Nondestructive Testing of Metal Parts by Using ... - Science Direct

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ScienceDirect Procedia Engineering 177 (2017) 562 – 567

XXI International Polish-Slovak Conference “Machine Modeling and Simulations 2016”

Nondestructive testing of metal parts by using infrared camera Zuzana Stankovičováa*, Vladimír Dekýša, František Novýb, Pavol Nováka a Department of Applied Mechanics, Faculty of Mechanical Engineering, University of Žilina, Univerzitná 1, 010 01 Žilina, Slovak Rep. Department of Material Engineering, Faculty of Mechanical Engineering, University of Žilina, Univerzitná 1, 010 01 Žilina, Slovak Rep.

b

Abstract The paper deals with non-destructive testing of metal parts excited by ultrasound. Material response to this excitation is detected by infrared camera. Damages in the material, such as crakes and cavities are identified based on temperature changes detected by infrared camera. The paper presents detection of cracks on the real steel form used in the production of aluminium castings when cracks resulting from thermal fatigue. There are also discussed some problems associated with the using of this technology. 2017The TheAuthors. Authors. Published by Elsevier Ltd. is an open access article under the CC BY-NC-ND license © 2017 © Published by Elsevier Ltd. This (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of MMS 2016. Peer-review under responsibility of the organizing committee of MMS 2016 Keywords: NDT; metal; IR camera; defect;

1. Introduction Nondestructive testing (NDT) is the process of inspecting, testing or evaluating materials or components for discontinuities without deconstruction or damage of the system. This definition of nondestructive testing includes thermal evaluation using infrared camera. Thermal nondestructive evaluation is divided into two different approaches: passive and active thermography. Passive thermography is steady-state displaying surface thermal fields of electrical and mechanical parts. The temperature of the object is recorded without the need of external stimulation, as in the active thermography. Active thermography evaluates dynamic temperature changes. An energy source is required to produce a thermal contrast between the material and the background. The way how the heat spreads object is also reflected on the surface temperature. Therefore, it is possible to examine the materials below the surface. It is evaluated the response and evolution of the temperature field to the heat excitation. If a defect in a material absorbs the injected waves, it will locally heat up. The resulting temperature gradient on the material surface is measured by infrared camera.

* Corresponding author. Tel.: +421-41-513-2965; fax: +421-41 -5940. E-mail address: [email protected]

1877-7058 © 2017 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license

(http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of MMS 2016

doi:10.1016/j.proeng.2017.02.261

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Zuzana Stankovičová et al. / Procedia Engineering 177 (2017) 562 – 567

Active thermography can be divided into several parts depending on the stimulation method. One of the most popular methods of active thermography is lock-in thermography [1]. 2. Lock-in thermography Lock-in thermography is also known as thermal wave imaging. It is described by theory of oscillating waves. Heat performance occurs periodically with a lock-in frequency. Local surface temperature is evaluated and averaged over a number of periods. The advantage of this method is that due to averaging the sensitivity of the measurement can be improved. Because of averaging it needs a long measure time, but on the other hand the statistical noise is extracted. Lock in method can be described as a multiplication of detected signal F(t) by a weighting factor K(t). Usually this process is called lock-in correlation procedure. Output signal S for synchronous correlation is obtained by linear averaging over an integration time ‫[ ݐ݊݅ݐ‬2]: ܵൌ

ͳ ‫ݐ݊݅ݐ‬

‫ݐ݊݅ ݐ‬

න ‫ܨ‬ሺ‫ݐ‬ሻ‫ܭ‬ሺ‫ݐ‬ሻ݀‫ ݐ‬Ǥ

(1)

Ͳ

The correlation function optimum to achieve the best signal to noise ratio is the harmonic function. When we use sine wave with amplitude A and its phase ߔ we get [2]: ‫ܨ‬ሺ‫ݐ‬ሻ ൌ ‫݊݅ݏܣ‬ሺʹߨ݂݈‫ ݇ܿ݋‬െ݅݊ ‫ ݐ‬൅ ߔሻ ൌ ‫݊݅ݏܣ‬ሺʹߨ݂݈‫ ݇ܿ݋‬െ݅݊ ‫ݐ‬ሻܿ‫ ߔݏ݋‬൅ ‫ݏ݋ܿܣ‬ሺʹߨ݂݈‫ ݇ܿ݋‬െ݅݊ ‫ݐ‬ሻ‫ߔ݊݅ݏ‬Ǥ

