Description:
TECHNOLOGY AREA(S): Materials
OBJECTIVE: Develop conceptual approaches that assess component degradation, flaws, and anomalies; the approaches use advanced sensors and image analysis that overcome current nondestructive inspection (NDI) limitations with real-time optical and electromagnetic sensors and algorithms.
DESCRIPTION: A significant challenge in aerospace engine and airframe structural inspection is in how to reduce manpower and provide repeatable accuracy. The increasing use of composite materials and integrated components such as blisks add to the technical challenge and point to the need for development of a reliable accurate non-intrusive rapid inspection capabilities. Current state-of-the-art (SOA) aircraft and engine structural inspections are done using electromagnetic sensors (eddy current), ultrasound, and penetrant dye (FPI) techniques. These techniques are labor intensive (over 100 hours per module), are low volume, and often do not have consistent accuracy (5-mil flaws). Hot and cold section turbine blades are discarded due to poor cost of inspection versus replacement. More automated inspection techniques, such as acoustic thermography and scanning electromagnetic probes, are maturing; however, the problem is the time required to perform these inspections is a linear function of the area that the sensor can cover in time and is a serious limitation of these techniques for integrated rotor inspections. Limitations of current optical near infrared (NIR) imaging inspection are also related to labor, efficiency, and capability. Significant developments in the field of hyperspectral (HSI) and hyper temporal (HTI) imaging algorithms and hardware have occurred over the last 10 years. They have historically been applied to environmental, surveillance, and other remote sensing problems. These techniques which apply wide spectrum imaging sensors can currently be leveraged to damage assessment of aircraft and engine structural components. HSI/HTI is accomplished by collecting spectral band data at the pixel level or through spatial light modulation over time. Passive sensors, such as cameras with ambient illumination are often used for image generation. The data sets obtained can provide a complete inspection record from the images scanned. The ability to inspect a wide array of aerospace structural components using HSI/HTI with passive optics of an image of arbitrary size has benefits over current optical visible and infrared (IR) techniques. Application of spectral and temporal image processing offer significant capability improvements to aircraft and engine health management at the engine depot or repair facility. They will also augment current methods in use. The SBIR program should address development of an innovative image-based inspection capability that has the potential to replace FPI, reduce use of eddy current by 50 percent, accommodate component dimensioning, and increase parts inspected per unit time over 2X. Image inspection advances NDI technology by capturing all relevant surface data of complex parts or multiple parts in a single array for processing, compared to low rate, high cost individual component area inspection. Development of new algorithms that reduce dimensionality and increase ability to find hidden flaws in the data at high rates compared to visual and scanning NDI techniques. The effort should leverage SOA sensors and imaging components while applying new algorithms that perform data separation and intelligent processing that can assess the structural integrity of aerospace structures and components (detection of flaws, material artifacts). SOA wide spectrum imaging hardware may be considered for this application. Technology such as cameras, optics, digital array sensors, excitation sources, and mechanical controls for HSI can be leveraged to achieve this capability. Comparison of the results with baseline SOA methods should be accomplished to establish a measure of improvement in both capability and inspection time. Working with an engine or airframe vendor is recommended to enhance the relevance and transition ability of the final research product.
PHASE I: Apply new NDI methodology to relevant rotor NDT image data collected by an engine original equipment manufacturer (OEM) or depot. Demonstrate the feasibility of the algorithms using collected spectral image data to the detection of flaws in integrally bladed rotors (IBRs). Demonstrate the potential to achieve on-line analysis and high levels of confidence for small features on complex parts compared with SOA penetrant and eddy current.
PHASE II: Develop a fully functional HSI/HTI component imaging capability, including data collection methodology and software algorithms. Select a relevant engine turbine component, such as an IBR to demonstrate the capability. Compare the accuracy and probability of detection (POD)/confidence levels achieved with SOA dye penetrant and electromagnetic methodology. Develop a preliminary transition plan and business case analysis.
PHASE III: Apply the concepts and capability developed in Phase II to a depot or inspection facility application. Develop an implementable hardware/software and imaging solution for industry use.
REFERENCES:
1. Hyperspectral Imagery Algorithms for the Processing of Multimodal Data, Mohammed Seghir Benmoussat, Thesis, 19 December 2013, Fresnel Institute, Fraunhoffer IIS.
2. Yong-Kai Zhu, Gui Yun Tian, and Rong-Sheng Lu Hong Zhang, A Review of Optical NDT Technologies, Sensors Open Access Journal (MDPI) ISSN, 8 August, 2011, pp. 7773-7798.
3. Giovanni Maria Carlomagno, Infrared Thermography in Materials Inspection and Thermo-Fluid Dynamics, Carosena Meola, University of Naples, Federico II, Italy, 2011.
4. Joseph Zalameda, Air Coupled Acoustic Thermography Inspection Technique, NASA Langley Research Center, MS221, Hampton, VA, 2007.
5. Frank O. Clark, Ryan Penny, Jason Cline, Wellesley Pereira, and John Kielkopf, Passive Optical Detection of a Vibrating Surface, SPIE, August, 2014.
KEYWORDS: Imaging, Optical Imaging, NDT, Inspection, Flaw Detection, Disk Crack Detection, Infrared (IR) Thermography
