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Edge detection combined with optical and infrared NDT techniques: an aid for wooden samples with complex surface and subsurface defects by S. Sfarra 1,

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Presentation on theme: "Edge detection combined with optical and infrared NDT techniques: an aid for wooden samples with complex surface and subsurface defects by S. Sfarra 1,"— Presentation transcript:

1 Edge detection combined with optical and infrared NDT techniques: an aid for wooden samples with complex surface and subsurface defects by S. Sfarra 1, J.L. Bodnar 2, D. Ambrosini 1, K. Mouhoubi 2, D. Paoletti 1 1 Department of Industrial and Information Engineering and Economics (DIIIE), Las.E.R. Laboratory, University of LAquila, LAquila, Italy 2 GRESPI/ECATHERM, UFR Sciences Exactes et Naturelles, Reims cedex 02, France Stefano Sfarra 1/22

2 Abstract To explore wood is to realize its complexity, its diversity, and its variability. The union of wood and paint is as old as the human desire to protect an object, or simply to decorate a surface. The link between paint and wood is therefore at the heart of any approach to conservation of these objects. To the conservator, the analysis of conditions and problems involves a familiarity with the physical structure of the wood as a material and with its surface interaction with the applied paint, as well as with the behaviour of the wood after paint application. Because paints are mixtures of pigments and binders, degradation is only partially determined by the nature of the binder. Many paints based on the binders are very fragile; consequently, their susceptibility to physical or mechanical damage is generally a more important cause of conservation problems than chemical changes in the binders themselves. Panel paintings are increasingly being investigated using advanced non-destructive infrared and optical measurement techniques. In the present work, a wooden sample having a complex surface and realized following the Cennino Cennini rules, containing natural and fabricated defects (Mylar ® inserts), was investigated by stimulated thermography, near-infrared reflectography, double-exposure (DE) and sandwich holographic (SH) interferometry. The stimulated thermography technique consists in depositing energy, whatever be the means of deposition and the type of energy (sun, flash lamp, laser, and hot air flow), into the observed system (in the present case a wooden sample with complex surface and subsurface defects) and in monitoring the temporal and/or local evolution of the surface temperature field of the system caused by this thermal stimulation. Infrared reflectography is a non- destructive testing imaging technique based on the different optical behaviour of visible and near-infrared (NIR) radiation through a thin pictorial layer. This effect is a consequence of both lower NIR absorption and reduced NIR scattering due to the particle size smaller than the wavelength. The acquisition of NIR images using LED lamps working at different wavelengths, seems a very promising method in this field. However, NIR and DE are not dynamic techniques, while SH is a dynamic technique. In the latter, a number of holograms can be made, each one recording a single state of the object, in a temporal sequence. Since enhancing the edge of a detached area identified by SH, means improving the detection of the defects position, this idea was applied in the present research. Instead, the defects depth was retrieved working with phase analysis, i.e. using the pulsed phase thermography (PPT) technique. Finally, the results coming from optical and infrared NDT techniques were compared each other in order to explore the advantages and disadvantages of the methods used. Stefano Sfarra 2/22

3 Stefano SfarraOutline 1.Introduction; 2.Description of the sample; 3.Principles of Non-Destructive Testing (NDT): I.Active InfraRed Thermography (IRT); a.Principal Component Thermography (PCT); b.Pulsed Phase Thermography (PPT); II.Holographic Interferometry (HI: DE – SH); III.Near-InfraRed Reflectography (NIRR); IV.Ultraviolet (UV) Imaging; 4.The Canny edge detector; 5. Experimental results; 6. Conclusions. 3/22

4 Stefano Sfarra 1. Introduction 4/22

5 Stefano Sfarra 2. Description of the sample Figure: (a) sketch of the sample, (b) α side. The position of the defects are marked, and (c) β side. The position of the defects are marked. The inspected sample is an hollow cylinder [height (l): 180 mm] that is composed of a support in poplar wood [thickness (r – r i ): 16.5 mm], several defects in Mylar ® [thickness: 0.2 mm], a layer of gypsum [thickness: 2 mm, α = 4.7 m 2 /s x ] and a layer of paint [thickness: 0.5 mm, α = 0.87 m 2 /s x ]. The layers of paint and gypsum are given by r e – r (Fig. a). In particular, the defects of the α side are five (Fig. b), while the defects of the β side (Fig. 1c) are seven, ranging from 10 mm of diameter (α 4 ) to 4.5 x 2 mm 2 (α 2 ). In this Fig. it is possible to see the gypsum layer, while in Fig. a the painted sample. 5/22

