Detection and Estimation of Sub-surface Defects Using Frequency Modulated Thermal Wave Imaging

Thermal wave imaging is an imperative non-destructive testing technique with inherent capabilities for testing and evaluating various solid materials. The method has the advantages of being a safe, fast, non-contact, and reliable technique, mapping surface temperature distribution which is utilized for non-destructive testing of materials. This work focuses on inspecting mild steel material, extensively used in various industries like construction, transportation, oil and gas, etc. Most commonly used industrial components made up of mild steel include construction beams, chimneys, sliding and rod type gates, etc. Safety and demand for quality of in-service products require thorough testing and reliable monitoring methodology to avoid failures. Thus, the characterization of materials and the processes going on in them is of considerable interest for the design of components to operate them safely and reliably. Aperiodic thermal-wave imaging methodology has been signifying as a consistent material characterization capability. In this work, a linear frequency modulated thermal wave imaging technique is applied in visualizing inclusions present in modeled mild steel sample. Also, an algorithm for automated defect identification is proposed for analyzing image contents, such as locating centers edges or regions and segmentation from the background image. Obtained results clearly show the capabilities of the proposed scheme for automatic detection of sub-surface defects. The shape of all the defects is recognized accurately irrespective of the type of inclusion used. It clearly distinguishes the elliptical irregular defects (for all defects, different materials are used for inclusions) present in the metallic sample from the sample’s sound regions and gives the other geometrical parameters of detected anomalies.