Staff Profile

An experienced engineer and academic with a strong focus on machine vision and deep learning for object recognition. Over the years, I have led and collaborated on groundbreaking projects in digital manufacturing, automation, and smart system design. I currently serve as an Assistant Professor in the Mechanical Design and Manufacturing Engineering at Newcastle University, Singapore. I bring technical expertise in developing and deploying innovative solutions in both research and industry contexts, particularly in predictive maintenance, digital twin technology, and IoT applications.

Linkedin

Key Areas of Interest



• Machine Vision Systems for Object Recognition
• Applied Deep Learning and Lightweight Analytics
• Digital Twin Technology for Smart Manufacturing
• Predictive Maintenance and Process Monitoring
• IoT and Cyber-Physical Systems in Manufacturing
• On-Demand Research in Digital Trust in Manufacturing System

Experience Highlights

• Successfully led a project under the PUB Global Innovation Challenge 2021 to automate the identification and enumeration of midge populations using machine vision and AI, addressing critical challenges in environmental monitoring.
• Developed advanced data-driven manufacturing optimization techniques, contributing to improved efficiency and reliability in industry workflows.
• Designed smart IoT devices and low-powered analytics systems for applications in manufacturing.
• Guided and supervised student internships to deploy practical AI-driven solutions in dynamic environments, focusing on real-world applications of machine vision and predictive analytics.

Recognition and Achievements

• PUB Global Innovation Challenge Winner (2021): Recognized for leading a project that utilized machine vision and AI to automate the identification and enumeration of midge populations.
• Best Paper Award (2018): Awarded at the International PhD Academic Forum on Intelligent Manufacturing for innovative contributions to the field.
• HEIDENHAIN GmbH Scholarship Recipient (2016): Selected for academic excellence and potential in advancing precision manufacturing technologies.
• IMechE Mechatronics Student of the Year (2015): Honoured for outstanding innovation in mechatronics systems.
• Multiple prestigious scholarships, including the NCL SAgE Faculty Doctoral Training Scholarship (2014–2018) and the Gold Medal Award from Keppel Corporation Ltd (2013), recognizing excellence in engineering and manufacturing.

Research Interests

• Integration of machine vision and lightweight analytics into engineering problems for enhanced performance and precision.
• Development of scalable digital twin frameworks for predictive modelling and dynamic operations.
• Applications of cyber-physical systems in smart factories and robotics.
• Investigations of AI trust and acceptance in smart systems.

Teaching Areas

• Undergraduate: Programming, Robotics, Data Analytics, Digital Manufacturing.
• Postgraduate: Data Analytics Using Machine Learning, Energy Management and Standards Compliance.

• Articles

  • Choong ZJ, Huo D, Ponon N, Savidis R, Degenaar P, O'Neill A. A novel hybrid technique to fabricate silicon-based micro-implants with near defect-free quality for neuroprosthetics application. Materials Science and Engineering C 2020, 110, 110737.
  • Choong ZJ, Huo D, Degenaar P, O'Neill A. Micro-machinability and edge chipping mechanism studies on diamond micro-milling of monocrystalline silicon. Journal of Manufacturing Processes 2019, 38, 93-103.
  • Choong ZJ, Huo D, Degenaar P, O'Neill A. Edge chipping minimisation strategy for milling of monocrystalline silicon: A molecular dynamics study. Applied Surface Science 2019, 486, 166-178.
  • Choong Z, Huo D, Degenaar P, O'Neill A. Effect of crystallographic orientation and employment of different cutting tools on micro-end-milling of monocrystalline silicon. Proceedings of the Institution of Mechanical Engineering. Part B: Journal of Engineering Manufacture 2016, 230(9), 1756-1764.
  • Huo D, Choong Z, Shi Y, Hedley J, Zhao Y. Diamond Micro-milling of Lithium Niobate for Sensing applications. Journal of Micromechanics and Microengineering 2016, 26(9), 095005.
  • Huo DH, Lin C, Choong Z, Pancholi K, Degenaar P. Surface and subsurface characterisation in micro-milling of monocrystalline silicon. International Journal of Advanced Manufacturing Technology 2015, 81(5-8), 1319-1331.

• Conference Proceedings (inc. Abstracts)

  • Wong EWL, Goh KL, Lau MWS, Chong JJ, Choong ZJ. Digitalisation of Logbooks for Capstone Project. In: Applied Learning Conference. 2022, Singapore.
  • Choong ZJ, Huo D, Degenaar P, O'Neill A. Micro-machining of monocrystalline silicon with improved edge quality. In: 17th International Conference of the European Society for Precision Engineering and Nanotechnology, EUSPEN 2017. 2017, Hannover, Germany: euspen.
  • Choong ZJ, Huo D, Degenaar P, O'Neill A. Micro-Machinability Studies of Single Crystal Silicon Using Diamond End-Mill. In: ASME 2016 11th International Manufacturing Science and Engineering Conference. 2016, Blacksburg, Virginia, USA: American Society of Mechanical Engineers.
  • Choong ZJ, Huo D, Degenaar P, O'neill A. Investigation of edge-chipping reduction on silicon micro-milling. In: 16th International Conference of the European Society for Precision Engineering and Nanotechnology (EUSPEN 2016). 2016, Nottingham, UK: EUSPEN.
  • Al-Shibaany ZYA, Choong ZJ, Huo D, Hedley J, Hu ZX. CNC Machining of Lithium Niobate for Rapid Prototyping of Sensors. In: 2015 IEEE SENSORS. 2015, Busan, South Korea: IEEE.