6 Stefano Sfarra 3. Principles of Non-Destructive Testing (NDT) 6/22

7 Stefano Sfarra 3.I.a. IRT – Principal Component Thermography (PCT) Rajic N. Principal component thermography for flaw contrast enhancement and flaw depth characterization in composite structures, Compos. Struct., 58: , In the square pulse configuration, the specimen surface is submitted to a long square pulse (from a few seconds to several minutes), and the temperature rise and decay is registered using an infrared camera and stored as a 3D matrix composed by N thermograms, where x and y are the spatial coordinates, and t is the time. SPT data is generally processed to improve defect visibility and to performed quantitative characterization of defects. Arndt proposes an adaptation of the pulsed phase thermography algorithm to detect and characterize defects from SPT experiments. In this work, we propose to use principal components to process SPT data. 7/22

8 Stefano Sfarra 3.I.b. IRT – Pulsed Phase Thermography (PPT) Mathematically, a pulse can be decomposed into a multitude of individual sinusoidal components. In that respect, when a specimen is pulse heated, thermal waves of various amplitudes and frequencies are launched into the specimen, in a transient mode. Going back and forth between temporal and frequency domains is possible with mathematical tools such as the well known Fourier transform. Figure 1 shows an example of correspondence between these domains. In fact for PT thermal waves of different frequencies are launched in the specimen simultaneously in the transient regime while in LT, one thermal wave of single frequency is tested in stationary regime. The possibility to link both techniques was then found interesting and this was called pulsed phase thermography (PPT) processing. 8/22

9 3.II. Holographic Interferometry (HI: DE) Experimental configuration for Double Exposure (DE) HI Stefano Sfarra 9/22

10 Stefano Sfarra 3.II. Holographic Interferometry (HI: DE) In Double-Exposure holographic interferometry, two holograms of the two object waves occurring sequentially in time are recorded on a single photographic plate. The interference between these images produces interference fringes overlaid on the image of the object. The interference fringes are indicative of deformation, displacement, rotation and change in refractive index or thickness of the object. The fringes appear to be localized in space, not necessarily on the object. When viewing direction is altered, the fringes shift and change their form. A hologram of the object in its initial unstressed state is recorded by exposing the photographic plate. Without removing the photographic plate from the setup, the object is stressed and a second exposure is made. The plate is then developed and reconstructed by the reconstruction wave to observe the interference. 10/22

11 3.II. Holographic Interferometry (HI: SH) The basic idea of the Sandwich HI technique consists in recording of two individual holograms for two different stages of the object. Both holograms are reconstructed at the same time. Unlike the DE method, here it is possible to change mutual position of the records of the reconstructed wavefronts during the reconstruction process, because each of them regenerates independently. The relative change in position of one hologram with respect to the other is equivalent to the relevant change of the object between the exposures, and vice versa. In this manner, it is possible to compensate the specific changes of the object additionally in the process of reconstruction. The technical realization of this method is based on the use of special kinematical equipment to place the holograms exactly back to the places where they were exposed. The method facilitates mainly the measurements of the displacements perpendicular to the surface plane of the investigated object; but it can also be applied to measure any of its deformations. Stefano Sfarra 11/22

12 3.III. Near-InfraRed Reflectography (NIRR) Near-infrared reflectography (NIRR) is a non-destructive imaging technique based on the different optical behaviour of visible and near infrared (NIR) radiation through a thin pictorial layer. This effect is a consequence of both lower NIR absorption and reduced NIR scattering due to the particle size smaller than the wavelength, whilst near-infrared transmittography (NIRT) provides information about the internal layers, i.e. the fibre distribution. Stefano Sfarra 12/22

13 Stefano Sfarra 3.IV. Ultraviolet (UV) imaging A repainted front drivers side fender on a Toyota Prius: left – color, right – near-UV Grease stains on countertop: left – color, right – near-UV 13/22

14 Stefano Sfarra 4. The Canny edge detector The Canny edge detector is an edge detection operator that uses a multi-stage algorithm to detect a wide range of edges in images. It was developed by John F. Canny in Canny's aim was to discover the optimal edge detection algorithm. In this situation, an "optimal" edge detector means:edge detectionalgorithmJohn F. Canny good detection – the algorithm should mark as many real edges in the image as possible. good localization – edges marked should be as close as possible to the edge in the real image. minimal response – a given edge in the image should only be marked once, and where possible, image noise should not create false edges. To satisfy these requirements Canny used the calculus of variations – a technique which finds the function which optimizes a given functional. The optimal function in Canny's detector is described by the sum of four exponential terms, but it can be approximated by the first derivative of a Gaussian.calculus of variations functionfunctionalexponentialderivative Gaussian The Canny algorithm contains a number of adjustable parameters, which can affect the computation time and effectiveness of the algorithm. For example, it is possible to remember: the size of the Gaussian filter and the thresholds. 14/22

15 Stefano Sfarra 5. Experimental results 1/6 Figure: (a) the painted sample; NIR result working with a filter at: (b) 715 nm, (c) 850 nm, (d) 1000 nm; (e) UV result; NIR result working with a LED lamp at: (f) 850 nm, (g) 940 nm. Seeing the NIR and UV results inherent the α side (Figs. b-g) and comparing them with the VIS image (Fig. a), no information can be retrieved about the preparatory drawing and the authors signature, that is positioned at the bottom right corner of this side. 15/22