(2)

Using weight correlation functions: ʹߨሺ݆ െ ͳሻ ‫Ͳ݆ܭ‬ι ൌ ʹ‫ ݊݅ݏ‬൬ ൰ǡ ݊

(3)

ʹߨሺ݆ െ ͳሻ ‫Ͳͻ݆ܭ‬ι ൌ ʹܿ‫ ݏ݋‬൬ ൰Ǥ ݊

(4)

We get two correlations ܵ Ͳι (real component) and ܵ ͻͲι (imaginary component): ܵ Ͳι ൌ ‫ݏ݋ܿܣ‬ሺߔሻǡ

(5)

ܵ ͻͲι ൌ ‫݊݅ݏܣ‬ሺߔሻǡ

(6)

The (3) and (4) are used to calculate amplitude A and phase φ: ‫ ܣ‬ൌ ඥሺܵ Ͳ ሻʹ ൅ ሺܵ ͻͲ ሻʹ ǡ

(7)

And phase φ: ߔ ൌ ܽ‫ ݊ܽݐܿݎ‬ቆ

ܵ ͻͲ ቇǤ ܵͲ

(8)

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3. Lock-in ultrasound thermography Lock-in ultrasound method is suitable for selective detection of faults and defects in the material. The most commonly using is detection of closed and opened fractures, control of rivets, welded joints or for the detection of impact and delamination of composite materials. As a source of excitation in lock-in thermography is most commonly used: x Halogen lamp – to monitor the spread of delamination in composite materials x Ultrasound – to detect the material cracks x Mechanical excitation – to detect the material cracks and thermos-elastic stress analysis

Fig. 1 Principle of ultrasound thermography [3].

In our department there is an ultrasonic source from EDEVIS. The principle of lock-in ultrasound thermography is on figure 1. It is based on the interaction of mechanical and heat waves to detect material defects. Ultrasound transducer is attached to a fixed point. There are waves spread throughout the volume, where are reflected several times until turn into heat. If a defect in material absorbs ultrasound waves causes local heating. A defect differs from the surroundings by thermal gradient, which can be caused by concentration of stress. Hysteresis effect of friction in the cracks may occur during periodic events [3]. In the ultrasound lock-in thermography it is possible to determine the defect depth. Important parameter is thermal diffusion length μ [m], which is used to determine the depth at which discontinuity could be detected [1]: ߤൌඨ

ߙ ǡ ߨ݂

(9)

Where α is thermal diffusivity and f is heat stimulation frequency. Equation (9) expresses that lower frequency penetrate deeper into material than higher frequency of thermal wave


4. Experiment The paper presents detection of cracks on the real steel form used in the production of aluminum castings when cracks resulting from thermal fatigue. It is possible to simulate behavior of material during processing [4, 5, 6]. The main goal of experimental part is crack detection using lock-in ultrasound thermography. For testing FLIR IR camera SC7500 with cooled detector was used. This IR camera has a temperature resolution of 20mK. The camera was attached to ultrasound source which was used to generate harmonic waves passing the specimen with adequate frequency. Specimen using in experiment is on Fig. 2. Yellow rectangle represents investigation area. The specimen used in test is a hot work tool steel W300 specially developed for use in casting with parameters [7], density 7800kg/m3, specific heat 460 J/(kg K), thermal conductivity 26W/(mK).

Fig. 2 Specimen and area of investigation with direction of view A and B.

a)

b) Fig. 3 a) A-view, ܵ Ͳι (real component), detection net of cracks at 0.05Hz, B-view, b) ܵ ͻͲι (imaginary component), detection of one dominant crack at 0.01Hz.

