capability of acoustic emission technique for inspecting manual arc

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JOURNAL OF ENGINEERING RESEARCH AND APPLIED SCIENCES (JERAS) 4th Edition –Volume (1) - December 2017, ( Hoon – Libya )

CAPABILITY OF ACOUSTIC EMISSION TECHNIQUE FOR INSPECTING MANUAL ARC WELDING DEFECTS Abdulhakeem B. Miskeen a 1, Mousa Mayb 2, Taha Abdullahc 2 1. 2.

Agriculture Engineering Department, Faculty of Agriculture, Sebha University Materials Engineering Department, Faculty of Energy and Mining Engineering, Sebha University

a-

[email protected], b- [email protected], c- [email protected]

ABSTRACT The aim of this work was to investigate the existence of defects occurring in an arc welding process. An acoustic emission technique was used for online monitoring during manual arc welding of a mild steel plate of dimension 20 cm x 15 cm x 0.5 cm. To capture the acoustic signals emitted from the real-time welding process, a sensor, located at the surface of the steel plate, analyzer and software PHYSICAL AE-Win 3.1 were utilized. The analysis of these signals was carried out using Matlab software, which was based on time. The parameters considered are RMS amplitude, peak amplitude and energy amplitude. Values of these parameters were compared to identify good and poor welds. The results exhibit variation between AE parameters, where the parameters for poor weld were greater in value than for the good weld. Moreover, locations of higher value parameters was determined and cut out for inspection using typical metallographic methods to determine the type of defects in the weld. The signal analysis and optical microscope inspection show that the signals associated with the strong weld contain high amplitude pulses; a medium strength weld shows a greater incidence of high amplitude pulses while a weak weld has the highest amplitude pulses than the strong weld. This study proved that that acoustic emission technique can be used for online monitoring of arc welding process and it is applicable of detecting crack, porosity, lack of sidewall and lack of penetration online. Keywords: Mild Steels, Acoustic Emission, Are Welding Process.

