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BİR OTOMOTİV İŞLETMESİNDE MAKİNE ÖĞRENMESİ TEKNİKLERİ İLE KESİCİ TAKIM AŞINMASININ TAHMİNLENMESİ

Year 2025, Volume: 36 Issue: 1, 21 - 46, 30.04.2025

Merve Deniz , Feriştah Özçelik , Tuğba Saraç

https://doi.org/10.46465/endustrimuhendisligi.1569135

Abstract

Kesici takım aşınmasını tahmin etmek ve önlemek için etkili bir yöntem geliştirmek işletmelerde üretim verimliliği açısından kritiktir. Bu çalışmada, bir otomotiv firmasında, makine öğrenmesi teknikleri kullanılarak CNC makinelerinden elde edilen veriler ile kesici takım aşınmasını tahminlemek amaçlanmıştır. Rastgele Orman Regresyonu (RFR), Gradyan Artırma Regresyonu (GBR), Aşırı Gradyan Artırma Regresyonu (XGB) ve Uyarlamalı Artırma Regresyonu (ABR) makine öğrenmesi modelleri kullanılmıştır. Bu modeller, veri setlerinin farklı kombinasyonlarından oluşan 3 farklı senaryo üzerinde değerlendirilmiştir. Veri setleri kesici takım ömrünün evrelerine bölünmüş ve farklı modellerin, kesici takımın farklı evrelerini daha iyi tahmin edebileceği öngörülerek denemeler yapılmıştır. Modeller, Hata Kareleri Ortalaması (MSE) metriğine göre, farklı pencere büyüklükleri ve evreler için tüm senaryolarda değerlendirilmiştir. Senaryo 1’de elde edilen minimum MSE değeri 0,0242; Senaryo 2’de 0,0404; Senaryo 3’te ise 0,0041 olmuştur. Kesici takım aşınmasının evrelere ayrılması, daha hassas bir şekilde tahmin edilmesini sağlamaktadır ve işletmelere daha doğru ve zamanında müdahale imkanı vermektedir.

Keywords

Kestirimci bakım kesici takım ömrü yapay zeka makine öğrenmesi

Thanks

Verilerin ve süreçle ilgili bilgilerin sağlanması konusundaki desteği için sayın Gamze KEÇİBAŞ VİT’e teşekkürlerimizi sunarız.

