Next-generation NDE: integrating artificial intelligence into advanced non-destructive inspection systems and structural health monitoring
R Maev
University of Windsor, Canada
As non-destructive evaluation (NDE) continues to evolve in the face of increasing complexity in engineered systems and material science, the integration of artificial intelligence (AI) stands out as a transformative opportunity. AI, particularly in the form of machine learning, deep learning and data-driven predictive analytics, offers substantial advances in the capabilities of non-destructive testing (NDT) and structural health monitoring (SHM) systems. From automated defect recognition to adaptive system behaviour and predictive maintenance scheduling, AI is poised to redefine the inspection landscape.
The exponential growth in data volume from sensor-rich NDE systems creates both opportunities and challenges. Manual interpretation of ultrasonic, radiographic, eddy current or visual data is time-consuming and prone to inconsistency. AI-based analytics can extract actionable insights from complex, high-dimensional datasets, enhancing both defect detection rates and diagnostic confidence. In aviation, for example, embedded SHM sensors combined with AI inference models enable real-time monitoring and damage prognosis during operational flight cycles.
A key area of innovation lies in supervised and unsupervised machine learning models trained on extensive inspection datasets. These models are capable of pattern recognition, anomaly detection and even classification of flaw types across variable testing conditions. Importantly, AI algorithms can continuously improve via retraining, making them highly suitable for in-line, closed-loop quality control systems in high-throughput environments such as automotive manufacturing.
Fast feedback and closed-loop adaptation are critical and have begun to be even more crucial these days. AI-based systems must not only interpret inspection data but also deliver insights rapidly enough to guide process adjustments. Real-time decision-making is central to enabling zero-defect manufacturing paradigms. Our research demonstrates that this vision is attainable today, with measurable reductions in false positives, labour costs and reliance on destructive verification testing.
Despite the promise, widespread implementation of AI in NDE still faces several barriers. These include the need for standardised datasets for training, interpretability of AI decisions, trust in autonomous systems, its precision and measurement stability and integration with legacy NDT infrastructure. We explore how emerging AI research, particularly in generative modelling, explainable AI (XAI) and hybrid physics-informed learning, can help address these limitations.
We will also outline the role of advanced computer vision in augmenting conventional techniques. For instance, AI-enhanced visual inspections using convolutional neural networks (CNNs) can surpass human capabilities in detecting micro-defects in welds, composites or additive-manufactured components. Similarly, AI-integrated robotic platforms extend the reach of NDE into hazardous, remote or geometrically complex environments with minimal operator intervention.
In pipeline integrity management, AI algorithms are now being used to fuse multi-sensory SHM data streams (for example acoustic, thermal, vibrational) for real-time structural condition assessment and early fault detection, which is critical for avoiding catastrophic failures.
The convergence of AI, robotics, sensor technology and materials informatics opens unprecedented avenues for advancing the mission of NDE: ensuring structural safety, lifecycle reliability and cost-effective asset management. As deep learning models continue to mature and become more interpretable and robust, they will play a central role in transitioning from detection to prediction and from passive to active intelligent NDE systems.
The exponential growth in data volume from sensor-rich NDE systems creates both opportunities and challenges. Manual interpretation of ultrasonic, radiographic, eddy current or visual data is time-consuming and prone to inconsistency. AI-based analytics can extract actionable insights from complex, high-dimensional datasets, enhancing both defect detection rates and diagnostic confidence. In aviation, for example, embedded SHM sensors combined with AI inference models enable real-time monitoring and damage prognosis during operational flight cycles.
A key area of innovation lies in supervised and unsupervised machine learning models trained on extensive inspection datasets. These models are capable of pattern recognition, anomaly detection and even classification of flaw types across variable testing conditions. Importantly, AI algorithms can continuously improve via retraining, making them highly suitable for in-line, closed-loop quality control systems in high-throughput environments such as automotive manufacturing.
Fast feedback and closed-loop adaptation are critical and have begun to be even more crucial these days. AI-based systems must not only interpret inspection data but also deliver insights rapidly enough to guide process adjustments. Real-time decision-making is central to enabling zero-defect manufacturing paradigms. Our research demonstrates that this vision is attainable today, with measurable reductions in false positives, labour costs and reliance on destructive verification testing.
Despite the promise, widespread implementation of AI in NDE still faces several barriers. These include the need for standardised datasets for training, interpretability of AI decisions, trust in autonomous systems, its precision and measurement stability and integration with legacy NDT infrastructure. We explore how emerging AI research, particularly in generative modelling, explainable AI (XAI) and hybrid physics-informed learning, can help address these limitations.
We will also outline the role of advanced computer vision in augmenting conventional techniques. For instance, AI-enhanced visual inspections using convolutional neural networks (CNNs) can surpass human capabilities in detecting micro-defects in welds, composites or additive-manufactured components. Similarly, AI-integrated robotic platforms extend the reach of NDE into hazardous, remote or geometrically complex environments with minimal operator intervention.
In pipeline integrity management, AI algorithms are now being used to fuse multi-sensory SHM data streams (for example acoustic, thermal, vibrational) for real-time structural condition assessment and early fault detection, which is critical for avoiding catastrophic failures.
The convergence of AI, robotics, sensor technology and materials informatics opens unprecedented avenues for advancing the mission of NDE: ensuring structural safety, lifecycle reliability and cost-effective asset management. As deep learning models continue to mature and become more interpretable and robust, they will play a central role in transitioning from detection to prediction and from passive to active intelligent NDE systems.