‫الخالصة‬ ‫ش‬١ٍّ‫د طليع أػٕخء ػ‬ٛ١‫ حوظ٘خف ػ‬ٟ‫) ف‬AE ( ‫ش‬١‫ط‬ٌٜٛ‫ش حالٔزؼخػخص ح‬١ٕ‫ش حٓظويحَ طم‬١ٔ‫يف ِٓ ٌ٘ح حٌزلغ حٌظلمك ِٓ اِىخ‬ٌٙ‫ح‬ ١‫الٌظمخ‬.ُٓ 0.5 × ُٓ 15 × ُٓ 20 ‫ أرؼخى‬ًٚ َٞ‫الً حٌط‬ٛ‫ف ِٓ حٌف‬ٌٛ ٍٝ‫ طّض حٌظـَرش ػ‬.ٟ‫َرخث‬ٙ‫ّ حٌى‬ٛ‫ رخٌم‬ٞٚ‫ي‬١ٌ‫حٌٍلخَ ح‬ َ‫لي حٓظوي‬ٚ ،َ‫ش ٌٍلخ‬َٟ‫الً حٌّؼ‬ٛ‫كش حٌف‬ٌٛ ‫ ٓطق‬ٍٝ‫خُ حٓظ٘ؼخٍ ػ‬ٙ‫غ ؿ‬ٟٚ ُ‫ ط‬، َ‫ش حٌٍلخ‬١ٍّ‫ش حٌّٕزؼؼش ِٓ ػ‬١‫ط‬ٌٜٛ‫حإلٗخٍحص ح‬ ‫وخٔض‬ٚ ، Matlab ‫ً ٌٖ٘ حإلٗخٍحص لّٕخ رخٓظويحَ رَٔخِؾ‬١ٍ‫ ٌظل‬ٚ . PHYSICAL AE-Win 3.1 ‫رَٔخِؾ‬ٚ ًٍ‫ِل‬ energy amplitude ٚ peak amplitude , RMS ٟ‫ؼ‬١‫ حٌظَر‬٢ٓٛ‫ حٌـيٍ حٌّظ‬ٟ٘ ‫حٌّؼخِالص حٌّٔظويِش‬ ‫غ‬١‫ ك‬،AE ‫ٓ لَحء حص ِؼخِالص‬١‫َ ر‬١‫أ ظَ٘ص حٌٕظخثؾ فَق وز‬ٚ .‫ت‬١ٌٔ‫ي ِٓ ح‬١‫ي حٌٍلخَ حٌـ‬٠‫ُ ٌٖ٘ حٌّؼخِالص ٌظلي‬١‫طّض ِمخٍٔش ل‬ٚ ُ١‫ حٌم‬ًٚ ‫حلغ حٌّؼخِالص‬ِٛ ‫ي‬٠‫ طُ طلي‬،‫ ًٌه‬ٍٝ‫س ػ‬ٚ‫ػال‬ٚ .‫ي‬١‫ّش ِؼخِالص حٌٍلخَ حٌـ‬١‫ ل‬ٟ‫ت أوزَ ف‬١ٌٔ‫وخٔض حٌّؼخِالص ٌٍلخَ ح‬ ‫ً حإلٗخٍحص‬١ٍ‫رظل‬ٚ .َ‫د حٌّٔظليػش ٌٍلخ‬ٛ١‫ع حٌؼ‬ٛٔ ‫ي‬٠‫ حٌّؼخىْ ٌظلي‬ٚ‫ذ فل‬١ٌ‫ٖ رخٓظويحَ أٓخ‬١‫خ ٌٍظفظ‬ٙ‫لطؼ‬ٚ ‫ف‬ٌٍٛ‫ ح‬ٍٝ‫ ػ‬ٍٝ‫حألػ‬ ‫حإلٗخٍحص‬ ٚ ،‫ش‬١ٌ‫ ِؼخِالص ػخ‬ٍٝ‫ ػ‬ٞٛ‫ي طلظ‬١‫َص أْ حإلٗخٍحص حٌَّطزطش رخٌٍلخَ حٌـ‬ٙ‫ أظ‬ٟ‫ث‬ٛ٠ٌ‫َ ح‬ٙ‫ حٌى٘ف طلض حٌّـ‬ٚ ‫ ِمخٍٔش‬ٍٝ‫ُ حٌّؼخِالص حألػ‬١‫ ل‬ٍٝ‫ ػ‬ٞٛ‫لظ‬٠ ‫ت‬١ٌٔ‫ٓ حْ حٌٍلخَ ح‬١‫ ك‬ٟ‫ ف‬ٍٝ‫ ِؼخِالص أػ‬ٍٝ‫ ػ‬ٞٛ‫ي طلظ‬ٛ‫حٌَّطزطش رخٌٍلخَ حٌّمز‬ ‫حٓطش‬ٛ‫ ر‬ٞٚ‫ي‬١ٌ‫ش حٌٍلخَ ح‬١ٍّ‫خ ٌَّحلزش ػ‬ِٙ‫ّىٓ حٓظويح‬٠ )AE ( ‫ش‬١‫ط‬ٌٜٛ‫ش حالٔزؼخػخص ح‬١ٕ‫أػزظض ٌٖ٘ حٌيٍحٓش أْ طم‬.‫ي‬١‫رخٌٍلخَ حٌـ‬ .‫ش‬١ِ‫ حٌّٔخ‬،‫يع‬ٌٜ‫ك ٌٍى٘ف ػٓ ح‬١‫لخرٍش ٌٍظطز‬ٚ ّٛ‫ٌلخَ حٌم‬

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‫ش‬١‫م‬١‫َ حٌظطز‬ٍٛ‫حٌؼ‬ٚ ‫ش‬١ٓ‫ٕي‬ٌٙ‫ع ح‬ٛ‫ِـٍش حٌزل‬ ) ‫خ‬١‫ز‬١ٌ – ْٛ٘ ( 2017 َ‫ّٔز‬٠‫) – ى‬1( ‫حٌؼيى حٌَحرغ – حٌّـٍي‬

