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Titreşim Optimizasyonu için Akıllı Cnc Router Sistem Tasarımı

Year 2025, Volume: 13 Issue: 2, 462 - 470, 30.06.2025

Duygu Gürkan , Ahmet Mavi , İhsan Korkut

https://doi.org/10.29109/gujsc.1633204

Abstract

Dünyada dijital teknolojiler ile ivmelenen Endüstri 4.0, geleneksel üretim sistemlerine entegre edilerek, üretim sektörünü önemli boyutta dönüştürmekte ve mevcut endüstriyel süreçlerin dijitalleştirilmesini amaçlamaktadır. Bu dijitalleştirme, insan hatası faktörüne bağlı olmayan, akıllı ve kendisini uyarlayan, kalitesi ve verimi daha yüksek, daha hızlı bir imalat sanayinin en büyük adımı olmaktadır. Bu çalışmada, olması gereken titreşim seviyesi ve istenilen kaliteye göre kesme parametrelerini optimize edebilen akıllı bir CNC Router tasarımı ve montajı yapılmıştır. CNC Router’ın kesme parametrelerini tahmin etmesi ve gerçek zamanlı olarak güncellemesi için Stepcraft CNC Router’a ait makine kontrol kartı, sürücüleri, fener mili, güç kaynağı değiştirilmiştir. Ayrıca veri toplama ve kesme parametrelerine müdahale edebilmek için ara yüz tasarımı gerçekleştirilmiştir. Sonuç olarak, kesme parametrelerini olması gereken titreşim seviyesi ve beklenilen kaliteye göre tahmin edebilen, operatör müdahalesi olmadan gerçek zamanlı olarak operasyon sürecine karar verebilen ve kendini uyarlayan özgün bir CNC Router elde edilmiştir.

Keywords

CNC Router Derin öğrenme Kesme parametreleri Tırlama

Ethical Statement

Bu makalenin yazarı çalışmalarında kullandıkları materyal ve yöntemlerin etik kurul izni ve/veya yasal-özel bir izin gerektirmediğini beyan ederler

Supporting Institution

Gazi Üniversitesi Bilimsel Araştırma Projeleri Birimi

Project Number

FGA-2023-8959

Thanks

Gazi Üniversitesi Bilimsel Araştırma Projeleri Birimi'ne FGA-2023-8959 numaralı projemize verdikleri destekten dolayı teşekkür ederiz.