The measurement was made using three frequencies 0.005Hz, 0.01Hz and 0.05Hz. Images of phase differences were processed in commercial software DisplayImg fa Edevis and imported to MATLAB for better manipulation with results. On the Fig. 3 a) is view in direction A on the specimen and by using real component infrared response the net of crack under surface was detected at 0.05Hz and on the Fig. 3 b) is view in direction B on this specimen

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and by using imaginary component infrared response the dominant crack deeper under surface was detected at 0.05Hz. The macro photos with net of cracks and dominant crack was done too, see Fig. 4a, 4b.

a)

b) Fig. 4 a) Macro photos net of cracks b) dominant cracks.

On the Fig. 5 we can see results for frequency 0.005Hz. There was selected an area and displayed 3D behavior of image phase. Red projection indicates the dominant crack. Blue depression is caused by reflection of rounded part. Even if the method used is not sensitive to the exact value of emissivity [8] of the material, it helps to avoid reflection. Fig. 6 images phase image for frequency 0.01Hz. The resolution is lower compared to Fig. 5 at 0.005Hz and higher compared to Fig. 7 at 0.05 Hz. This was caused by diffusion length, which decreases with increasing frequency. The resolution of the phase image on Fig. 5 is the best.

Fig. 5 Phase images at frequency 0.005Hz.




5. Conclusion The paper deals with non-destructive testing of metal parts excited by ultrasound. Material response to this excitation is detected in frequency range 3μm – 5μm by infrared camera. The theory of oscillating waves using in ultrasound lock-in thermography and using of this method on metal form is presented. To the best detection of damage as the cracks in materials was done by using phase, real and imaginary part of complex images. This work has been helpful in assessing cracks in the analyzed object and allowed to focus mainly on areas with cracks. At this time, it has also been solving the problems of modeling crack [9-13]. Acknowledgements This work was supported by the Slovak Research and Development Agency under the contract No. APVV-073612 and by the Slovak Grant agency VEGA 1/0234/13. References [1] X.P.V. Maldague, Infrared and thermal testing, third ed., United States of America, 2001. [2] O. Breitenstein, Lock-in thermography, second ed. , New York, 2010. [3] Ultrasound thermography. Avaliable at : [4] A. Bokota, T. Domański, Modelling and numerical analysis of hardening phenomena of tools steel elements, Archives of Metallurgy and Materials, 54, 3 (2009) 575-587. [5] T. Domanski, W. Piekarska, M. Kubiak, Z. Saternus, Determination of the final microstructure during processing carbon steel hardening, Procedia Engineering, 136 (2016) 77-81. [6] Z. Saternus, W. Piekarska, M. Kubiak, T. Domański, L. Sowa, Numerical analysis of deformations in sheets made of X5CRNI18-10 steel welded by a hybrid laser-arc heat source, Procedia Engineering, 136 (2016) 95 – 100. [7] Böhler, Hot work tool steel W300. Available at: < http://www.bohler-edelstahl.com/media/W300DE.pdf>. [8] M. Honner, P. Honnerova, Survey of emissivity measurement by radiometric methods, Applied Optics, 54 (2015) 669-683. [9] M. Žmindák, Z. Pelagič, Modeling of shock wave resistance in composite solids, Procedia engineering, 96 (2014) 517-526. [10] M. Žmindák, M. Dudinský, Computational Modelling of Composite Materials Reinforced by Glass Fibers, Procedia engineering, 48 (2012) 701-710. [11] M. Žmindák, P. et al, Finite element thermo-mechanical transient analysis of concrete structure, Procedia engineering 65 (2013) 224-229. [12] M. Sága, P. Kopas,M. Uhricik, Modeling and Experimental Analysis of the Aluminium Alloy Fatigue Damage in the case of Bendingtorsion Loading, in: Modelling of Mechanical and Mechatronics Systems, Procedia Engineering, 48 (2012) 599-606. [13] P. Kopas, L. Jakubovičová,M. Vaško,M. Handrik, Fatigue Resistance of Reinforcing Steel Bars, Procedia Engineering, 136 (2016) 193–197.

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