About Kazem
I have a strong background in advanced manufacturing system, reverse engineering, robotic, dental technology, and predictive maintenance. My research interests are in two different areas of robotic and bioengineering in dental and health care, predictive maintenance, and renewable energy (wind turbine through-life engineering services).
In robotic and bioengineering, the emphasis is on prototyping novel humanoid chewing simulators for In-vitro, testing of dental components/materials at relatively low cost and drug delivery system using a medicated chewing gum. Both dental and biomedical applications involve a multidisciplinary approach finding inspiration from biological systems to design and develop a new product for health care. The recent collaboration with the Clinical Trials Unit at the Bristol Dental School has shown a great potential for these two inventions.
In predictive maintenance, the emphasis is on performance assessment and failure prediction of wind turbine gearbox. This pioneering work with Vestas is the first of its kind, developing predictive maintenance techniques for early detection of failures in wind turbine gearboxes (WTs), proving a benchmark tool for offshore wind farm owners to make decisions in advance to plan and schedule for operations and maintenance to reduce cost.
Publications
- Innovative PLM methodology framework for bionics with built-in reverse biomimetics for innovative product development: from inception to proof-of-concept and clinical validation of large-scale, human skeletal systems
- Development of a Chewing Robot with Built-in Humanoid Jaws to Simulate Mastication to Quantify Robotic Agents Release from Chewing Gums Compared to Human Participants
- Prototyping and Evaluating a Novel Humanoid Chewing Robot for Drug Delivery using a Medicated Chewing Gum
- Prototyping and Evaluating a Novel Humanoid Chewing Robot for Drug Delivery using a Medicated Chewing Gum
- Through-life engineering services of wind turbines
- Through-life engineering services of wind turbines
- Analysing RMS and peak values of vibration signals for condition monitoring of wind turbine gearboxes
- A framework for optimising product performance through feedback and reuse of in-service experience