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

Year 2024, , 1 - 13, 26.06.2024

Zekeriya Balcı , Ahmet Mert

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

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.

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