References

  • Alajmi, M. ve Almeshal, A. M. (2020). Predicting the tool wear of a drilling process using novel machine learning XGBoost-SDA. Materials, 13, 4952. Doi: https://www.mdpi.com/1996-1944/13/21/4952
  • Aldekoa, I., Olmo, A., Pinilla, L., Mouliet, S., Novoa, U. ve Lacalle, L. N. (2023). Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors. Mechanical Systems and Signal Processing, 204, 110773. Doi: https://doi.org/10.1016/j.ymssp.2023.110773
  • Bilgili, D., Keçibaş, G., Beşirova, G., Chehrehzad, M. R., Burun, G., Pehlivan, T., Üresin, U., Emekli, E. ve Lazoğlu, İ. (2023). Tool flank wear prediction using high-frequency machine data from ındustrial edge device. Procedia CIRP, 118, 483–488. Doi: https://doi.org/10.1016/j.procir.2023.06.083
  • Chacón, J.L., Barrena, T. F., García, A., Buruaga, M. S., Badiola, X. ve Vicente, J. (2021). A novel machine learning-based methodology for tool wear prediction using acoustic emission signals. Sensors, 21, 5984. Doi: https://doi.org/10.3390/s21175984
  • Chehrehzad, M. R., Keçibaş, G., Beşirova, G., Üresin, U., İrican, M. ve Lazoğlu, İ. (2024). Tool wear prediction through AI-assisted digital shadow using ındustrial edge device. Journal of Manufacturing Processes, 113, 117–130. Doi: https://doi.org/10.1016/j.jmapro.2024.01.052
  • Gündüzöz, U. (2002). Yapay sinir ağı kullanılarak tornalamada kesici takım aşınması tahmini (Yüksek Lisans Tezi). Yıldız Teknik Üniversitesi Fen Bilimleri Enstitüsü, İstanbul.
  • Hahn, T. (2020). End-to-end deep learning in tool wear monitoring (M.Sc. Thesis). Queen's University. Erişim adresi: http://dx.doi.org/10.1007/s00170-024-13909-w
  • He, Z., Shi, T., Xuan, J. ve Li, T. (2021). Research on tool wear prediction based on temperature signals and deep learning. Wear, 478-479, 203902. Doi: https://doi.org/10.1016/j.wear.2021.203902
  • Huang, Z., Shao, J., Guo, W., Li, W., Zhu, J. ve Fang, D. (2023). Hybrid machine learning-enabled multi-information fusion for indirect measurement of tool flank wear in milling. Measurement, 206, 112255. Doi: https://doi.org/10.1016/j.measurement.2022.112255
  • Javanjal, V.K., Mahajan, K. A., Vijay, R., Sudhakar, G. P. ve Munde, K. H. (2022). Tool wear prediction system using machine learning approach. NeuroQuantology, 20(10), 12922-12928. Doi: 10.14704/nq.2022.20.10.NQ551252
  • Jin, F., Bao, Y., Li, B. ve Jin, X. (2022). Tool wear prediction in edge trimming of carbon fiber reinforced polymer using machine learning with instantaneous parameters. Journal of Manufacturing Processes, 82, 277–295. Doi: https://doi.org/10.1016/j.jmapro.2022.08.006
  • Kalkanlı, E. (2021). Ses sinyalleri kullanılarak talaşlı imalat prosesinde takım durumunun izlenmesi (Yüksek Lisans Tezi). Bursa Teknik Üniversitesi Lisansüstü Eğitim Enstitüsü, Bursa.
  • Korkmaz, M. E., Gupta, M. K., Çelik, E., Ross, N. S. ve Günay, M. (2024). Tool wear and its mechanism in turning aluminum alloys with image processing and machine learning methods. Tribology International, 191, 109207. Doi: https://doi.org/10.1016/j.triboint.2023.109207
  • Korkmaz, M. E., Gupta, M. K., Kuntoğlu, M., Patange, A. D., Ross, N. S., Yılmaz, H., Chauhan, S. ve Vashishtha, G. (2023). Prediction and classification of tool wear and its state in sustainable machining of bohler steel with different machine learning models. Measurement, 223, 113825. Doi: https://doi.org/10.1016/j.measurement.2023.113825
  • Li, C., Zhao, X., Cao, H., Li, L. ve Chen, X. (2023). A data and knowledge-driven cutting parameter adaptive optimization method considering dynamic tool wear. Robotics and Computer–Integrated Manufacturing, 81, 102491. Doi: https://doi.org/10.1016/j.rcim.2022.102491