JOURNAL OF ENGINEERING RESEARCH AND APPLIED SCIENCES (JERAS) 4th Edition –Volume (1) - December 2017, ( Hoon – Libya )

cracks and other defects caused by welding using acoustic emission (AET) has been widely used [5.6.7.8].Compared to conventional inspection methods, the Acoustic emission technique has many advantages. This method is relatively low cost, high sensitivity and can predicate early and rapid detection of defects, flaws, cracks, possibility to perform a continuous investigation of the material, processes can be monitored at the time on place of accurance, and large structure can be monitored by limited number of sensors [5.9].Acoustic emission techniques detect transient acoustic waves generated by a sudden change in the local stress field in a material. Detectable waves in this technique are generated by stress changes in microscopic regions[8]. The cracking of a single metallic grain or the interface between two such grains will generate a transient acoustic wave or acoustic emission. The use of acoustic emission in nondestructive testing (NDT) is based upon the fact that crack tips have high stresses and will grow, producing acoustic emission, at lower overall stress levels than necessary to introduce damage in the unflawed material[5]. Flaws which are located in low stress regions will not grow or produce acoustic emission. Thus, flaws whose size, location, or orientation do not affect the strength of the structure under normal load will not produce acoustic emission during a small overload [1].In the current work, acoustic waves were measured in the surrounding air and in the mild steel substrates being welded by employing a microphone and PZT sensor. The results indicate that the arc sound exhibits distinct characteristics for each welding situation and that the main source of acoustic waves is arc reignition. From acoustic signals one can easily assess process stability and detect welding conditions resulting in weld defects online.

1. Introduction It well known that welding is a fabricationprocess that joins materials, usually metals or thermoplastics, by causing coalescence. This is often done by melting the workpieces and adding a filler material to form a pool of molten material (the weld puddle) that cools to become a strong joint. Many different energy sources can be used for welding, including a gas flame, an electric arc, a laser, an electron beam, friction, and ultrasound. While often an industrial process, welding can be done in many different environments, including open air, underwater and in outer space, regardless of location [1]. It is very important when carrying out any welding to ensure that it is done correctly. It is well known that when a solid is subjected to stress at certain levels, discrete acoustic wave packets are generated which can be detected. The phenomenon of sound generation in materials under stress is termed Acoustic Emission (AE). AE are the high frequency stress waves generated by the rapid release of energy that occurs within a material during crack growth, plastic deformation, or phase transformation. AE is a method for observing rapid dynamic material processes with elastic waves [2]. Materials that are to be welded have to tolerate severe thermal transients created by the welding process without suffering deterioration of their mechanical properties or adverse phase changes. Defects during welding process can be attributed to some factors, such as the welder‟s inability to set up and manipulate the welding equipment; although bad joint design and faulty welding equipment can also be responsible[3]. The most significant defects are porosity, slag inclusions, incomplete fusion and penetration, weld profile (underfilling and undercutting), surface damage, and cracks [4].Many NDT techniques were used to investigate the quality of welding processes. Detecting of

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‫ش‬١‫م‬١‫َ حٌظطز‬ٍٛ‫حٌؼ‬ٚ ‫ش‬١ٓ‫ٕي‬ٌٙ‫ع ح‬ٛ‫ِـٍش حٌزل‬ ) ‫خ‬١‫ز‬١ٌ – ْٛ٘ ( 2017 َ‫ّٔز‬٠‫) – ى‬1( ‫حٌؼيى حٌَحرغ – حٌّـٍي‬

JOURNAL OF ENGINEERING RESEARCH AND APPLIED SCIENCES (JERAS) 4th Edition –Volume (1) - December 2017, ( Hoon – Libya )