References

  • [1] A. G. Frank, L. S. Dalenogare, N. F Ayala, Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210 (2019) 15-26.
  • [2] X. Zhang, X. Ming, Z. Liu, D. Yin, Z. Chen, Y. Chang A reference framework and overall planning of industrial artificial intelligence (I-AI) for new application scenarios”, The International Journal of Advanced Manufacturing Technology, 101(9), (2019) 2367-2389.
  • [3] M. Ammar, A. Haleem, M. Javaid, S. Bahl, A. S. Verma, Implementing Industry 4.0 technologies in self-healing materials and digitally managing the quality of manufacturing. Materials Today: Proceedings, 52 (2022) 2285-2294.
  • [4] Y. Cui, S. Kara, K. C. Chan, Manufacturing big data ecosystem: A systematic literature review. Robotics and computer-integrated Manufacturing, 62 (2020) 101861.
  • [5] S. Yağmur, AISI 1050 çeliğinin tornalanmasında minimum miktarda yağlamanın (MMY) kesme kuvvetleri ve yüzey pürüzlüğü üzerindeki etkisinin araştırılması. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji, 11(4) (2023) 1024-1034.
  • [6] A. Giampieri, J. Ling-Chin, Z. Ma, A. Smallbone, Roskilly, A. P.. A review of the current automotive manufacturing practice from an energy perspective, Applied Energy, 261 (2020) 114074.
  • [7] Z. You, H. Gao, L. Guo, Y. Liu, J. Li, C. Li,. ”Machine vision based adaptive online condition monitoring for milling cutter under spindle rotation”, Mechanical Systems and Signal Processing, 171 (2022) 108904.
  • [8] B. S. Bayram, İ. Korkut, Parmak Frezelerde Kesme Kuvvetlerinin Modellenmesi. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji, 10(4) (2022) 964-977.
  • [9] T. Debroy, W. Zhang, J. Turner, S. S. Babu, Building digital twins of 3D printing machines. Scripta Materialia, 135 (2017) 119-124.
  • [10] A. Kusiak, Smart manufacturing. International Journal of Production Research, 56(1-2) (2018) 508-517.
  • [11] J. Wang, Y. Ma, L. Zhang, R. X. Gao, D. Wu, Deep learning for smart manufacturing: Methods and applications. Journal of manufacturing systems, 48 (2018), 144-156.
  • [12] M. A. Tnani, M. Feil, K. Diepold, Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine Monitoring. Procedia CIRP, 107 (2022) 131-136.
  • [13] W. Cai, F. Liu, H. Zhang, P. Liu, J. Tuo, Development of dynamic energy benchmark for mass production in machining systems for energy management and energy-efficiency improvement, Applied Energy, 202 (2017) 715-725.
  • [14] W. Cai, C. Liu, K. H. Lai, L. Li, J. Cunha, L. Hu, Energy performance certification in mechanical manufacturing industry: A review and analysis, Energy Conversion and Management, 186 (2019) 415-432.
  • [15] A. Dogan, D. Birant, Machine learning and data mining in manufacturing. Expert Systems with Applications, 166 (2021) 114060.
  • [16] E. Traini, G. Bruno, G. D’antonio, F. Lombardi, Machine learning framework for predictive maintenance in milling. IFAC-PapersOnLine, 52(13) (2019) 177-182.
  • [17] A. Aslan, Machine learning models and machinability analysis for comparison of various cooling and lubricating mediums during milling of Hardox 400 steel. Tribology International, 198 (2024) 109860.
  • [18] R. Teimouri, M. Grabowski. Effect of ultrasonic vibration on fatigue life of Inconel 718 machined by high-speed milling: Physics-enhanced machine learning approach. Mechanical Systems and Signal Processing, 224 (2025) 112115.
  • [19] H. K. Elminir, , M. A. El-Brawany, D. A. Ibrahim, H. M. Elattar, E. A.Ramadan, An efficient deep learning prognostic model for remaining useful life estimation of high speed CNC milling machine cutters. Results in Engineering, 24 (2024) 103420.
  • [20] V. H. Nguyen, T. T. Le, A. T. Nguyen, X. T. Hoang, N. T. Nguyen, N. K. Nguyen, Optimization of milling conditions for AISI 4140 steel using an integrated machine learning-multi objective optimization-multi criteria decision making framework. Measurement, 242 (2025) 115837.
  • [21] Y. Li, Y. Zeng, Y. Qing, G. B. Huang, Learning local discriminative representations via extreme learning machine for machine fault diagnosis. Neurocomputing, 409 (2020) 275-285.
