College of Engineering Technology

Additive manufacturing, and tissue engineering

AI research in additive manufacturing (AM) and tissue engineering is at the forefront of innovation, aiming to enhance the capabilities of these fields and drive advancements in various applications. Here’s an overview of how AI is influencing research in additive manufacturing and tissue engineering:

AI in Additive Manufacturing:

1. Process Optimization:
   • Topology Optimization: AI algorithms optimize the design and structure of 3D-printed objects, ensuring they meet specific criteria for strength, weight, and functionality.
   • Parameter Optimization: AI is used to adjust printing parameters, such as temperature and speed, in real-time for improved printing quality and efficiency.
2. Generative Design:
   • AI-Generated Designs: Generative design, powered by AI, creates innovative and complex designs that are often beyond human intuition, optimizing structures for specific functions.

AI in Tissue Engineering:

1. Bioprinting Optimization:
   • Cell Placement: AI algorithms optimize the precise placement of cells during bioprinting, enhancing the creation of functional tissues and organs.
   • Vascularization: AI assists in designing vascular networks within printed tissues to improve nutrient and oxygen transport.

In both additive manufacturing and tissue engineering, AI is revolutionizing the fields by improving precision, efficiency, and the overall capabilities of these technologies. As research continues, the synergy between AI and these domains is expected to unlock new possibilities in personalized healthcare, advanced materials, and regenerative medicine.

Mobile Robotics, Controls

AI Research in Mobile Robotics and Controls:

AI research in mobile robotics and controls is at the forefront of transforming autonomous systems, enabling them to navigate, perceive, and adapt in dynamic environments. Here’s an overview of how AI is influencing research in mobile robotics and controls:

AI in Mobile Robotics:

1. Simultaneous Localization and Mapping (SLAM):
   • Visual SLAM: AI algorithms enhance visual SLAM capabilities, allowing robots to navigate and map their surroundings using cameras and sensors.
   • Lidar and Radar Processing: AI processes data from lidar and radar sensors to create accurate and real-time maps, facilitating precise robot localization.
2. Object Recognition and Scene Understanding:
   • Deep Learning for Object Recognition: AI, particularly deep learning, enables robots to recognize and categorize objects in their environment, enhancing their ability to interact with and respond to their surroundings.
   • Semantic Understanding: AI-driven semantic segmentation helps robots understand the context of their environment, distinguishing between different elements and making informed decisions.
3. Path Planning and Navigation:
   • Dynamic Path Planning: AI algorithms dynamically adjust robot paths based on real-time changes in the environment, ensuring safe and efficient navigation.
   • Learning-Based Navigation: Reinforcement learning and other AI techniques empower robots to learn optimal navigation strategies, adapting to diverse terrains and scenarios.

As research progresses in mobile robotics and controls, the integration of AI technologies promises to revolutionize automation, creating intelligent systems capable of increasingly sophisticated tasks. From autonomous vehicles to robotic platforms, the synergy between AI and robotics is paving the way for a new era of intelligent and adaptive automation.

Automation, controls, instrumentation

AI in Body Sensors/Monitoring/Instrumentation:

1. Body Health Monitoring:
   • Continuous Vital Sign Monitoring: AI processes data from body sensors to monitor vital signs continuously, providing real-time health insights.
2. Medical Instrumentation:
   • Smart Diagnostics: AI-driven medical instruments enhance diagnostic capabilities, interpreting complex data from imaging and diagnostic tools.
   • Remote Patient Monitoring: AI in instrumentation supports remote patient monitoring, allowing healthcare professionals to track patient health outside traditional healthcare settings.

The integration of AI in automation, controls, and body sensors/monitoring/instrumentation is creating intelligent systems that not only enhance operational efficiency but also revolutionize healthcare and personalized well-being. As research progresses, the synergy between these domains will contribute to a future where intelligent automation seamlessly integrates with human-centric technologies for improved quality of life and productivity.

Generative AI implementation in STEM education

This is new work that is just starting. New generative AI-powered tools will be introduced and implemented into Civil Engineering courses and will prepare students to understand and use the generative AI tools to solve engineering problems. This approach will emphasize critical thinking skills to facilitate students’ ability to collect evidence, evaluate evidence, and make engineering judgments.

AI Theory and Applications

Fundamental research in AI theory, focusing on basis functions, dimension reduction, multi-objective and multi-criteria evaluation systems, high-performance computing with efficient and parallel algorithms, symbolic computation; AI applications to partial differential equations, data science, internet of things, connected and autonomous vehicle, smart cities, intelligent transportation system, asset management system, transportation safety, damage assessment, disaster monitoring and safety management, remote sensing and photogrammetry, construction material, building, and infrastructure.