Ebrahim Oromiehie
Postdoctoral Researcher and AFP Robot Specialist
University of New South Wales
Education: He holds a Bachelor's degree in Mechanical Engineering, a Master's degree in Manufacturing Engineering and Management, and a PhD in Advanced Manufacturing from the School of Mechanical & Manufacturing Engineering at UNSW Sydney.
Research interests: Dr. Oromiehie is a Senior Technical Officer (Robotic Composites) and Postdoctoral Fellow at the Australian Research Council (ARC) Training Centre for Automated Manufacture of Advanced Composites (AMAC). Dr. Oromiehie possesses adapted knowledge of Automated Fiber Placement (AFP)-based composites and has hands-on experience in manufacturing composite specimens using advanced techniques such as AFP, as well as traditional autoclaving, resin infusion, and hand layup techniques. His expertise lies in the in-situ structural health monitoring (SHM) of AFP composites using optical sensors. besides, He has collaborated with several research groups, institutes and universities to implement different NDT techniques for SHM of advanced composites. In addition, Dr. Oromiehie has over 10 years of experience as a Mechanical Designer and Project Engineer in the automotive industry, where he designed various parts, railroad cars, jigs, checking fixtures, and industrial measuring tools, while also managing all aspects of engineering projects from concept to manufacture.
Additional skills: Structural Health Monitoring: Optical Fibre Bragg grating Sensors; 3D CAD Modeling: SolidWorks, CATIA, AutoCAD; FEA: ABAQUS, ANSYS (Static structural analysis, Modal analysis, Thermal analysis, ANSYS ACP); Composite Fabrication: Automated fibre placement (AFP), Autoclaving, wet layup, resin infusion & vacuum infusion; Fibre Path Generation: Fibre Placement Management (FPM)/ VERICUT Composite Planning / Simulation (VCP/VCS); Drawing: Geometric Dimensioning & Tolerancing (GD&T)
Research interests: Dr. Oromiehie is a Senior Technical Officer (Robotic Composites) and Postdoctoral Fellow at the Australian Research Council (ARC) Training Centre for Automated Manufacture of Advanced Composites (AMAC). Dr. Oromiehie possesses adapted knowledge of Automated Fiber Placement (AFP)-based composites and has hands-on experience in manufacturing composite specimens using advanced techniques such as AFP, as well as traditional autoclaving, resin infusion, and hand layup techniques. His expertise lies in the in-situ structural health monitoring (SHM) of AFP composites using optical sensors. besides, He has collaborated with several research groups, institutes and universities to implement different NDT techniques for SHM of advanced composites. In addition, Dr. Oromiehie has over 10 years of experience as a Mechanical Designer and Project Engineer in the automotive industry, where he designed various parts, railroad cars, jigs, checking fixtures, and industrial measuring tools, while also managing all aspects of engineering projects from concept to manufacture.
Additional skills: Structural Health Monitoring: Optical Fibre Bragg grating Sensors; 3D CAD Modeling: SolidWorks, CATIA, AutoCAD; FEA: ABAQUS, ANSYS (Static structural analysis, Modal analysis, Thermal analysis, ANSYS ACP); Composite Fabrication: Automated fibre placement (AFP), Autoclaving, wet layup, resin infusion & vacuum infusion; Fibre Path Generation: Fibre Placement Management (FPM)/ VERICUT Composite Planning / Simulation (VCP/VCS); Drawing: Geometric Dimensioning & Tolerancing (GD&T)
Binayak Bhandari
Research Associate and Artificial Intelligence Specialist
University of New South Wales
Education: PhD Student - I completed a Bachelor of Mechanical Engineering at UNSW in 2021, with a thesis investigating fault detection in gearboxes using rotary encoders. During my time as an undergraduate, I worked extensively with the solar racing team, Sunswift, where I worked on the design and fabrication of Sunswift 7’s carbon fibre chassis.
Research interests: Spent over a decade on innovation, research & development, and higher education and has contributed to composite materials, advanced machining, artificial intelligence, structural health monitoring, robotics, renewable energy, railroad engineering, design and analysis of mechanical systems, and appropriate technology. Member of various professional organizations, including Engineers Australia, Korean Society for Precision Engineering, and Korean Society of Railway.
Additional skills: Computer Aided Design (CATIA); Finite Element Analysis (ANSYS, Mechanical, Static, Explicit Dynamics, Fluent); Artificial Intelligence (Scikit-Learn, Tensorflow); Acoustic Emission and Structural Health Monitoring (Mistras, Noesis); Programming Languages (Python, Matlab)
Research interests: Spent over a decade on innovation, research & development, and higher education and has contributed to composite materials, advanced machining, artificial intelligence, structural health monitoring, robotics, renewable energy, railroad engineering, design and analysis of mechanical systems, and appropriate technology. Member of various professional organizations, including Engineers Australia, Korean Society for Precision Engineering, and Korean Society of Railway.
Additional skills: Computer Aided Design (CATIA); Finite Element Analysis (ANSYS, Mechanical, Static, Explicit Dynamics, Fluent); Artificial Intelligence (Scikit-Learn, Tensorflow); Acoustic Emission and Structural Health Monitoring (Mistras, Noesis); Programming Languages (Python, Matlab)
Prashanth Nagulapally
Research Associate and Sensing Specialist
University of New South Wales
Education: PhD (Mechanical Engineering, UNSW, Aus); Master of Engineering Science (Mechanical Eng, University of Southern Queensland, Toowoomba, Aus); Bachelor of Technology (Mechanical Eng, SRM University, India)
Research interests: Fibre optic sensing, Structural Health Monitoring, FRP composite structures, ML
Additional skills: Fiber optic sensors installation and operation, FEA, Data analysis, Composite Fabrication
Research interests: Fibre optic sensing, Structural Health Monitoring, FRP composite structures, ML
Additional skills: Fiber optic sensors installation and operation, FEA, Data analysis, Composite Fabrication