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

Year 2025, Volume: 28 Issue: 4, 1722 - 1731, 03.12.2025

Mahmut Durgun , Bilal Yıldırım

Abstract

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.

Keywords

Nesnelerin İnterneti Kenar Bilişim Kaynak Kalitesi Analizi Makine Öğrenimi

References

  • Asif, K., Zhang, L., Derrible, S., Indacochea, E., Ozevin, D., & Ziebart, B. (2022). Machine learning model to predict welding quality using air-coupled acoustic emission and weld inputs. Journal of Intelligent Manufacturing, 33, 1–15. https://doi.org/10.1007/s10845-020-01667-x
  • Billa, N. M., Akki, P., Seeniappan, K., Radha, M., & Pathuri, S. K. (2024). A ML Approach for Predicting the Winner of IPL-24 Using Novel-Hybrid Classifier. (Proceedings of the 2024 IEEE 9th International Conference on …). https://www.researchgate.net/publication/381349178_A_ML_Approach_for_Predicting_The_Winner_of_IPL-24_Using_Novel-Hybrid_Classifier
  • Chen, J., Wang, T., Gao, X., & Wei, L. (2018). Real-time monitoring of high-power disk laser welding based on support vector machine. Computers in Industry, 94, 75–81. https://doi.org/10.1016/j.compind.2017.10.003
  • Demirel, E., & Yaralı, C. (2023). İmalat işletmelerinin dijitalleşme süreçleri üzerine nitel bir çalışma. Yönetim ve Ekonomi, 30(100 Yıl Özel Sayısı), 21–41. https://doi.org/10.18657/yonveek.1379397
  • Dingorkar, S., Kalshetti, S., Shah, Y., & Lahane, P. (2024). Real-Time Data Processing Architectures for IoT Applications: A Comprehensive Review. 2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP), 507–513. https://doi.org/10.1109/TIACOMP64125.2024.00090
  • Doshi, R., Inamdar, S., Karmarkar, T., & Wakode, M. (2024). Distributed MQTT Broker: A Load-Balanced Redis-Based Architecture. 2024 International Conference on Emerging Smart Computing and Informatics (ESCI), 1–6. https://doi.org/10.1109/ESCI59607.2024.10497427
  • Döven, S. (2023). Fog computing nodes in IoT and wireless sensor networks. May 2023. https://www.researchgate.net/publication/370865527
  • Durgun, Y. (2021). Nesnelerin İnterneti Teknolojisinin Kümes Ortamına Uygulanması ve Etkileri. Avrupa Bilim ve Teknoloji Dergisi, (28), 463-468. https://doi.org/10.31590/ejosat.1005685
  • Durgun, Y., & Durgun, M. (2025). Destek vektör makinesi ve kenar bilişim ile güçlendirilmiş gerçek zamanlı ambulans sireni algılama sistemi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 40(2), 1147-1158. https://doi.org/10.17341/gazimmfd.1416188
  • Durgun, Y., & Durgun, M. (2025). Arı kovanlarının çevresel ve akustik verilere dayalı durum analizi: Normal ve özel koşulların karşılaştırılması. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 414–429. https://dergipark.org.tr/tr/pub/ksujes
  • Gedik, Y. (2021). Endüstri 4.0 teknolojilerinin ve Endüstri 4.0’ın üretim ve tedarik zinciri kapsamındaki etkileri: Teorik bir çerçeve. Journal of Emerging Economies and Policy, 6(1), 248–264. https://dergipark.org.tr/tr/pub/joeep/issue/62672/933783
  • Hanon, W., & Salman, M. (2024). Smart Controller Integrated with MQTT Broker Based on Machina Learning Techniques. Journal Européen Des Systèmes Automatisés, 57, 87–94. https://doi.org/10.18280/jesa.570109
  • Lin, C.-Y., & Wu, I.-C. (2024). Real-time simulation and control of indoor air exchange volume based on Digital Twin Platform. 637–644. https://koreascience.kr/article/CFKO202431947397342.pdf
  • Lipovetsky, S. (2024). Symbolic Regression. Technometrics, 66(4), 674–675. https://doi.org/10.1080/00401706.2024.2407721
  • Liu, K., Wu, T., Shi, Z., Yu, X., Lin, Y., Chen, Q., & Jiang, H. (2024). Interpretable machine learning models for predicting the bond strength between UHPC and normal-strength concrete. Materials Today Communications, 40, 110006. https://doi.org/10.1016/j.mtcomm.2024.110006
  • Mazuin, E., Yusof, M. I., Ali, R., Harjimi, I., & Bahrin, Q. (2020). Welding station monitoring system using internet of thing (IOT). Indonesian Journal of Electrical Engineering and Computer Science, 18, 1319. https://doi.org/10.11591/ijeecs.v18.i3.pp1319-1330
  • Rausch, T., Nastic, S., & Dustdar, S. (2018). EMMA: Distributed QoS-aware MQTT middleware for edge computing applications. Proceedings - 2018 IEEE International Conference on Cloud Engineering, IC2E 2018, (i), 191–197. https://doi.org/10.1109/IC2E.2018.00043
  • Saimon, S. I., Islam, I., Abir, S., Sultana, N., Roy, M., & Shiam, S. (2025). Advancing Neurological Disease Prediction through Machine Learning Techniques. Journal of Computer Science and Technology Studies, 7, 139–156. https://doi.org/10.32996/jcsts.2025.7.1.11
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003
  • Turgut, M. (2024). Use of Industry 4.0 Technologies in the Logistics Activities Process in the Agriculture-Food Supply Chain. Turkish Journal of Agriculture - Food Science and Technology, 12(11), 1968-1980. https://doi.org/10.24925/turjaf.v12i11.1968-1980.6822
  • Udayakumar, P., & Anandan, R. (2024). Evaluation of Protocol-Centric IDS for the IoMT Leveraging ML Techniques. 2024 IEEE World AI IoT Congress (AIIoT), 546–551. https://doi.org/10.1109/AIIoT61789.2024.10578945
  • Yadav, P., Rishiwal, V., Yadav, M., Alotaibi, A., Maurya, V., Agarwal, U., & Sharma, S. (2024). Investigation and Empirical Analysis of Transfer Learning for Industrial IoT Networks. IEEE Access, 12(October), 173351–173379. https://doi.org/10.1109/ACCESS.2024.3499741
  • Yusof, M., Ishak, M., & Ghazali, M. F. (2021). Acoustic methods in real-time welding process monitoring: Application and future potential advancement. Journal of Mechanical Engineering and Sciences, 15, 8490–8507. https://doi.org/10.15282/jmes.15.4.2021.03.0669
  • Zhang, Y., You, D., Gao, X., Wang, C., Li, Y., & Gao, P. (2020). Real-time monitoring of high-power disk laser welding statuses based on deep learning framework. Journal of Intelligent Manufacturing, 31. https://doi.org/10.1007/s10845-019-01477-w
  • Zhu, C., Liu, X., Xu, Y., Liu, W., & Wang, Z. (2021). Determination of boundary temperature and intelligent control scheme for heavy oil field gathering and transportation system. Journal of Pipeline Science and Engineering, 1(4), 407–418. https://doi.org/10.1016/j.jpse.2021.09.007