  • Li, W., Fu, H., Zhuo, Y., Liu, C. ve Jin,H. (2023). Semi-supervised multi-source meta-domain generalization method for tool wear state prediction under varying cutting conditions. Journal of Manufacturing Systems, 71, 323–341. Doi: https://doi.org/10.1016/j.jmsy.2023.09.011
  • Li, Y., Wang, J., Huang, Z. ve Gao, R. X. (2022). Physics-informed meta learning for machining tool wear prediction. Journal of Manufacturing Systems, 62, 17–27. Doi: https://doi.org/10.1016/j.jmsy.2021.10.013
  • Li, Z., Liu, X., İncecik, A., Gupta, M. K., Kr´olczyk, G. M. ve Gardoni, P. (2022). A novel ensemble deep learning model for cutting tool wear monitoring using audio sensors. Journal of Manufacturing Processes, 79, 233–249. Doi: https://doi.org/10.1016/j.jmapro.2022.04.066
  • Monferrer, C. D., P´erez, J., Santos, R., Migu´elez, M. H. ve Cantero, J. L. (2022). Machine learning approach in non-intrusive monitoring of tool wear evolution in massive cfrp automatic drilling processes in the aircraft industry. Journal of Manufacturing Systems, 65, 622–639. Doi: https://doi.org/10.1016/j.jmsy.2022.10.018
  • Özdemir, U. ve Erten, M. (2003). Talasli imalat sirasinda kesici takimda meydana gelen hasar mekanizmalari ve takim hasarini azaltma yöntemleri. Havacılık ve Uzay Teknolojileri Dergisi, 1(1), 37-50. Erişim adresi: https://www.researchgate.net/publication/237362821_TALASLI_IMALAT_SIRASINDA_KESICI_TAKIMDA_MEYDANA_GELEN_HASAR_MEKANIZMALARI_VE_TAKIM_HASARINI_AZALTMA_YONTEMLERI
  • Pashmforoush, F., Araghizad, A.E. ve Budak, E. (2024) Physics-informed tool wear prediction in turning process: A thermo-mechanical wear-included force model integrated with machine learning. Journal of Manufacturing Systems, 77, 266–283. Doi: https://doi.org/10.1016/j.jmsy.2024.09.008
  • Ravikumar, S. ve Ramachandrana, K. I. (2018). Tool wear monitoring of multipoint cutting tool using sound signal features signals with machine learning techniques. Materials Today: Proceedings, 5, 25720–25729. Doi: https://doi.org/10.1016/j.matpr.2018.11.014
  • Sönmez, M., Ertunç, H. M. ve Karakuzu, C. (2000). Kesici takım aşınma durumunun yapay sinir ağı kullanılarak belirlenmesi. Erişim adresi: https://www.academia.edu/12210520
  • Tabaszewski, M., Twardowski, P., Pikuła, M., Znojkiewicz, N., Czyryca, A. ve Czy˙zycki, J. (2022). Machine learning approaches for monitoring of toolwear during grey cast-iron turning. Materials, 15, 4359. Doi: https://doi.org/10.3390/ma15124359
  • Twardowski, P., Czy˙ zycki, J., Czyryca, A., Tabaszewski, M. ve Pikuła, M. (2023). Monitoring and forecasting of tool wear based on measurements of vibration accelerations during cast iron milling. Journal of Manufacturing Processes, 95, 342–350. Doi: https://doi.org/10.1016/j.jmapro.2023.04.036
  • Twardowski, P. ve Pikuła, M. (2019). Prediction of tool wear using artificial neural networks during turning of hardened steel. Materials, 12(19), 3091. Doi: https://doi.org/10.3390/ma12193091
  • Twardowski, P., Tabaszewski, M., Pikuła, M. ve Czyryca, A. (2021). Identification of tool wear using acoustic emission signal and machine learning methods. Precision Engineering, 72, 738–744. Doi: https://doi.org/10.1016/j.precisioneng.2021.07.019
  • Wang, C. ve Shen, B. (2024). Auxiliary input-enhanced siamese neural network: a robust tool wear prediction framework with improved feature extraction and generalization ability. Mechanical Systems and Signal Processing, 211, 111243. Doi: https://doi.org/10.1016/j.ymssp.2024.111243
  • Xu, X., Wang, J., Zhong, B., Ming, W. ve Chen, M. (2021). Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism. Measurement, 177, 109254. Doi: https://doi.org/10.1016/j.measurement.2021.109254
  • Yang, Y., Zhao, X. ve Zhao, L. (2022). Research on asymmetrical edge tool wear prediction in milling tc4 titanium alloy using deep learning. Measurement, 203, 111814. Doi: https://doi.org/10.1016/j.measurement.2022.111814

