Ai-driven non-destructive waveform analysis for adhesive and coating assessment
Leveraging AI with non-destructive ultrasonic and eddy current waveform analysis, this technology rapidly quantifies adhesive and coating strength, enabling efficient, high-throughput testing compared to traditional ASTM methods.
Overview
This innovative technology utilizes advanced machine learning applied to non-destructive testing methods, specifically ultrasonic and eddy current waveform analysis, to rapidly quantify the mechanical integrity of adhesives and coatings. Traditional ASTM methods for assessing adhesion strength are time-consuming and not suitable for high throughput. By capturing full waveforms and correlating them with ASTM results, this solution offers a quick (seconds) and efficient alternative that can potentially be deployed in the field, enhancing the speed and flexibility of testing processes.
Technical specifications
Key features:
- Ultrasonic waveform analysis: Applied to adhesives to determine mechanical interlocking and chemical bonding quality.
- Eddy current waveform analysis: Utilized for assessing coating thickness and integrity.
- Machine learning integration: Full waveform data is used to train high-fidelity models that provide rapid screening of adhesive and coating metrics.
- Field portability: Potential for deploying a portable system for on-site testing and analysis.
- Compatibility with ASTM standards: Allows for correlation with existing ASTM test results, enhancing validation and reliability.
Technology readiness level
The core technology is at TRL-3, indicating active research and development with demonstrated feasibility. The ultrasound system has received FAA certification (TRL-6) for part qualification, while the integration of machine learning for waveform analysis and eddy current methods are at earlier stages. Ongoing developments aim to enhance and validate the system for broader applications.
About Baylor University
Baylor University is a private, faith‑driven research university in Waco, Texas, recognized at the Carnegie R1 level. Industry collaborates through a university‑affiliated research and technology park that co‑locates faculty labs with company tenants, shared instrumentation, convening space, and business acceleration. The institution’s Dallas‑based nursing school is located on the campus of a major teaching hospital, creating channels for clinical collaboration and translational projects. Corporate engagement is supported by an Office of Innovation & Economic Development that manages IP, licensing, startup formation, and sponsored research agreements. Research is sustained by competitive federal funding and partnerships that leverage Central Texas’s I‑35 corridor linking Dallas–Fort Worth, Austin, and San Antonio.
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