Computational simulations trained neural network to characterize non-visible material flaws from ultrasound real-life measurements

Computational simulations trained neural network to characterize non-visible material flaws from ultrasound real-life measurements

Overview

Non-destructive testing (NDT) has been used as an important tool to detect flaws/crack-like anomalies to assess structural integrity for a wide variety of applications. Current in-field ultrasonic defect detection in pipelines relies heavily on interpretation of a human operator thus is not robust and large uncertainties remain in the detection. This technology presents a machine learning-based system for non-destructive evaluation (NDE) of materials and structures to detect and classify internal defects such as cracks, corrosion, and other flaws. The invention leverages finite element modeling (FEM) and computational simulations to generate training datasets for machine learning models. These models analyze ultrasonic scan data, identifying defect types, locations, and severities with high precision. The approach integrates deep learning algorithms, including convolutional neural networks (CNNs), to enhance flaw detection accuracy beyond traditional ultrasonic testing methods. By simulating real-world material defects in a virtual environment, the system ensures improved defect characterization while reducing reliance on costly and time-consuming experimental data collection.

Market Opportunity

Structural integrity assessment is critical in aerospace, automotive, civil engineering, and manufacturing industries, where undetected material flaws can lead to catastrophic failures. Traditional NDE methods, such as ultrasonic testing (UT), often require skilled operators and are limited by interpretation challenges and data variability. With growing demands for automated and highly accurate inspection solutions, AI-driven defect detection presents a significant commercial opportunity. The global NDE market is projected to grow rapidly due to increased safety regulations and aging infrastructure requiring frequent inspections. Industries stand to benefit from machine learning-based real-time defect classification, reducing maintenance costs and improving structural reliability. The proposed system bridges a major gap by offering an efficient, scalable, and highly accurate alternative to conventional inspection techniques.

Innovation and Meaningful Advantages

There are multiple advantages to using this novel AI-powered framework for material defect detection. First, there is an automated and highly accurate defect detection that utilizes AI models trained on realistic defect simulations, improving flaw identification and classification accuracy over traditional NDE methods. Next, this technology is a cost-effective and scalable solution that reduces the need for extensive manual inspections and physical testing by generating synthetic training data from finite element simulations. Then there is advanced machine learning integration which implements CNN-based deep learning models to analyze ultrasonic signals, enabling precise detection of cracks, corrosion, and other structural anomalies. This technology also offers a versatile application because it is applicable across multiple industries, including aerospace, construction, manufacturing, and energy sectors, where structural integrity is a top priority. Finally, this technology has enhanced predictive maintenance that can support early defect detection, allowing industries to implement proactive maintenance strategies and minimize unexpected failures. Overall, this invention represents a breakthrough in non-destructive testing by combining AI, computational modeling, and ultrasonic inspection into an advanced, automated defect evaluation system.

Collaboration Opportunity: We are interested in exploring research collaborations and licensing opportunities.

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