16 5. Experimental results 2/6 Stefano Sfarra More information about the inner defects linked to the α and β sides are identifiable using the stimulated infrared thermography approach (Figs. a, b). In fact, all the defects (from α 1 to α 5 ) are detected in Fig. a, while two subsurface defects (β 1 and β 4, i.e. the smaller defects of the β side) are not detectable in Fig. b. Very interesting to note: 1) the presence of two unknown defects, surrounded by red dotted ovals in Fig. b, probably located inside the wooden support, and 2) the red colour of the α 5 defect (Fig. a) if compared to the other defects. Figure. Stimulated infrared thermography results: (a) α side, and (b) β side. 16/22

17 Stefano Sfarra 5. Experimental results 3/6 A comparison among NDT has involved the use of the DE (Figs. a, b) and SH techniques with the latter pattern of fringes enhanced by the Canny edge detector (Figs. c, d). Several fabricated defects can be detected using the integrated approach, as well as the use of the Cannys algorithm (Fig. c, d) seems very promising to exalt the position and the shape of the defects, if compared to the classical DE holographic recording (Figs. a, b). Figure. α side: (a) 1 st configuration: DE-HI t exp = 3 s – heating time = 4 min, (b) 2 nd configuration: DE-HI t exp = 3 s – heating time = 3 min; β side: (c) 1 st configuration: SH-HI, (d) 2 nd configuration: SH-HI. 17/22

18 Stefano Sfarra 5. Experimental results 4/6 Comparing the results coming from the stimulated thermography and the active thermography methods, it seems that the first one appears more suitable to retrieve all the subsurface defects; also in the latter case, an interesting link is due to the α 5 defect characterized to a bright spot, i.e. the most visible defect detected. The same consideration is adapt to link the β 3 and β 7 defects of the β side. Figure. Active thermography results - α side: (e) PCT-EOF4, β side: (f) PCT-EOF3. 18/22

19 5. Experimental results 5/6 Stefano Sfarra Since the depth of the defects are known and identical, i.e. they are located 2.5 mm beneath the paint surface, the defects depth was also calculated from a relationship of the form: where f b [Hz] is the blind frequency defined as the limiting frequency at which a defect located at a particular depth presents enough (phase or amplitude) contrast to be detected on the frequency spectra. Defect contrast is enhanced using the phase allowing deeper probing. Conventional experimental C 1 values when using the phase from lock-in thermography experiments range between 1.5 to more than 2 with a value of C 1 = 1.82 typically adopted in experimental studies. PPT results agree with these numbers. In this way, the inversion problem in PPT is reduced to the estimation of f b from the phase, while α can be considered as a combined diffusivity (α c ), according to: It is well-known that noise content present in phase data is considerable, especially at high frequencies. This causes a problem for the determination of the blind frequency. A de-noising step is therefore often required. The combination of PPT and TSR has proven to be very effective for this matter, reducing noise and allowing the depth retrieval for a defect. Taking into account the data obtained working with the phase, as well as the input data, i.e. Δt = 1 s, heating time = 300 s, cooling time = 900 s, processed thermograms = 600, f max = 0.5 Hz, f b = 0.07 Hz, the estimated depth is 2.4 mm, very close to the real depth. 19/22

20 5. Experimental results 6/6 Stefano Sfarra Figure. α side: (a) thermogram at t = 3 s with cold image subtracted, (b) temperature evolution for two reference points marked in (a), (c) phase contrast curve: Δ ϕ = ϕ defected – ϕ sound, (d) phasegram at f = Hz linked to the red square detail in (a), (e) thermographic signal reconstruction (TSR) – 5 th order, and (f) phase-frequency curve. α 3 defect, was chosen as the reference defect for the depth retrieval with the phase, considering the difficulty of detecting its position using the PCT technique. 20/22

21 Stefano Sfarra 6. Conclusions In the cultural heritage field, there is an interest in inspecting objects having a complex surface. From the present research, several conclusions can be drawn: a) the large amounts of energy absorbed by the carbon of the pigments used in the drawing is testified from the results and taking into account that the underdrawings were realized using graphite, b) the processing of the data inherent the stimulated infrared thermography approach, it is very effective in order to retrieved the position of the subsurface defects, c) the use of the Cannys algorithm on the SH results can be considered an interesting application both to enhance the contrast of the fringes pattern and to underline the shape of the defects, d) the integration between the PCT technique and the stimulated infrared thermography method has been able to characterize the presence of the α 5 defect as dissimilar to the other defects at least in the nature, e) the deviation [% error = 4% = (z est – z)/z] between the real depth of the defects and the depth retrieved with the phase analysis provides a useful information to the restorer before the starting of the restoration procedure. 21/22

22 Stefano Sfarra 22/22


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