Year 2025, Volume: 36 Issue: 1, 21 - 46, 30.04.2025

Merve Deniz , Feriştah Özçelik , Tuğba Saraç

https://doi.org/10.46465/endustrimuhendisligi.1569135

Abstract

References

  • Alajmi, M. ve Almeshal, A. M. (2020). Predicting the tool wear of a drilling process using novel machine learning XGBoost-SDA. Materials, 13, 4952. Doi: https://www.mdpi.com/1996-1944/13/21/4952
  • Aldekoa, I., Olmo, A., Pinilla, L., Mouliet, S., Novoa, U. ve Lacalle, L. N. (2023). Early detection of tool wear in electromechanical broaching machines by monitoring main stroke servomotors. Mechanical Systems and Signal Processing, 204, 110773. Doi: https://doi.org/10.1016/j.ymssp.2023.110773
  • Bilgili, D., Keçibaş, G., Beşirova, G., Chehrehzad, M. R., Burun, G., Pehlivan, T., Üresin, U., Emekli, E. ve Lazoğlu, İ. (2023). Tool flank wear prediction using high-frequency machine data from ındustrial edge device. Procedia CIRP, 118, 483–488. Doi: https://doi.org/10.1016/j.procir.2023.06.083
  • Chacón, J.L., Barrena, T. F., García, A., Buruaga, M. S., Badiola, X. ve Vicente, J. (2021). A novel machine learning-based methodology for tool wear prediction using acoustic emission signals. Sensors, 21, 5984. Doi: https://doi.org/10.3390/s21175984
  • Chehrehzad, M. R., Keçibaş, G., Beşirova, G., Üresin, U., İrican, M. ve Lazoğlu, İ. (2024). Tool wear prediction through AI-assisted digital shadow using ındustrial edge device. Journal of Manufacturing Processes, 113, 117–130. Doi: https://doi.org/10.1016/j.jmapro.2024.01.052
  • Gündüzöz, U. (2002). Yapay sinir ağı kullanılarak tornalamada kesici takım aşınması tahmini (Yüksek Lisans Tezi). Yıldız Teknik Üniversitesi Fen Bilimleri Enstitüsü, İstanbul.
  • Hahn, T. (2020). End-to-end deep learning in tool wear monitoring (M.Sc. Thesis). Queen's University. Erişim adresi: http://dx.doi.org/10.1007/s00170-024-13909-w
  • He, Z., Shi, T., Xuan, J. ve Li, T. (2021). Research on tool wear prediction based on temperature signals and deep learning. Wear, 478-479, 203902. Doi: https://doi.org/10.1016/j.wear.2021.203902
  • Huang, Z., Shao, J., Guo, W., Li, W., Zhu, J. ve Fang, D. (2023). Hybrid machine learning-enabled multi-information fusion for indirect measurement of tool flank wear in milling. Measurement, 206, 112255. Doi: https://doi.org/10.1016/j.measurement.2022.112255
  • Javanjal, V.K., Mahajan, K. A., Vijay, R., Sudhakar, G. P. ve Munde, K. H. (2022). Tool wear prediction system using machine learning approach. NeuroQuantology, 20(10), 12922-12928. Doi: 10.14704/nq.2022.20.10.NQ551252
  • Jin, F., Bao, Y., Li, B. ve Jin, X. (2022). Tool wear prediction in edge trimming of carbon fiber reinforced polymer using machine learning with instantaneous parameters. Journal of Manufacturing Processes, 82, 277–295. Doi: https://doi.org/10.1016/j.jmapro.2022.08.006
  • Kalkanlı, E. (2021). Ses sinyalleri kullanılarak talaşlı imalat prosesinde takım durumunun izlenmesi (Yüksek Lisans Tezi). Bursa Teknik Üniversitesi Lisansüstü Eğitim Enstitüsü, Bursa.
  • Korkmaz, M. E., Gupta, M. K., Çelik, E., Ross, N. S. ve Günay, M. (2024). Tool wear and its mechanism in turning aluminum alloys with image processing and machine learning methods. Tribology International, 191, 109207. Doi: https://doi.org/10.1016/j.triboint.2023.109207
  • Korkmaz, M. E., Gupta, M. K., Kuntoğlu, M., Patange, A. D., Ross, N. S., Yılmaz, H., Chauhan, S. ve Vashishtha, G. (2023). Prediction and classification of tool wear and its state in sustainable machining of bohler steel with different machine learning models. Measurement, 223, 113825. Doi: https://doi.org/10.1016/j.measurement.2023.113825
  • Li, C., Zhao, X., Cao, H., Li, L. ve Chen, X. (2023). A data and knowledge-driven cutting parameter adaptive optimization method considering dynamic tool wear. Robotics and Computer–Integrated Manufacturing, 81, 102491. Doi: https://doi.org/10.1016/j.rcim.2022.102491
  • Li, W., Fu, H., Zhuo, Y., Liu, C. ve Jin,H. (2023). Semi-supervised multi-source meta-domain generalization method for tool wear state prediction under varying cutting conditions. Journal of Manufacturing Systems, 71, 323–341. Doi: https://doi.org/10.1016/j.jmsy.2023.09.011
