Xiaoyan Han

Develop algorithms to improve detectability of defects in Sonic IR imaging NDE

Sonic Infrared (IR) technology is relative new in the NDE family. It is a fast, wide area imaging method. It combines ultrasound excitation and infrared imaging while the former to apply ultrasound energy thus induce friction heating in defects and the latter to capture the IR emission from the target. This technology can detect both surface and subsurface defects such as cracks and disbands/delaminations in various materials, metal/metal alloy or composites. However, certain defects may results in only very small IR signature be buried in noise or heating patterns. In such cases, to effectively extract the defect signals becomes critical in identifying the defects. In this paper, we will present algorithms which are developed to improve the detectability of defects in Sonic IR.

Further Development of Image Processing Algorithms to Improve Detectability of Defects in Sonic IR NDE

Sonic Infrared imaging (SIR) technology is a relatively new NDE technique that has received significant acceptance in the NDE community. SIR NDE is a super-fast, wide range NDE method. The technology uses short pulses of ultrasonic excitation together with infrared imaging to detect defects in the structures under inspection. Defects become visible to the IR camera when the temperature in the crack vicinity increases due to various heating mechanisms in the specimen. Defect detection is highly affected by noise levels as well as mode patterns in the image. Mode patterns result from the superposition of sonic waves interfering within the specimen during the application of sound pulse. Mode patterns can be a serious concern, especially in composite structures. Mode patterns can either mimic real defects in the specimen, or alternatively, hide defects if they overlap. In last year’s QNDE, we have presented algorithms to improve defects detectability in severe noise. In this paper, we will present our develop...

Developing signal processing method for recognizing defects in metal samples based on heat diffusion properties in sonic infrared image sequences

Abstract. In sonic infrared (SonicIR) imaging, heat is generated in defect areas during the sonic pulse; the heat appears bright in SonicIR images as the indication of a defect. However, in practical applications of SonicIR, there are lots of disturbing bright areas in infrared images, such as heat reflection and paint problem. When crack size is small, the generated heat appears not bright enough to be recognizable. Based on heat diffusion properties in the one-dimensional temporal and two-dimensional spatial domain, a method is developed to automatically recognize defect signals from SonicIR image sequences. The algorithm is verified with the SonicIR image sequences of 100 metal plates which may have different thickness, materials, or crack sizes.

Studying the nonlinearity in Sonic IR NDE

Sonic IR Imaging combines pulsed ultrasound excitation and infrared imaging to detect defects in materials. The sound pulse causes rubbing due to non---unison motion between faces of defects, and infrared sensors image the temperature map over the target to identify defects. It works in various materials, including metal/metal alloy, ceramics, and composite materials. Its biggest advantage is that it’s a fast, wide area NDE technique. It takes only a fraction of a second or a few seconds, depending on the thermal properties of the target, for one test over a few square feet. However, due to the nonlinearity in the coupling between the ultrasound transducer and the target, the repeatability has been an issue, which affects its application. In this paper, we present our study on this issue in Sonic IR.