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

Year 2025, Volume: 28 Issue: 4, 1722 - 1731, 03.12.2025

Mahmut Durgun , Bilal Yıldırım

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

Internet of Things Edge Computing Welding Quality Analysis Machine Learning

References

  • Asif, K., Zhang, L., Derrible, S., Indacochea, E., Ozevin, D., & Ziebart, B. (2022). Machine learning model to predict welding quality using air-coupled acoustic emission and weld inputs. Journal of Intelligent Manufacturing, 33, 1–15. https://doi.org/10.1007/s10845-020-01667-x
  • Billa, N. M., Akki, P., Seeniappan, K., Radha, M., & Pathuri, S. K. (2024). A ML Approach for Predicting the Winner of IPL-24 Using Novel-Hybrid Classifier. (Proceedings of the 2024 IEEE 9th International Conference on …). https://www.researchgate.net/publication/381349178_A_ML_Approach_for_Predicting_The_Winner_of_IPL-24_Using_Novel-Hybrid_Classifier
  • Chen, J., Wang, T., Gao, X., & Wei, L. (2018). Real-time monitoring of high-power disk laser welding based on support vector machine. Computers in Industry, 94, 75–81. https://doi.org/10.1016/j.compind.2017.10.003
  • Demirel, E., & Yaralı, C. (2023). İmalat işletmelerinin dijitalleşme süreçleri üzerine nitel bir çalışma. Yönetim ve Ekonomi, 30(100 Yıl Özel Sayısı), 21–41. https://doi.org/10.18657/yonveek.1379397
  • Dingorkar, S., Kalshetti, S., Shah, Y., & Lahane, P. (2024). Real-Time Data Processing Architectures for IoT Applications: A Comprehensive Review. 2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP), 507–513. https://doi.org/10.1109/TIACOMP64125.2024.00090
  • Doshi, R., Inamdar, S., Karmarkar, T., & Wakode, M. (2024). Distributed MQTT Broker: A Load-Balanced Redis-Based Architecture. 2024 International Conference on Emerging Smart Computing and Informatics (ESCI), 1–6. https://doi.org/10.1109/ESCI59607.2024.10497427
  • Döven, S. (2023). Fog computing nodes in IoT and wireless sensor networks. May 2023. https://www.researchgate.net/publication/370865527
  • Durgun, Y. (2021). Nesnelerin İnterneti Teknolojisinin Kümes Ortamına Uygulanması ve Etkileri. Avrupa Bilim ve Teknoloji Dergisi, (28), 463-468. https://doi.org/10.31590/ejosat.1005685
  • Durgun, Y., & Durgun, M. (2025). Destek vektör makinesi ve kenar bilişim ile güçlendirilmiş gerçek zamanlı ambulans sireni algılama sistemi. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 40(2), 1147-1158. https://doi.org/10.17341/gazimmfd.1416188
  • Durgun, Y., & Durgun, M. (2025). Arı kovanlarının çevresel ve akustik verilere dayalı durum analizi: Normal ve özel koşulların karşılaştırılması. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 414–429. https://dergipark.org.tr/tr/pub/ksujes
  • Gedik, Y. (2021). Endüstri 4.0 teknolojilerinin ve Endüstri 4.0’ın üretim ve tedarik zinciri kapsamındaki etkileri: Teorik bir çerçeve. Journal of Emerging Economies and Policy, 6(1), 248–264. https://dergipark.org.tr/tr/pub/joeep/issue/62672/933783
  • Hanon, W., & Salman, M. (2024). Smart Controller Integrated with MQTT Broker Based on Machina Learning Techniques. Journal Européen Des Systèmes Automatisés, 57, 87–94. https://doi.org/10.18280/jesa.570109
  • Lin, C.-Y., & Wu, I.-C. (2024). Real-time simulation and control of indoor air exchange volume based on Digital Twin Platform. 637–644. https://koreascience.kr/article/CFKO202431947397342.pdf