2. Experimental Procedures Two experimental methods were conducted to monitor the weld defects. The first was the use of AE technique test to capture the signals and these signals have been further analyzed using MATLAB. The second was metallography inspection tests to confirm the types of defect that occurred. 2.1 Specimen Preparation The dimension of the sample for this work was taken from a study by [4], based on an international standard (AWS), regardless of the thickness, a mild steel of 200mm x 150mm x 5mm dimensions were used.The plates were cut using plasma cutting machine. The specimenswere well cleaned to ensure no surface variations and a uniform surface finish.Weld edge preparation was carried out carefully. A land of 1mm was provided and a bevel angle of 30 ˚ was maintained. The plates were butted together to form a single „V‟ groove with a weld angle of 60˚. A root gap of 1mm was maintained, see Figure 1.

has a current range of 20-160 Amps, 40 amp used for this work.The electrode used was E 6013 of diameter 2.5mm and length 350mm, according to the AWS A5.1-specification for carbon steel electrodes for shielded metal arc welding. Welding was carried out in the (1G) as named by AWS or (PA) by ISO 6947 position. For acoustic emission, a sensor used is a wide band acoustic sensor, see Figure 2. This sensor made by piezoelectric material and has frequency range of 100 kHz to 1MHz, since in most cases acoustic emission signal appear between this ranges. The sensor was located and covered at the location of interest as shown in Figure 3 to ensure that no spatter will damage it.In order to capture good signal and reach the maximum detection, grease was used between the sensor and the surface of the plate. The signal obtained from the sensor needed to be amplified before can be analyzed using suitable software. The pre-amplifier used was 1220A. This amplifier have two options of gain, 40 dB and 60 dB, for the current work 40 dB have been used.

Figure 2 - Wide band acoustic sensor

Figure 1. Typical weld joint design.

2 Test and Tools Preparation The weldedjoint was made using an Inverter ES 1600 arc-welding machine of welding industries (Malaysia) module. This machine 4

‫ش‬١‫م‬١‫َ حٌظطز‬ٍٛ‫حٌؼ‬ٚ ‫ش‬١ٓ‫ٕي‬ٌٙ‫ع ح‬ٛ‫ِـٍش حٌزل‬ ) ‫خ‬١‫ز‬١ٌ – ْٛ٘ ( 2017 َ‫ّٔز‬٠‫) – ى‬1( ‫حٌؼيى حٌَحرغ – حٌّـٍي‬

JOURNAL OF ENGINEERING RESEARCH AND APPLIED SCIENCES (JERAS) 4th Edition –Volume (1) - December 2017, ( Hoon – Libya )

These areas are circled by red color. Although the non-circled areas appeared to be good weld as well, however once the welding area (pattern) had been cleaned and theflux was removed; only the circled areas shows a good weld.

Figure 3 - The location of the sensor on mild steel welded specimen For the analysis of the result data, the ADC (Analog to Digital Convertor) card have been used to convert the analog to digital since the computer can only read the digital signal. The card was already built-in inside the computer. Physical Acoustic AE-Win 3.1 is the software that was used for signal capturing process. This software is able to interpret the acoustic waves that are collected from the experiment as a data and convert it into an understandable form displayed at computer. MATLAB software was used to analyze the experimental results data. The schematic illustrated in figure 4, shows complete and ready to run experiment‟s set up for the first test.

Figure 5 - Welded patterns; the red circle shows the interested areas to be analyzed 3.1 Signal analysis AE signals were taken from online arc welding process where the sensor was located at the surface of the test sample for capturing process. The signals were analyzed in time domain. Three parameters have been used in time domain, which were RMS amplitude, peak amplitude and the energy. This procedure have collected a hundreds of signals, however only 13 signals were taken to be analyzed, other signals have been ignored due to the poor welding profile. The 13 signals have been recorded using the computer and analyzed using MATLAB software. The analysis of signals was based on the fact that was found by W. D,that the more sizeable bursts were related to the growth of microfissures and larger cracks [10]. This was confirmed by Williams, (1982), he found that the emission associated with the strong weld contain only few high amplitude pulses; a medium strength weld shows a greater incidence of high amplitude pulses while a weak weld has many high amplitude pulses than the strong weld [11]. Thus, the maximum values of the amplitudes of the signals were expected to be related to areas where defects

Figure 4 - Schematic of the experimental setup.

