Farklı Katı Malzemelerde Görgül Kip Analizi Tabanlı Foto Akustik Sinyal İşleme ile Kusur Tespiti

Year 2024, Volume: 5 Issue: 1, 1 - 13, 26.06.2024

Zekeriya Balcı , Ahmet Mert

https://doi.org/10.55546/jmm.1422073

Cited By: 1

Abstract

Bu çalışmada, görgül kip ayrışımı (GKA) ve makine öğrenimi algoritması kullanılarak malzeme kusurlarının tespiti için bir fotoakustik (FA) sinyal işleme çerçevesi önerilmiştir. Zaman ve zaman-frekans düzleminde çıkarılan özellikler ve gelişmiş sinyal işleme yöntemlerinin yardımıyla kusurların başarılı bir şekilde tespit edilmesini sağlamıştır. Lazer, mikrofon ve veri toplama kartı tabanlı bir FA sistem kullanılarak alüminyum, demir ve ahşap malzemelerden FA sinyallerinden oluşan veritabanı elde edilmiştir. Her bir malzeme grubundan toplam 240 örnek (120 sağlam örnek ve 120 kusurlu örnek) ve toplam 720 örnek, GKA uygulandıktan sonra zaman ve zaman-frekans düzlemi özelliklerini çıkarmak için kullanılmıştır. Daha sonra k-en yakın komşu sınıflandırıcısı veri tabanındaki kusurlu ve sağlam malzemelerin tespiti için çıkarılan 14 özellik kullanılarak eğitilmiş ve test edilmiştir. Materyaller özelinde ve materyaller arası sınıflandırma yapılmış ve doğruluk oranları sırasıyla %100 ve %97.77 olarak elde edilmiştir.

Keywords

Fotoakustik, Görgül kip ayrışımı, Destek vektör makinesi, K-en yakın komşu, Karar ağacı, Kusur tespiti

Project Number

210D003

Thanks

Bu çalışma, Bursa Teknik Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü Birimi tarafından “210D003” kodlu proje ile desteklenmiştir.

Defect Detection with EMD-Based FA Signal Processing on Different Solid Materials

Abstract

In this study, we propose a photoacoustic (PA) signal processing framework for material defect detection using empirical mode decomposition (EMD) and machine learning algorithms. The extracted time and time-frequency domain features enables to detect defects successfully with the help of the advanced processing methods. A database of the PA signals has been obtained from aluminum, iron and wood materials using a laser, microphone and data acquisition board-based PA apparatus. Within each material group, a total of 240 samples (120 intact samples and 120 defective samples), and a total of 720 samples are used to extract time and time-frequency domain features after applying the EMD. k-nearest neighbor classifier is then trained and tested using the extracted 14 features for detection of the defective and intact materials in the database. Inter- material and cross-material evaluations are performed, and the accuracy rates were 100% and 97.77% respectively.

Keywords

Photoacoustic, Empirical mode decomposition, Support vector machine, K-nearest neighbor, Decision tree, Non-destructive testing

Project Number

210D003

Thanks

Bu çalışma, Bursa Teknik Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü Birimi tarafından “210D003” kodlu proje ile desteklenmiştir.

