PhD Studentship - UK STUDENTS ONLY - Bachelor's or Master's degree in Engineering/Physics

---> PhD Studentship for UK STUDENTS ONLY <---

******* DO NOT APPLY IF YOU ARE NOT A UK STUDENT ******

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Job opportunity: 3.5 year ECR PhD Studentship with a start date of September 2025. Funding will cover tuition fee and stipend.

Location: University of Manchester (Dept. of Materials, Engineering Building A, Oxford Road) [The University of Manchester is among the top ten academic institutions in Europe and among the top thirty in the world]

Supervisors: Prof. Prasad Potluri (Chair in Robotics and Textile Composites), Dr Leandro Maio (Head of the NDT Lab)

PhD Project: AICOM (AI for Composites Operations and Maintenance)

Activity to be performed: development of smart system for composites health monitoring

Stipend: £20,780 (tax free stipend set at the UKRI rate)

Entry Requirements: UK STUDENTS ONLY - Bachelor's degree or Master's degree in engineering or physics

Deadline: Monday, June 19th, 2025

How to apply: https://www.findaphd.com/phds/project/aicom-ai-for-composites-operations-and-maintenance/?p182117

For further info: [email protected]

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About the project

Composite materials are increasingly utilized in aerospace, automotive, and civil engineering due to their high strength-to-weight ratio and excellent mechanical properties. However, damage such as delamination, fiber breakage, and matrix cracking can significantly compromise the structural integrity of composite components. Traditional inspection methods are often time-consuming, labor-intensive, and may not provide real-time insights. This research, supervised by Dr. Leandro Maio (in charge of the NDT Lab) and Prof. Prasad Potluri (Chair in Robotics and Textile Composites and Director of the Northwest Composites Centre), proposes a novel Structural Health Monitoring (SHM) system leveraging the power of AI and smart sensors to enable real-time, on-site damage detection in composite structures.

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Research Objectives

• Develop a network of surface or embedded smart sensors capable of monitoring key parameters (strain, vibration, acoustic emission) within composite structures.
• Integrate an edge AI processing unit into the sensor network for real-time data acquisition, processing, and decision-making.
• Develop and train AI algorithms (e.g., Convolutional Neural Networks, Recurrent Neural Networks) for:

-- Anomaly Detection: Identify deviations from normal structural behavior.

-- Damage Classification: Categorize the type and severity of damage (e.g., delamination, fiber breakage).

-- Damage Localization: Pinpoint the location of damage within the structure.

• Evaluate the performance and accuracy of the proposed SHM system through controlled laboratory experiments.
• Demonstrate the feasibility of implementing this system in real-world applications.

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Research Methodology

• Sensor Selection/Design and Integration:

-- Select appropriate sensor technologies (e.g., fiber optic sensors, piezoelectric sensors, acoustic emission sensors, graphene-based sensors) based on their sensitivity to specific damage types and design novel directional transducers.

-- Develop strategies for embedding sensors within composite structures while minimizing their impact on structural performance.

• Edge AI Development:

-- Select an appropriate edge computing platform (e.g., microcontrollers with integrated AI accelerators).

-- Develop and train AI models using supervised and unsupervised learning techniques.

-- Optimize AI models for low-power operation and real-time inference on the edge device.

• Experimental Validation:

-- Conduct controlled laboratory experiments on composite specimens with induced damage (e.g., impact tests, fatigue tests).

-- Collect sensor data and evaluate the performance of the AI algorithms in detecting and classifying damage.

-- Perform tests on structures to validate the system's performance in realistic operating conditions.

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Expected Outcomes

• Real-time Damage Detection: Accurate and timely identification of damage in composite structures.
• Improved Structural Safety: Proactive maintenance and repair strategies to prevent catastrophic failures.
• Reduced Inspection Costs: Minimize the need for time-consuming and costly manual inspections.
• Reliability and Durability: Extend the service life of composite structures.

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This research has the potential to significantly impact the field of SHM by: (i) enabling real-time, on-site monitoring of critical composite structures; (ii) improving the safety, reliability, and maintainability of advanced composite materials; (iii) facilitating the development of more efficient and cost-effective inspection and maintenance strategies.

It is highly multidisciplinary and allows the selected candiate to acquire skills in numerous fields, from materials (composites and nanocomposites) to numerical modelling, passing through additive manufacturing, high level programming languages, signal analysis especially, up to electronics and much more..a broad-spectrum training and research experience.

Discover! Innovate! Join our leading research team!

To apply, please go to this link:

https://www.findaphd.com/phds/project/aicom-ai-for-composites-operations-and-maintenance/?p182117