3. Result and discussions Due to the lack of skill in the welding process that was used, the entire weld had only few areas that appeared to be acceptable. Figure 5, shows the areas of the weld that had been analyzed and inspected.

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‫ش‬١‫م‬١‫َ حٌظطز‬ٍٛ‫حٌؼ‬ٚ ‫ش‬١ٓ‫ٕي‬ٌٙ‫ع ح‬ٛ‫ِـٍش حٌزل‬ ) ‫خ‬١‫ز‬١ٌ – ْٛ٘ ( 2017 َ‫ّٔز‬٠‫) – ى‬1( ‫حٌؼيى حٌَحرغ – حٌّـٍي‬

JOURNAL OF ENGINEERING RESEARCH AND APPLIED SCIENCES (JERAS) 4th Edition –Volume (1) - December 2017, ( Hoon – Libya )

are present, therefore it needed to be cut for metallographic inspection.

Table 1 -The average RMS amplitude for the signals concerned.

3.1.1 RMS (Root Mean Square) Values The experimental result and analysis for the average RMS amplitudes for different signals are summarized in Figure 6. Where the signals 67, 73, 88 and 97 indicated the highest RMS amplitude among the entire 13 signal, the values of highest RMS amplitude for these signals are 0.0028, 0.0026, 0.0027 and 0.0026 respectively, see Table 1, which indicated a serious defect as based on the fact mentioned earlier. However, the result also indicated that the signals 84 and 93 have lower RMS value comparing with that in bad welding result. The values of lower RMS amplitude for these signals are 0.0020 and 0.0022, see Table 1.These signals indicated RMS value of good welding results.

Distance (mm)

Signal

Average RMS amplitude

60-70

67

0.0028

70-80

73

0.0026

80-90

84

0.0020

80-90

88

0.0027

90-100

93

0.0022

90-100

97

0.0026

Peak Amplitude values 3.1.2 The experimental results of average peak amplitudefor different signals are plotted in Figure 7.The analysis of these signals also showed the same raise of the value.These can be attributed as series of welding defects (bad weld).The average peak amplitude belongs to the same signals that indicate the highest RMS, which were 67, 73, 88 and 97. In addition, a few other signals indicated the same raise of the average peak amplitude value which were 80, 83, 89 and 96, and that means more activities have occurred. Table 2 shows the average peak amplitude for these signals only. However, two signals were suspected to be inspected for good weld, high peak amplitude of 0.0076 and 0.0077 were appeared at signal 84 and 93, see Figure 7. These peak amplitude indicates a lesser activity have occurred.

Figure 6 - Different RMS amplitude values for the ignored signals and signals for bad and good welding

Figure 7 Different Peak amplitude values for the ignored signals and signals for bad and good welding

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‫ش‬١‫م‬١‫َ حٌظطز‬ٍٛ‫حٌؼ‬ٚ ‫ش‬١ٓ‫ٕي‬ٌٙ‫ع ح‬ٛ‫ِـٍش حٌزل‬ ) ‫خ‬١‫ز‬١ٌ – ْٛ٘ ( 2017 َ‫ّٔز‬٠‫) – ى‬1( ‫حٌؼيى حٌَحرغ – حٌّـٍي‬

JOURNAL OF ENGINEERING RESEARCH AND APPLIED SCIENCES (JERAS) 4th Edition –Volume (1) - December 2017, ( Hoon – Libya )

Table 2 - The average peak amplitude for the signals concerned Distance (mm)

