inspection and AI-based decision making.
Articles In Journals
- Liu H, Li W, Yang L, Deng K & Zhao Y (2022) Automatic reconstruction of irregular shape defects in pulsed thermography using deep learning neural network, Neural Computing and Applications, 34 (24) 21701-21714.
- Marchante Rodriguez V, Grasso M, Zhao Y, Liu H, Deng K, Roberts A & Appleby-Thomas GJ (2022) Surface damage in woven carbon composite panels under orthogonal and inclined high-velocity impacts, Journal of Composites Science, 6 (10) Article No. 282.
- Zhou J, Du W, Yang L, Deng K, Addepalli S & Zhao Y (2022) Pattern recognition of barely visible impact damage in carbon composites using pulsed thermography, IEEE Transactions on Industrial Informatics, 18 (10) 7252-7261. Dataset/s: 10.17862/cranfield.rd.17135021