References

• Arslan, M., Toplan, N., AA6061 Serisi Alüminyum Plakalarına Yapılan MIG ve TIG Kaynak Tamirlerinin Tahribatlı ve Tahribatsız Testlerle İncelenmesi. J. Mater. Mechat. A 4, 333–354, 2023.
• Beard P.C., Photoacoustic imaging of blood vessel equivalent phantoms. Biomedical Optoacoustics III 4618, 54–62, 2002.
• Bell A.G., On the production and reproduction of sound by light. American Journal of Science 3(20), 305–324, 1880.
• Chen S.L., Tian C., Recent developments in photoacoustic imaging and sensing for nondestructive testing and evaluation. Visual Computing for Industry, Biomedicine, and Art 4, 6, 2021.
• Fisher R.A., Frequency Distribution of the Values of the Correlation Coefficient in Samples from an Indefinitely Large Population. Biometrika 10, 507–521, 1915.
• Huang N.E., Shen Z., Long S.R., Wu M.C., Shih H.H., Zheng Q., Yen N.C., Tung C.C., Liu H.H., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London Series A 454, 903–998, 1998.
• Jeon S., Kim J., Yun J.P., Kim C., Non-destructive photoacoustic imaging of metal surface defects. J. Opt. 18, 114001, 2016.
• Jin Y., Yin Y., Li C., Liu H., Shi J., Non-Invasive Monitoring of Human Health by Photoacoustic Spectroscopy. Sensors 22, 1155, 2022.
• Keeratirawee K., Furter J.S., Hauser P.C., Low-cost electronic circuitry for photoacoustic gas sensing. HardwareX 11, e00280, 2022.
• Keeratirawee K., Hauser P.C., Photoacoustic detection of ozone with a red laser diode. Talanta 223, 121890, 2021.
• Kot P., Muradov M., Gkantou M., Kamaris G.S., Hashim K., Yeboah D., Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring. Applied Sciences 11, 2750, 2021.
• Kumar V.P., Sowmya I., A review on pros and cons of machine learning algorithms. Journal of Engineering Sciences 12, 272–276, 2021.
• Li C., Qi H., Zhao X., Guo M., An R., Chen K., Multi-pass absorption enhanced photoacoustic spectrometer based on combined light sources for dissolved gas analysis in oil. Optics and Lasers in Engineering 159, 107221, 2022.
• Li J., Chen Y., Ye W., Zhang M., Zhu J., Zhi W., Cheng Q., Molecular breast cancer subtype identification using photoacoustic spectral analysis and machine learning at the biomacromolecular level. Photoacoustics 30, 100483, 2023.
• Liao Z., Zhang J., Gan Z., Wang Y., Zhao J., Chen T., Zhang G., Thermal runaway warning of lithium-ion batteries based on photoacoustic spectroscopy gas sensing technology. International Journal of Energy Research 46, 21694–21702, 2022.
• Mert A., Akan A., Emotion recognition from EEG signals by using multivariate empirical mode decomposition. Pattern. Anal. Applic. 21, 81–89, 2013.
• Nakazawa H., Tokumine J., Lefor A.K., Yamamoto K., Karasawa H., Shimazu K., Yorozu T., Use of a photoacoustic needle improves needle tip recognition in a video recording of simulated ultrasound-guided vascular access: A pilot study. J. Vasc. Access 0, 11297298221122137, 2022.
• Setiawan A., Suparta G.B., Mitrayana M., Nugroho W., Surface Crack Detection with Low-cost Photoacoustic Imaging System. IJTech 9, 159, 2018.
• Shiraishi D., Kato R., Endoh H., Hoshimiya T., Destructive Inspection of Weld Defect and its Nondestructive Evaluation by Photoacoustic Microscopy. Jpn. J. Appl. Phys. 49, 07HB13, 2010.
• Strahl T., Steinebrunner J., Weber C., Wöllenstein J., Schmitt K., Photoacoustic methane detection inside a MEMS microphone. Photoacoustics 29, 100428, 2023.
• Stylogiannis A., Prade L., Buehler A., Aguirre J., Sergiadis G., Ntziachristos V., Continuous wave laser diodes enable fast optoacoustic imaging. Photoacoustics 9, 31–38, 2018.
• Sun M., Lin X., Wu Z., Liu Y., Shen Y., Feng N., Non-destructive photoacoustic detecting method for high-speed rail surface defects. IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 12-15 Mayıs, 2014, Montevideo.
• Tasmara F.A., Widyaningrum R., Setiawan A., Mitrayana M., Photoacoustic imaging of hidden dental caries using visible–light diode laser. Journal of Applied Clinical Medical Physics 24, e13935, 2023.
• Vangi D., Banelli L., Gulino M.S., Interference-based amplification for CW laser-induced photoacoustic signals. Ultrasonics 110, 106270, 2021.
• V.R. N., Mohapatra A.K., Nayak R., V.K. U., Kartha V.B., Chidangil S., UV laser-based photoacoustic breath analysis for the diagnosis of respiratory diseases: Detection of Asthma. Sensors and Actuators B: Chemical 370, 132367, 2022.
• Wang S., Tran T., Xiang L., Liu Y., 2019. Non-Destructive Evaluation of Composite and Metallic Structures using Photo-Acoustic Method. AIAA Scitech 2019 Forum. 7-11 Ocak, 2019, San Diego.
• Wong T.T., Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation. Pattern Recognition 48, 2839–2846, 2015.
• Wu S., Liu Y., Chen Y., Xu C., Chen P., Zhang M., Ye W., Wu D., Huang S., Cheng Q., Quick identification of prostate cancer by wavelet transform-based photoacoustic power spectrum analysis. Photoacoustics 25, 100327, 2022.
• Xu M., Wang L.V., Photoacoustic imaging in biomedicine. Review of Scientific Instruments 77, 041101, 2006.
• Yan L., Gao C., Zhao B., Ma X., Zhuang N., Duan H., Non-destructive Imaging of Standard Cracks of Railway by Photoacoustic Piezoelectric Technology. Int. J. Thermophys. 33, 2001–2005, 2012.
• Yang L., Chen C., Zhang Z., Wei X., Glucose Determination by a Single 1535 nm Pulsed Photoacoustic Technique: A Multiple Calibration for the External Factors. J Healthc Eng 2022, 9593843, 2022.
• Zakrzewski J., Chigarev N., Tournat V., Gusev V., Combined Photoacoustic–Acoustic Technique for Crack Imaging. Int. J. Thermophys. 31, 199–207, 2010.
• Zhang S., Li X., Zong M., Zhu X., Wang R., Efficient kNN Classification With Different Numbers of Nearest Neighbors. IEEE Transactions on Neural Networks and Learning Systems 29, 1774–1785, 2018.
• Zhang Y., Wang M., Yu P., Liu Z., Optical gas sensing of sub-ppm SO2F2 and SOF2 from SF6 decomposition based on photoacoustic spectroscopy. IET Optoelectronics 16, 277–282, 2022.
• Zhang Z., Jin H., Zhang W., Lu W., Zheng Z., Sharma A., Pramanik M., Zheng Y., Adaptive enhancement of acoustic resolution photoacoustic microscopy imaging via deep CNN prior. Photoacoustics 30, 100484, 2023.