Signal

Peak amplitude (V)

60-70

67

0.0082

70-80

73

0.0082

80-90

84

0.0076

80-90

88

0.0082

90-100

93

0.0077

90-100

97

Table 3 - shows the energy values for the signals indicated in this discussion only (poor and good welds). Small differences in the energy values between the signals were noticed, which means the signals have different energy generation, the higher energy the more the activity. On another hand, the raise of energy predict that the maximum the energy the stronger the defect. Table 3 - The energy values of the signals concerned. Distance (mm)

Signal

Energy (J)

60-70

67

9.8089

70-80

73

8.5296

80-90

84

7.3227

80-90

88

9.5225

90-100

93

8.8206

90-100

97

9.1602

0.0082

3.1.3 Energy Analysis The experimental results of average energy values for all signals are plotted in Figure 8. Energy analysis also showed the same activity of RMS and peak amplitude, the raise of energy in signals 67, 73, 88, and 97 confirmed that within these signal there were activities taking place at the same signal and at the same location. This assumption will be confirmed later by metallographic inspection.

The maximum energy values of signals 67, 73, 88, 93 and 97 were at the same signals as mentioned in RMS and peak amplitude analysis. The maximum energy among these four signals was from the signal 67 which was associated with rise in peak amplitude and RMS amplitude. This signal is expected to have much more activity or a serious defect and visual inspection test will confirm that. However, the energy value is lower in good welding comparing with that in bad welding. The signal 84showed less energy value. The signal in a good welding have less activity of energy comparing with that in bad welding.

Figure 8 - Different energy values for the ignored signals and signals for bad and good welding.

3.1.4 Metallography Analysis As mentioned above, only few locations had been cut to be visually inspected, and since all inspected specimen have incomplete was 7

‫ش‬١‫م‬١‫َ حٌظطز‬ٍٛ‫حٌؼ‬ٚ ‫ش‬١ٓ‫ٕي‬ٌٙ‫ع ح‬ٛ‫ِـٍش حٌزل‬ ) ‫خ‬١‫ز‬١ٌ – ْٛ٘ ( 2017 َ‫ّٔز‬٠‫) – ى‬1( ‫حٌؼيى حٌَحرغ – حٌّـٍي‬

JOURNAL OF ENGINEERING RESEARCH AND APPLIED SCIENCES (JERAS) 4th Edition –Volume (1) - December 2017, ( Hoon – Libya )

assumed that there were no defects, but only a lack of penetration, which was ignored as a defect for this work. The location where the cutting process is applied was based on the result from AE signal where the raise of the value of the parameters from first experiment is suspected. An optical microscope with magnification capability of 10X was used for this experiment to detect the microdefects such as initial crack and porosity.

distance between 70-80 mm from the start point; this value indicated that there is a defect. Visual inspection test confirmed that there is a crack at this point, see Figure 10. Figure 10 (1, 2, 3) Cracks in welded sample For the signal 73 at the distance between 90-

Figure 9 Prepared specimen for metallographic inspection The highest values of RMS amplitude, peak amplitude, the energy were generated from the same signals; 67, 73, 88 and 97. Since a crack is a first class defect and it is not allowed for any inspection test; meaning that if any tested pattern got crack it will be rejected from production line immediately. The maximum value for the all parameters in time domain was from the signal 67 at

100 mm another crack was noticed and it was associated with porosity, however the crack was less danger comparing with first one. These defects were noticed under the microscope. Figure 11 shows the crack, the porosity and their locations For the signal 88 at the distance between 110-120 mm from the start point, a microcrack occurred, this crack shows lower amplitude value than the first two cracks which means, it is smaller crack in dimension and length as well. Figure 12 a) and b) illustrates the crack

Figure 11- (a) The inspected specimen, (b) porosity and (c) crack under the microscope