  • [22] Y. Lei, B.Yang, X. Jiang, F. Jia, N. Li, A. K. Nandi, Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138 (2020) 106587
  • [23] Y. Yang, M. Huang, Z. Y. Wang, Q. B. Zhu, Robust scheduling based on extreme learning machine for bi-objective flexible job-shop problems with machine breakdowns. Expert Systems with Applications, 158 (2020) 113545.
  • [24] A. S. Roque, V. W. Krebs, I. C. Figueiro, N. Jazdi, An analysis of machine learning algorithms in rotating machines maintenance. IFAC-PapersOnLine, 55(2) (2022) 252-257.
  • [25] B. Dietrich, J. Walther, M. Weigold, E. Abele, Machine learning based very short term load forecasting of machine tools”, Applied Energy, 276 (2020) 115440.
  • [26] Y. He, P. Wu, Y. Li, Y. Wang, F. Tao, Y. Wang, A generic energy prediction model of machine tools using deep learning algorithms, Applied Energy, 275 (2020) 115402.
  • [27] M. Brillinger, M. Wuwer, M. A. Hadi, F. Haas, Energy prediction for CNC machining with machine learning, CIRP Journal of Manufacturing Science and Technology, 35 (2021) 715-723.
  • [28] T. Mikołajczyk, K. Nowicki, A. Bustillo, D. Y. Pimenov, Predicting tool life in turning operations using neural networks and image processing, Mechanical systems and signal processing, 104 (2018), 503-513.
  • [29] T. Mikołajczyk, K. Nowicki, A. Kłodowski, D. Y. Pimenov, 2017 Neural network approach for automatic image analysis of cutting edge wear”, Mechanical Systems and Signal Processing, 88, 100-110.
  • [30] Z. Jurkovic, G. Cukor, M. Brezocnik, T. Brajkovic, A comparison of machine learning methods for cutting parameters prediction in high speed turning process. Journal of Intelligent Manufacturing, 29(8) (2018) 1683-1693.
  • [31] X. Wu, Y. Liu, , Zhou, X., Mou, A. Automatic identification of tool wear based on convolutional neural network in face milling process”, Sensors, 19(18) (2019) 3817.
  • [32] V. Parwal, B. K. Rout, Machine learning based approach for process supervision to predict tool wear during machining. Procedia CIRP, 98 (2021) 133-138.
  • [33] Z. Huang, J. Zhu, J. Lei, X. Li, F. Tian, Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. Journal of Intelligent Manufacturing, 31(4) (2020) 953-966.
  • [34] I. Oleaga, C. Pardo, J. J. Zulaika, A. Bustillo, A machine-learning based solution for chatter prediction in heavy-duty milling machines. Measurement, 128 (2018) 34-44.
  • [35] M. Kuntoğlu, A. Aslan, D. Y. Pimenov, K. Giasin, T. Mikolajczyk, S. Sharma, “Modeling of cutting parameters and tool geometry for multi-criteria optimization of surface roughness and vibration via response surface methodology in turning of AISI 5140 steel. Materials, 13(19) (2020) 4242.
  • [36] I. Ragai, A. S. Abdalla, H. Abdeltawab, F. Qian, J. Ma, Toward smart manufacturing: Analysis and classification of cutting parameters and energy consumption patterns in turning processes. Journal of Manufacturing Systems, 2022
  • [37] H. Guo, Y. Zhang, K. Zhu, Interpretable deep learning approach for tool wear monitoring in high-speed milling. Computers in Industry, 138 (2022) 103638.
  • [38] E. Kim, T. Bui, J. Yuan, S. C. Mouli, B. Ribeiro, R. A. Yeh, M. B. Jun, Online real-time machining chatter sound detection using convolutional neural network by adopting expert knowledge. Manufacturing Letters, 41 (2024) 1386-1397
  • [39] J. Xie, P. Hu, J.Chen, W. Han, R. Wang, Deep learning-based instantaneous cutting force modeling of three-axis CNC milling. International Journal of Mechanical Sciences, 246 (2023) 108153.
  • [40] Z. Weiguo, Z. Jichao, G. Baosu, T. Weixiang, W. Fenghe, An optimized convolutional neural network for chatter detection in the milling of thin-walled parts, Int. J. Adv. Manuf. Technol. 106 (9) (2020) 3881–3895.
  • [41] W. Zhang, F. Teng, J. Li, Z. Zhang, L. Niu, D. Zhang, Z. Zhang, Denoising method based on CNN-LSTM and CEEMD for LDV signals from accelerometer shock testing. Measurement, 216 (2023) 112951.
  • [42] J. Duan, X. Zhang, T. Shi, A hybrid attention-based paralleled deep learning model for tool wear prediction. Expert Systems with Applications, 211 (2023), 118548.