  • Lipovetsky, S. (2024). Symbolic Regression. Technometrics, 66(4), 674–675. https://doi.org/10.1080/00401706.2024.2407721
  • Liu, K., Wu, T., Shi, Z., Yu, X., Lin, Y., Chen, Q., & Jiang, H. (2024). Interpretable machine learning models for predicting the bond strength between UHPC and normal-strength concrete. Materials Today Communications, 40, 110006. https://doi.org/10.1016/j.mtcomm.2024.110006
  • Mazuin, E., Yusof, M. I., Ali, R., Harjimi, I., & Bahrin, Q. (2020). Welding station monitoring system using internet of thing (IOT). Indonesian Journal of Electrical Engineering and Computer Science, 18, 1319. https://doi.org/10.11591/ijeecs.v18.i3.pp1319-1330
  • Rausch, T., Nastic, S., & Dustdar, S. (2018). EMMA: Distributed QoS-aware MQTT middleware for edge computing applications. Proceedings - 2018 IEEE International Conference on Cloud Engineering, IC2E 2018, (i), 191–197. https://doi.org/10.1109/IC2E.2018.00043
  • Saimon, S. I., Islam, I., Abir, S., Sultana, N., Roy, M., & Shiam, S. (2025). Advancing Neurological Disease Prediction through Machine Learning Techniques. Journal of Computer Science and Technology Studies, 7, 139–156. https://doi.org/10.32996/jcsts.2025.7.1.11
  • Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003
  • Turgut, M. (2024). Use of Industry 4.0 Technologies in the Logistics Activities Process in the Agriculture-Food Supply Chain. Turkish Journal of Agriculture - Food Science and Technology, 12(11), 1968-1980. https://doi.org/10.24925/turjaf.v12i11.1968-1980.6822
  • Udayakumar, P., & Anandan, R. (2024). Evaluation of Protocol-Centric IDS for the IoMT Leveraging ML Techniques. 2024 IEEE World AI IoT Congress (AIIoT), 546–551. https://doi.org/10.1109/AIIoT61789.2024.10578945
  • Yadav, P., Rishiwal, V., Yadav, M., Alotaibi, A., Maurya, V., Agarwal, U., & Sharma, S. (2024). Investigation and Empirical Analysis of Transfer Learning for Industrial IoT Networks. IEEE Access, 12(October), 173351–173379. https://doi.org/10.1109/ACCESS.2024.3499741
  • Yusof, M., Ishak, M., & Ghazali, M. F. (2021). Acoustic methods in real-time welding process monitoring: Application and future potential advancement. Journal of Mechanical Engineering and Sciences, 15, 8490–8507. https://doi.org/10.15282/jmes.15.4.2021.03.0669
  • Zhang, Y., You, D., Gao, X., Wang, C., Li, Y., & Gao, P. (2020). Real-time monitoring of high-power disk laser welding statuses based on deep learning framework. Journal of Intelligent Manufacturing, 31. https://doi.org/10.1007/s10845-019-01477-w
  • Zhu, C., Liu, X., Xu, Y., Liu, W., & Wang, Z. (2021). Determination of boundary temperature and intelligent control scheme for heavy oil field gathering and transportation system. Journal of Pipeline Science and Engineering, 1(4), 407–418. https://doi.org/10.1016/j.jpse.2021.09.007
There are 25 citations in total.

Details

Primary Language Turkish
Subjects Cyberphysical Systems and Internet of Things, Deep Learning
Journal Section Research Article
Authors

Mahmut Durgun 0000-0002-5010-687X

Bilal Yıldırım 0009-0006-5913-5841

Publication Date December 3, 2025
Submission Date April 10, 2025
Acceptance Date October 29, 2025
Published in Issue Year 2025 Volume: 28 Issue: 4

Cite

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.
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