Figure 12- (a) the crack, (b) the weld joint 8

‫ش‬١‫م‬١‫َ حٌظطز‬ٍٛ‫حٌؼ‬ٚ ‫ش‬١ٓ‫ٕي‬ٌٙ‫ع ح‬ٛ‫ِـٍش حٌزل‬ ) ‫خ‬١‫ز‬١ٌ – ْٛ٘ ( 2017 َ‫ّٔز‬٠‫) – ى‬1( ‫حٌؼيى حٌَحرغ – حٌّـٍي‬

JOURNAL OF ENGINEERING RESEARCH AND APPLIED SCIENCES (JERAS) 4th Edition –Volume (1) - December 2017, ( Hoon – Libya )

high amplitude pulses while a weak weld has the highest amplitude pulses than the strong weld.

The raise of the amplitudes and energy values for signal 97 at 120-130 mm was because of porosity occurred while welding process, and no major defects were found, Figure 13 (a and b). Figure 13 (a) high magnification image showing the porosity and (b) low magnification image showing the defected area. The rest of the signals which predicted low activities may be linked to lack of penetration or lack of side wall fusion as shown in figure 14. The signals for these type of defects showed neither rise in the amplitudes nor the energy in both time and frequency domain. For example signal 41at the distance of 50-60 mm shows lower amplitudes and energy than the good weld, it shows a big cavity of lack of side wall fusion defect.

5. REFERENCES 1. The industrial design engineering Wiki2017.ttp://wikid.eu/index.php/Wel ding [10January 2017]. 2. Xiaojin li. 2000. A study of hot-tearing during solidification of aluminum alloys via the acoustic emission method. 3. John Dyson Gowelding. 2016. http://www.gowelding.com/weld/failure /flailure.htm, [15 Novomber 2016]. 4. Serope Kalpakjin & Steven Schmid. 2006. Manufacturing Engineering technology, fifth Edition in SI Units. 5. The McGraw-Hill Companies 2003. Acoustic emission testing, chapter 10. 6. A.K. Rao. 1990. Acoustic emission and signal analysis. Department of Aerospace Engineering, Indian Institute of Science, Bangalore-560012 7. Theodore Hopwoodand James H. Havens 1978. Acoustic emission monitoring of weldments.Department of transportation. American Society for Testing Materials 8. P. Mazal1, L. Pazdera2, L. Kolar1 2005. Advanced acoustic emission signal treatment in the area of mechanical cyclic loading. The 8th International Conference of the Slovenian Society for Non-Destructive Testing 9. Dr. Gary martin & john Dimopoulos. 2006. Acoustic emission monitoring as a tool in risk based assessments. 12th aPCNDT 2006 – Asia-pacific conference on NDT, 5th – 10th NOV 2006, Auckland, New Zealand. 10. Jolly, W. D.1969,the application of acoustic emission to in-process weld inspection. Battele-northwest institute. 11. J.H. Williams Jr., D.M. DeLonga, and S.S. Lee, 1982"Correlations of Acoustic Emission with Fracture Mechanics Parameters in Structural Bridge Steels During Fatigue,"

Figure 14 lack of side wall fusion 4. Conclusions 1- AE technique was found to be applicable of detection of some common defects, which were crack, porosity, lack of side wall and lack of penetration online. It was proved by both tests, where the defects were suspected in signal analysis tests and then confirmed by the metallographic inspection test. 2This work proved that the more sizeable bursts were related to the growth of cracks and porosity. In other word, the signals associated with the strong weld contain high amplitude pulses; a medium strength weld shows a greater incidence of 9

‫ش‬١‫م‬١‫َ حٌظطز‬ٍٛ‫حٌؼ‬ٚ ‫ش‬١ٓ‫ٕي‬ٌٙ‫ع ح‬ٛ‫ِـٍش حٌزل‬ ) ‫خ‬١‫ز‬١ٌ – ْٛ٘ ( 2017 َ‫ّٔز‬٠‫) – ى‬1( ‫حٌؼيى حٌَحرغ – حٌّـٍي‬

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