Smart Cnc Router System Desıgn for Vibratıon Optimization

Year 2025, Volume: 13 Issue: 2, 462 - 470, 30.06.2025

Duygu Gürkan , Ahmet Mavi , İhsan Korkut

https://doi.org/10.29109/gujsc.1633204

Abstract

Accelerating with digital technologies in the world, Industry 4.0 is integrating into traditional production systems, transforming the production sector to a significant extent and aims to digitalize existing industrial processes. This digitalization is the biggest step of a manufacturing industry that is not dependent on human error, is smart and adaptable, has higher quality and efficiency, and is faster. In this study, a smart CNC Router that can optimize cutting parameters according to the required vibration level and quality was designed and assembled. In order for the CNC Router to estimate the cutting parameters and update them in real time, the machine control card, drivers, spindle, and power supply belonging to Stepcraft were changed. In addition, an interface design was carried out. As a result, an original CNC Router that can estimate the cutting parameters according to the required vibration level and expected quality, decide on the operation process in real time without operator intervention and adapt itself was obtained.

Keywords

CNC Router Deep learning Cutting parameters Chatter

Project Number

FGA-2023-8959

References

  • [1] A. G. Frank, L. S. Dalenogare, N. F Ayala, Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210 (2019) 15-26.
  • [2] X. Zhang, X. Ming, Z. Liu, D. Yin, Z. Chen, Y. Chang A reference framework and overall planning of industrial artificial intelligence (I-AI) for new application scenarios”, The International Journal of Advanced Manufacturing Technology, 101(9), (2019) 2367-2389.
  • [3] M. Ammar, A. Haleem, M. Javaid, S. Bahl, A. S. Verma, Implementing Industry 4.0 technologies in self-healing materials and digitally managing the quality of manufacturing. Materials Today: Proceedings, 52 (2022) 2285-2294.
  • [4] Y. Cui, S. Kara, K. C. Chan, Manufacturing big data ecosystem: A systematic literature review. Robotics and computer-integrated Manufacturing, 62 (2020) 101861.
  • [5] S. Yağmur, AISI 1050 çeliğinin tornalanmasında minimum miktarda yağlamanın (MMY) kesme kuvvetleri ve yüzey pürüzlüğü üzerindeki etkisinin araştırılması. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji, 11(4) (2023) 1024-1034.
  • [6] A. Giampieri, J. Ling-Chin, Z. Ma, A. Smallbone, Roskilly, A. P.. A review of the current automotive manufacturing practice from an energy perspective, Applied Energy, 261 (2020) 114074.
  • [7] Z. You, H. Gao, L. Guo, Y. Liu, J. Li, C. Li,. ”Machine vision based adaptive online condition monitoring for milling cutter under spindle rotation”, Mechanical Systems and Signal Processing, 171 (2022) 108904.
  • [8] B. S. Bayram, İ. Korkut, Parmak Frezelerde Kesme Kuvvetlerinin Modellenmesi. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji, 10(4) (2022) 964-977.
  • [9] T. Debroy, W. Zhang, J. Turner, S. S. Babu, Building digital twins of 3D printing machines. Scripta Materialia, 135 (2017) 119-124.
  • [10] A. Kusiak, Smart manufacturing. International Journal of Production Research, 56(1-2) (2018) 508-517.
  • [11] J. Wang, Y. Ma, L. Zhang, R. X. Gao, D. Wu, Deep learning for smart manufacturing: Methods and applications. Journal of manufacturing systems, 48 (2018), 144-156.
  • [12] M. A. Tnani, M. Feil, K. Diepold, Smart Data Collection System for Brownfield CNC Milling Machines: A New Benchmark Dataset for Data-Driven Machine Monitoring. Procedia CIRP, 107 (2022) 131-136.
  • [13] W. Cai, F. Liu, H. Zhang, P. Liu, J. Tuo, Development of dynamic energy benchmark for mass production in machining systems for energy management and energy-efficiency improvement, Applied Energy, 202 (2017) 715-725.
  • [14] W. Cai, C. Liu, K. H. Lai, L. Li, J. Cunha, L. Hu, Energy performance certification in mechanical manufacturing industry: A review and analysis, Energy Conversion and Management, 186 (2019) 415-432.
