Failure Characterization of Al-Zn-Mg Alloy and Its Weld Using Integrated Acoustic Emission and Digital Image Techniques
Abstract
:1. Introduction
2. Materials and Methods
2.1. Materials and Specimen Preparation
2.2. Experimental Set-Up
2.3. Data Filtering Procedures
3. Results and Discussion
3.1. Microstructural Characterization
3.2. Damage Evolution and AE Behavior
3.3. AE Waveform Characteristics
3.3.1. AE Time-Domain Characteristics
3.3.2. AE Frequency-Domain Characteristics
3.3.3. AE Time-Frequency Domain Characteristics
3.4. AE Source Mechanisms
3.5. Influencing Factors for AE Features
4. Conclusions
- (1)
- AE energy can be used to indicate crack initiation. Digital images obtained by monitoring the notch tip region of an A7N01 aluminum sample confirm the predictions based on AE signals. The prediction based on AE energy was validated by monitoring damage images at the notch tip of the A7N01 aluminum alloy sample.
- (2)
- Frequency–domain characteristic analysis showed that the transformation of centroid frequencies occurred after crack initiation in both the base material and weld seam. The centroid frequencies of the base material were transformed to the low-frequency range of 297~536 kHz, while those of the weld seam shifted to the high-frequency range of 515~660 kHz. Moreover, after crack initiation, new peak frequencies appeared in the frequency range of 112~188 kHz and 242~525 kHz for the base material and weld seam, respectively. Therefore, centroid and peak frequencies can also indicate crack initiation in aluminum alloys.
- (3)
- SEM images of the fractured surfaces indicate that the base metal exhibits smaller dimples and fewer de-lamination defects than the weld. It might be the main reason for the low energy emission, weak signal strength, and low peak amplitude observed in the welded specimens. In contrast, the base metal specimens exhibit more substantial AE energy, higher amplitude, and greater AE event counts.
- (4)
- With the increase in loading rate and notch length, the AE cumulative count of the base metal presented a gradually increasing trend during both the crack initiation and propagation stages. The total AE counts of the base material, heat-affected zone, and weld seam also increased during crack initiation and propagation.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Sun, X.; Han, X.; Dong, C.; Li, X. Applications of Aluminum Alloys in Rail Transportation. In Advanced Aluminium Composites and Alloys; IntechOpen: London, UK, 2021. [Google Scholar]
- Hirsch, J. Recent development in aluminium for automotive applications. Trans. Nonferrous Met. Soc. China 2014, 24, 1995–2002. [Google Scholar] [CrossRef]
- Xu, L.; Wang, Q.; Zhou, M. Micro-crack initiation and propagation in a high strength aluminum alloy during very high cycle fatigue. Mater. Sci. Eng. A 2018, 715, 404–413. [Google Scholar] [CrossRef]
- Reiner, J.; Chen, C.; Vaziri, R.; Poursartip, A. Combining digital image correlation and phased-array ultra-sonics for non-destructive testing of translaminar fracture in composite laminates. Compos. Part A Appl. Sci. Manuf. 2022, 161, 107060. [Google Scholar] [CrossRef]
- He, D.; Kusano, M.; Watanabe, M. Detecting the defects of warm-sprayed Ti-6Al-4V coating using Eddy current testing method. NDT E Int. 2022, 125, 102565. [Google Scholar] [CrossRef]
- Babu, M.N.; Mukhopadhyay, C.K.; Sasikala, G.; Albert, S.K.; Bhaduri, A.K.; Jayakumar, T.; Kumar, R. Study of fatigue crack growth in RAFM steel using acoustic emission technique. J. Constr. Steel Res. 2016, 126, 107–116. [Google Scholar] [CrossRef]
