KENAR BİLİŞİM TABANLI OTOMATİK KALİTE ANALİZİ: KAYNAK SÜREÇLERİNDE SES VERİSİNİN KULLANIMI

Mahmut Durgun; Bilal Yıldırım

doi:10.17780/ksujes.1673438

TR EN

KENAR BİLİŞİM TABANLI OTOMATİK KALİTE ANALİZİ: KAYNAK SÜREÇLERİNDE SES VERİSİNİN KULLANIMI

Öz

Bu çalışma, kaynak kalitesinin otomatik olarak analiz edilmesi amacıyla Nesnelerin İnterneti (IoT) ve Kenar Bilişim (Edge Computing) teknolojilerinin ses verisi analiziyle nasıl entegre edilebileceğini incelemektedir. Geleneksel kaynak kalite kontrol yöntemleri genellikle manuel denetime dayanmakta ve fiziksel ölçüm ekipmanlarına ihtiyaç duymaktadır. Buna karşılık önerilen yaklaşım, kaynak işlemleri sırasında elde edilen ses verilerinin analizine dayalı müdahale gerektirmeyen, gerçek zamanlı bir izleme yöntemi sunmaktadır. Çalışmada, farklı kaynak koşulları altında kaydedilen ses verilerinden oluşan bir veri seti kullanılmış ve bu ses kayıtları, gelişmiş sinyal işleme teknikleri (MFCC, spektral centroid, sıfır geçiş oranı) ile öznitelik çıkarımı yapılarak işlenmiştir. Elde edilen özellikler, daha sonra bir Yapay Sinir Ağı (ANN) modeli ile analiz edilerek yüksek ve düşük kaliteli kaynakların sınıflandırılması sağlanmıştır. Uygulanan model, %77 doğruluk oranına ulaşmış ve her iki sınıfta da tatmin edici performans sergilemiştir. İşlemin kenarda gerçekleştirilmesi, bulut kaynaklarına olan bağımlılığı azaltarak sistemin tepki süresini iyileştirmiş ve daha enerji verimli bir yapı oluşturmuştur. Bu çalışma, yalnızca akıllı üretim sistemlerine katkı sağlamakla kalmayıp, aynı zamanda endüstriyel kalite kontrol sistemlerinde Nesnelerin İnterneti (IoT) ve Kenar Bilişim (Edge Computing) tabanlı çözümlerin uygulanabilirliğine dair değerli bir temel sunmaktadır.

Anahtar Kelimeler

EDGE COMPUTING BASED AUTOMATED QUALITY ANALYSIS: UTILIZATION OF AUDIO DATA IN WELDING PROCESSES

Abstract

This study investigates how Internet of Things (IoT) and Edge Computing technologies can be integrated with sound data analysis to automatically evaluate welding quality. Traditional welding quality control methods typically rely on manual inspection and require physical measurement equipment. In contrast, the proposed approach offers a real-time, non-intrusive monitoring method based on the analysis of acoustic data collected during welding operations. A dataset consisting of sound recordings captured under different welding conditions was used, and these recordings were processed using advanced signal processing techniques such as MFCC, spectral centroid, and zero-crossing rate for feature extraction. The extracted features were then analyzed using an Artificial Neural Network (ANN) model to classify high- and low-quality welds. The implemented model achieved an accuracy rate of 77% and demonstrated satisfactory performance across both classes. Performing the computation at the edge reduced dependency on cloud resources, improved response time, and enhanced energy efficiency. This study not only contributes to the development of smart manufacturing systems but also provides a valuable foundation for the applicability of IoT- and Edge Computing–based solutions in industrial quality control processes.

Keywords

References

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Details

Primary Language

Turkish

Subjects

Cyberphysical Systems and Internet of Things , Deep Learning

Journal Section

Research Article

Authors

Mahmut Durgun
0000-0002-5010-687X
Türkiye

Bilal Yıldırım ^*
0009-0006-5913-5841
Türkiye

Publication Date

December 3, 2025

Submission Date

April 10, 2025

Acceptance Date

October 29, 2025

Published in Issue

Year 2025 Volume: 28 Number: 4

DOI

https://doi.org/10.17780/ksujes.1673438

IZ

https://izlik.org/JA82JM62FG

Cite

RIS / Bibtex

APA

Durgun, M., & Yıldırım, B. (2025). KENAR BİLİŞİM TABANLI OTOMATİK KALİTE ANALİZİ: KAYNAK SÜREÇLERİNDE SES VERİSİNİN KULLANIMI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(4), 1722-1731. https://doi.org/10.17780/ksujes.1673438