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Journals / Journal of Materials and Mechatronics: A / 2026 / Cilt: 5 Sayı: 1

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

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

Zekeriya Balcı(YÜZÜNCÜ YIL ÜNİVERSİTESİ, ÇALDIRAN MESLEK YÜKSEKOKULU)0000-0002-1389-1784
Ahmet Mert(BURSA TEKNİK ÜNİVERSİTESİ)0000-0003-4236-3646
Pages
1–13
Publication Date
2024-06-26
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Inter- material and cross-material evaluations are performed, and the accuracy rates were 100% and 97.77% respectively.

📌 3 Key Takeaways

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Objective & Hypothesis:In this study, we propose a photoacoustic (PA) signal processing framework for material defect detection using empirical mode decomposition (EMD) and machine learning algorithms.
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Key Findings & Data: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.
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Conclusion & Impact:Inter- material and cross-material evaluations are performed, and the accuracy rates were 100% and 97.77% respectively.
🔬Design:Modeling & Simulation
📍Scope:240 samples
⚙️Variables:Fotoakustik · Görgül kip ayrışımı · Destek vektör makinesi

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.

Özet

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.

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Cite This Article

Balcı, Z., Mert, A. (2024). Defect Detection with EMD-Based FA Signal Processing on Different Solid Materials. Journal of Materials and Mechatronics: A, Cilt: 5 Sayı: 1, 1–13. https://doi.org/10.55546/jmm.1422073

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