Author: Joel B. Harley

Why AI Struggles to Crack the Code of Nondestructive Testing

There’s a common trope in science fiction: an all-seeing machine, unblinking and exact, scanning the world for flaws we can’t detect. In the real world of nondestructive testing (NDT)—the science of using sensors to find cracks, corrosion, and hidden damage in everything from aircraft wings to oil pipelines—that dream still feels frustratingly out of reach.

We have artificial intelligence that can beat world champions at Go, generate Hollywood-quality dialogue, and diagnose rare diseases from pixelated scans. And yet, when we point these same tools at ultrasonic signals or thermographic images from NDT inspections, the results are… inconsistent at best.

June 4, 2025 0

Temporal Sparse Wavenumber Analysis Repository

Temporal Sparse Wavenumber Analysis (TSWA) is a novel technique that reconstructs high-resolution spatiotemporal wavefields using fewer temporal samples than traditional methods typically require. Developed by Soroosh Sabeti and Dr. Joel B. Harley, TSWA makes it possible to retrieve accurate guided wave information from temporally undersampled data, which is crucial in applications where high-speed sensing, data storage, or power consumption are limiting factors.

June 3, 2025 0
Biomechanics Research

When Biomechanics Meets Reality: The OpenSim Simulation Gap

In the realm of biomechanics, OpenSim stands as a cornerstone—a powerful, open-source simulation engine that enables researchers to model and analyze musculoskeletal dynamics. From studying gait patterns to designing prosthetics, OpenSim has been instrumental in advancing our understanding of human movement.

Yet, as with any model, there exists a gap between simulation and reality. Recent studies have highlighted discrepancies between OpenSim’s predictions and actual sensor data, raising questions about the fidelity of these simulations. This divergence, often referred to as the “sim-to-real gap,” underscores the challenges inherent in accurately modeling complex biological systems.

May 21, 2025 0
Data-driven matched field processing

Data-Driven Matched Field Processing (DDMFP) Repository

Data-Driven Matched Field Processing (DDMFP) is an innovative signal processing framework designed for localizing acoustic sources in complex environments, such as those encountered in structural health monitoring (SHM) using Lamb waves. Traditional matched field processing (MFP) techniques rely heavily on accurate physical models of the propagation medium, which can be challenging to obtain in real-world scenarios. DDMFP circumvents this limitation by constructing localization models directly from measured data, enhancing robustness and accuracy in complex, multimodal propagation environments.

May 17, 2025 0

Sparse Wavenumber Analysis (SWA) Repository

Sparse Wavenumber Analysis (SWA) is a signal processing algorithm designed to extract high-resolution wavenumber information from spatial wavefield measurements—especially when that data is sparse, noisy, or irregularly sampled. Developed by Dr. Joel B. Harley and collaborators, SWA enables researchers and engineers to analyze wave propagation with unprecedented clarity, even in situations where traditional Fourier-based methods fall short.

This CodeOcean capsule contains a complete, reproducible implementation of the SWA algorithm, based on the foundational work in:

This CodeOcean capsule contains a complete, reproducible implementation of the SWA algorithm, based on the foundational work in:

May 16, 2025 0

When Grains Go Rogue: Rethinking Microstructure Growth in Materials Science

New experiments challenge decades-old models of grain growth, revealing a more chaotic—and fascinating—reality.

In the world of materials science, grain growth has long been considered a well-understood process. The prevailing theory posits that, over time, the tiny crystalline grains within a metal or ceramic coalesce and grow, driven by the reduction of total grain boundary area—a process akin to soap bubbles merging to minimize surface tension. This phenomenon, often described by the mean curvature flow model, suggests a smooth, predictable evolution of microstructures.

May 16, 2025 0

Listening to the Plate: How Lamb Waves Quietly Reveal the Structure of Materials

Guided waves like Lamb modes are reshaping how we inspect, model, and understand solid materials — all by listening to vibrations within the structure itself.

If you’ve never heard of Lamb waves, you’re not alone. Though they’ve been known to physicists and engineers for over a century, they remain surprisingly underdiscussed outside specialized fields like non-destructive testing, ultrasonics, and solid mechanics. But behind the scenes, Lamb waves are playing a crucial role — helping us understand how materials behave, age, and break, all through the language of wave motion.

May 16, 2025 0

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