- Shrestha, S.; Kannan, M.; Morscher, G.N.; Presby, M.J.; Razavi, S.M. In-situ fatigue life analysis by modal acoustic emission, direct current potential drop and digital image correlation for steel. Int. J. Fatigue 2021, 142, 105924. [Google Scholar] [CrossRef]
- Carboni, M.; Crivelli, D. An acoustic emission based structural health monitoring approach to damage development in solid railway axles. Int. J. Fatigue 2020, 139, 105753. [Google Scholar] [CrossRef]
- Morscher, G.N.; Maxwell, R. Monitoring tensile fatigue crack growth and fiber failure around a notch in laminate SiC/SiC composites utilizing acoustic emission, electrical resistance, and digital image correlation. J. Eur. Ceram. Soc. 2019, 39, 229–239. [Google Scholar] [CrossRef]
- Ali, S.M.; Hui, K.; Hee, L.; Leong, M.S.; Abdelrhman, A.M.; Al-Obaidi, M.A. Observations of changes in acoustic emission parameters for varying corrosion defect in reciprocating compressor valves. Ain Shams Eng. J. 2019, 10, 253–265. [Google Scholar] [CrossRef]
- Šofer, M.; Cienciala, J.; Fusek, M.; Pavlíček, P.; Moravec, R. Damage analysis of composite CFRP tubes using acoustic emission monitoring and pattern recognition approach. Materials 2021, 14, 786. [Google Scholar] [CrossRef]
- Angela, D.D.; Ercolino, M. Acoustic Emission Entropy as a fracture-sensitive feature for real-time assessment of metal plates under fatigue loading. Procedia Struct. Integr. 2019, 18, 570–576. [Google Scholar] [CrossRef]
- Sagasta, F.; Zitto, M.E.; Piotrkowski, R.; Benavent-Climent, A.; Suarez, E.; Gallego, A. Acoustic emission energy b -value for local damage evaluation in reinforced concrete structures subjected to seismic loadings. Mech. Syst. Signal Process. 2018, 102, 262–277. [Google Scholar] [CrossRef]
- Wu, K.; Jung, W.-S.; Byeon, J.-W. In-situ monitoring of pitting corrosion on vertically positioned 304 stainless steel by analyzing acoustic-emission energy parameter. Corros. Sci. 2016, 105, 8–16. [Google Scholar] [CrossRef]
- Hou, G.; Shang, D.G.; Zuo, L.X.; Qu, L.F.; Guo, Y.E.; Xia, M.; Wu, S.D.; Yin, X. Fatigue damage iden-tification of SiC coated needled C/SiC composite by acoustic emission. Ceram. Int. 2021, 47, 15129–15138. [Google Scholar] [CrossRef]
- Maleki, A.; Saeedifar, M.; Najafabadi, M.A.; Zarouchas, D. The fatigue failure study of repaired aluminum plates by composite patches using Acoustic Emission. Eng. Fract. Mech. 2019, 210, 300–311. [Google Scholar] [CrossRef]
- Qiu, X.; Xu, J.; Xiao, S.; Yang, Q. Acoustic emission parameters and waveforms characteristics of fracture failure process of asphalt mixtures. Constr. Build. Mater. 2019, 215, 135–147. [Google Scholar] [CrossRef]
- Zhang, Z.; Wu, X.; Tan, J. In-situ monitoring of stress corrosion cracking of 304 stainless steel in high-temperature water by analyzing acoustic emission waveform. Corros. Sci. 2019, 146, 90–98. [Google Scholar] [CrossRef]
- Das, A.K.; Suthar, D.; Leung, C.K. Machine learning based crack mode classification from unlabeled acoustic emission waveform features. Cem. Concr. Res. 2019, 121, 42–57. [Google Scholar] [CrossRef]
- Kong, B.; Wang, E.; Li, Z.; Wang, X.; Niu, Y.; Kong, X. Acoustic emission signals frequen-cy-amplitude characteristics of sandstone after thermal treated under uniaxial compression. J. Appl. Geophys. 2017, 136, 190–197. [Google Scholar] [CrossRef]
- Baker, C.; Morscher, G.N.; Pujar, V.V.; Lemanski, J.R. Transverse cracking in carbon fiber reinforced polymer composites: Modal acoustic emission and peak frequency analysis. Compos. Sci. Technol. 2015, 116, 26–32. [Google Scholar] [CrossRef]