  • Li, Y., Wang, J., Huang, Z. ve Gao, R. X. (2022). Physics-informed meta learning for machining tool wear prediction. Journal of Manufacturing Systems, 62, 17–27. Doi: https://doi.org/10.1016/j.jmsy.2021.10.013
  • Li, Z., Liu, X., İncecik, A., Gupta, M. K., Kr´olczyk, G. M. ve Gardoni, P. (2022). A novel ensemble deep learning model for cutting tool wear monitoring using audio sensors. Journal of Manufacturing Processes, 79, 233–249. Doi: https://doi.org/10.1016/j.jmapro.2022.04.066
  • Monferrer, C. D., P´erez, J., Santos, R., Migu´elez, M. H. ve Cantero, J. L. (2022). Machine learning approach in non-intrusive monitoring of tool wear evolution in massive cfrp automatic drilling processes in the aircraft industry. Journal of Manufacturing Systems, 65, 622–639. Doi: https://doi.org/10.1016/j.jmsy.2022.10.018
  • Özdemir, U. ve Erten, M. (2003). Talasli imalat sirasinda kesici takimda meydana gelen hasar mekanizmalari ve takim hasarini azaltma yöntemleri. Havacılık ve Uzay Teknolojileri Dergisi, 1(1), 37-50. Erişim adresi: https://www.researchgate.net/publication/237362821_TALASLI_IMALAT_SIRASINDA_KESICI_TAKIMDA_MEYDANA_GELEN_HASAR_MEKANIZMALARI_VE_TAKIM_HASARINI_AZALTMA_YONTEMLERI
  • Pashmforoush, F., Araghizad, A.E. ve Budak, E. (2024) Physics-informed tool wear prediction in turning process: A thermo-mechanical wear-included force model integrated with machine learning. Journal of Manufacturing Systems, 77, 266–283. Doi: https://doi.org/10.1016/j.jmsy.2024.09.008
  • Ravikumar, S. ve Ramachandrana, K. I. (2018). Tool wear monitoring of multipoint cutting tool using sound signal features signals with machine learning techniques. Materials Today: Proceedings, 5, 25720–25729. Doi: https://doi.org/10.1016/j.matpr.2018.11.014
  • Sönmez, M., Ertunç, H. M. ve Karakuzu, C. (2000). Kesici takım aşınma durumunun yapay sinir ağı kullanılarak belirlenmesi. Erişim adresi: https://www.academia.edu/12210520
  • Tabaszewski, M., Twardowski, P., Pikuła, M., Znojkiewicz, N., Czyryca, A. ve Czy˙zycki, J. (2022). Machine learning approaches for monitoring of toolwear during grey cast-iron turning. Materials, 15, 4359. Doi: https://doi.org/10.3390/ma15124359
  • Twardowski, P., Czy˙ zycki, J., Czyryca, A., Tabaszewski, M. ve Pikuła, M. (2023). Monitoring and forecasting of tool wear based on measurements of vibration accelerations during cast iron milling. Journal of Manufacturing Processes, 95, 342–350. Doi: https://doi.org/10.1016/j.jmapro.2023.04.036
  • Twardowski, P. ve Pikuła, M. (2019). Prediction of tool wear using artificial neural networks during turning of hardened steel. Materials, 12(19), 3091. Doi: https://doi.org/10.3390/ma12193091
  • Twardowski, P., Tabaszewski, M., Pikuła, M. ve Czyryca, A. (2021). Identification of tool wear using acoustic emission signal and machine learning methods. Precision Engineering, 72, 738–744. Doi: https://doi.org/10.1016/j.precisioneng.2021.07.019
  • Wang, C. ve Shen, B. (2024). Auxiliary input-enhanced siamese neural network: a robust tool wear prediction framework with improved feature extraction and generalization ability. Mechanical Systems and Signal Processing, 211, 111243. Doi: https://doi.org/10.1016/j.ymssp.2024.111243
  • Xu, X., Wang, J., Zhong, B., Ming, W. ve Chen, M. (2021). Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism. Measurement, 177, 109254. Doi: https://doi.org/10.1016/j.measurement.2021.109254
  • Yang, Y., Zhao, X. ve Zhao, L. (2022). Research on asymmetrical edge tool wear prediction in milling tc4 titanium alloy using deep learning. Measurement, 203, 111814. Doi: https://doi.org/10.1016/j.measurement.2022.111814
There are 30 citations in total.

Details

Primary Language Turkish
Subjects Manufacturing and Industrial Engineering (Other)
Journal Section Research Articles
Authors

Merve Deniz 0009-0008-0298-3377

Feriştah Özçelik 0000-0003-0329-203X

Tuğba Saraç 0000-0002-8115-3206

Publication Date April 30, 2025
Submission Date October 17, 2024
Acceptance Date January 29, 2025
Published in Issue Year 2025 Volume: 36 Issue: 1

Cite

APA Deniz, M., Özçelik, F., & Saraç, T. (2025). BİR OTOMOTİV İŞLETMESİNDE MAKİNE ÖĞRENMESİ TEKNİKLERİ İLE KESİCİ TAKIM AŞINMASININ TAHMİNLENMESİ. Endüstri Mühendisliği, 36(1), 21-46. https://doi.org/10.46465/endustrimuhendisligi.1569135
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