- Al-Dossary, S.; Hamzah, R.R.; Mba, D. Observations of changes in acoustic emission waveform for varying seeded defect sizes in a rolling element bearing. Appl. Acoust. 2009, 70, 58–81. [Google Scholar] [CrossRef]
- Jung, D.; Yu, W.-R.; Na, W. Use of acoustic emission b(Ib)-values to quantify damage in composites. Compos. Commun. 2020, 22, 100499. [Google Scholar] [CrossRef]
- Shang, X.; Lu, Y.; Li, B.; Peng, K. A novel method for estimating acoustic emission b value using improved magni-tudes. IEEE Sens. J. 2021, 15, 16701–16708. [Google Scholar] [CrossRef]
Material | Si | Fe | Cu | Mn | Mg | Cr | Ni | Zn | Ti | V | Zr | Al |
---|---|---|---|---|---|---|---|---|---|---|---|---|
A7N01 | <0.001 | 0.087 | 0.032 | 0.377 | 1.049 | 0.089 | <0.001 | 4.23 | 0.029 | 0.014 | 0.083 | Bal. |
Weld Pass | Current (A) | Voltage (V) | Welding Speed (mm/s) | Gas Flow (L/min) |
---|---|---|---|---|
1st | 250 | 24 | 50 | 17 |
2nd | 270 | 24 | 42 | |
3rd | 270 | 24 | 42 | |
4th | 270 | 24 | 42 |
Types of Specimens | Position of the Notch | |
---|---|---|
Type A | Notch in base metal | |
Type B | Notch in weld seam |
Specimen Number | Load Rate (mm/min) | Notch Length (mm) | Peak Loads (KN) | Deflection (mm) | AE Activity | Crack-Related AE Activity | ||
---|---|---|---|---|---|---|---|---|
Sensor 1 | Sensor 2 | |||||||
Base metal | 1 | 1.2 | 2.5 | 8.869 | 10.7 | 8631 | 3665 | 528 |
2 | 8.881 | 11.3 | 7125 | 3113 | 439 | |||
3 | 9.021 | 10.8 | 6565 | 3285 | 408 | |||
4 | 8.956 | 6.1 | 1119 | 315 | 223 | |||
5 | 2.4 | 9.053 | 7 | 1895 | 1775 | 103 | ||
6 | 8.972 | 6.9 | 1976 | 1634 | 105 | |||
7 | 3.6 | 8.910 | 7.1 | 2460 | 1749 | 93 | ||
8 | 8.985 | 7.1 | 2108 | 2114 | 86 | |||
9 | 1.2 | 1.25 | 11.546 | 11.2 | 1507 | 2125 | 365 | |
10 | 11.420 | 10.8 | 1154 | 2116 | 223 | |||
11 | 11.389 | 10.7 | 1038 | 1700 | 194 | |||
12 | 1.2 | 3.75 | 7.140 | 6.4 | 2314 | 1956 | 154 | |
13 | 7.192 | 6.4 | 2244 | 2217 | 161 | |||
14 | 7.170 | 6.1 | 2424 | 2028 | 168 | |||
Weld | 1 | 1.2 | 2.5 | 6.447 | 16.9 | 634 | 385 | 228 |
2 | 6.180 | 16.1 | 546 | 306 | 182 | |||
3 | 6.096 | 16 | 387 | 183 | 140 | |||
4 | 6.159 | 7.4 | 579 | 377 | 213 |
AE Characteristics | Base Metal | Weld | ||||
---|---|---|---|---|---|---|
Variation Range | Average Value | Peak Value | Variation Range | Average Value | Peak Value | |
Amplitude (dB) | 40–99 | 53 | 99 | 40–89 | 47 | 89 |
Energy (eu) | 0–21,051 | 102 | 21,051 | 0–547 | 13 | 547 |
Risetime (μs) | 0–8291 | 137 | 8291 | 0–2217 | 93 | 2217 |
Counts (n) | 0–4223 | 134 | 4223 | 1–849 | 47 | 849 |
Specimen Type | Waveform | Amplitude (dB) | Energy (eu) | Risetime (μs) | Duration (μs) | Counts (n) |
---|---|---|---|---|---|---|
Base metal | Bm-a | 54 | 319 | 28 | 5569 | 583 |
Bm-b | 96 | 3045 | 17 | 24,157 | 1741 | |
Bm-c | 99 | 21,051 | 327 | 30,000 | 4223 | |
Weld | W-a | 63 | 45 | 148 | 2084 | 550 |
W-b | 79 | 206 | 269 | 3606 | 1009 | |
W-a | 84 | 539 | 82 | 5028 | 1874 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zhu, R.; Chi, D. Failure Characterization of Al-Zn-Mg Alloy and Its Weld Using Integrated Acoustic Emission and Digital Image Techniques. Metals 2024, 14, 190. https://doi.org/10.3390/met14020190
Zhu R, Chi D. Failure Characterization of Al-Zn-Mg Alloy and Its Weld Using Integrated Acoustic Emission and Digital Image Techniques. Metals. 2024; 14(2):190. https://doi.org/10.3390/met14020190
Chicago/Turabian StyleZhu, Ronghua, and Dazhao Chi. 2024. "Failure Characterization of Al-Zn-Mg Alloy and Its Weld Using Integrated Acoustic Emission and Digital Image Techniques" Metals 14, no. 2: 190. https://doi.org/10.3390/met14020190