  • [15] A. Dogan, D. Birant, Machine learning and data mining in manufacturing. Expert Systems with Applications, 166 (2021) 114060.
  • [16] E. Traini, G. Bruno, G. D’antonio, F. Lombardi, Machine learning framework for predictive maintenance in milling. IFAC-PapersOnLine, 52(13) (2019) 177-182.
  • [17] A. Aslan, Machine learning models and machinability analysis for comparison of various cooling and lubricating mediums during milling of Hardox 400 steel. Tribology International, 198 (2024) 109860.
  • [18] R. Teimouri, M. Grabowski. Effect of ultrasonic vibration on fatigue life of Inconel 718 machined by high-speed milling: Physics-enhanced machine learning approach. Mechanical Systems and Signal Processing, 224 (2025) 112115.
  • [19] H. K. Elminir, , M. A. El-Brawany, D. A. Ibrahim, H. M. Elattar, E. A.Ramadan, An efficient deep learning prognostic model for remaining useful life estimation of high speed CNC milling machine cutters. Results in Engineering, 24 (2024) 103420.
  • [20] V. H. Nguyen, T. T. Le, A. T. Nguyen, X. T. Hoang, N. T. Nguyen, N. K. Nguyen, Optimization of milling conditions for AISI 4140 steel using an integrated machine learning-multi objective optimization-multi criteria decision making framework. Measurement, 242 (2025) 115837.
  • [21] Y. Li, Y. Zeng, Y. Qing, G. B. Huang, Learning local discriminative representations via extreme learning machine for machine fault diagnosis. Neurocomputing, 409 (2020) 275-285.
  • [22] Y. Lei, B.Yang, X. Jiang, F. Jia, N. Li, A. K. Nandi, Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138 (2020) 106587
  • [23] Y. Yang, M. Huang, Z. Y. Wang, Q. B. Zhu, Robust scheduling based on extreme learning machine for bi-objective flexible job-shop problems with machine breakdowns. Expert Systems with Applications, 158 (2020) 113545.
  • [24] A. S. Roque, V. W. Krebs, I. C. Figueiro, N. Jazdi, An analysis of machine learning algorithms in rotating machines maintenance. IFAC-PapersOnLine, 55(2) (2022) 252-257.
  • [25] B. Dietrich, J. Walther, M. Weigold, E. Abele, Machine learning based very short term load forecasting of machine tools”, Applied Energy, 276 (2020) 115440.
  • [26] Y. He, P. Wu, Y. Li, Y. Wang, F. Tao, Y. Wang, A generic energy prediction model of machine tools using deep learning algorithms, Applied Energy, 275 (2020) 115402.
  • [27] M. Brillinger, M. Wuwer, M. A. Hadi, F. Haas, Energy prediction for CNC machining with machine learning, CIRP Journal of Manufacturing Science and Technology, 35 (2021) 715-723.
  • [28] T. Mikołajczyk, K. Nowicki, A. Bustillo, D. Y. Pimenov, Predicting tool life in turning operations using neural networks and image processing, Mechanical systems and signal processing, 104 (2018), 503-513.
  • [29] T. Mikołajczyk, K. Nowicki, A. Kłodowski, D. Y. Pimenov, 2017 Neural network approach for automatic image analysis of cutting edge wear”, Mechanical Systems and Signal Processing, 88, 100-110.
  • [30] Z. Jurkovic, G. Cukor, M. Brezocnik, T. Brajkovic, A comparison of machine learning methods for cutting parameters prediction in high speed turning process. Journal of Intelligent Manufacturing, 29(8) (2018) 1683-1693.
  • [31] X. Wu, Y. Liu, , Zhou, X., Mou, A. Automatic identification of tool wear based on convolutional neural network in face milling process”, Sensors, 19(18) (2019) 3817.
  • [32] V. Parwal, B. K. Rout, Machine learning based approach for process supervision to predict tool wear during machining. Procedia CIRP, 98 (2021) 133-138.
  • [33] Z. Huang, J. Zhu, J. Lei, X. Li, F. Tian, Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. Journal of Intelligent Manufacturing, 31(4) (2020) 953-966.
  • [34] I. Oleaga, C. Pardo, J. J. Zulaika, A. Bustillo, A machine-learning based solution for chatter prediction in heavy-duty milling machines. Measurement, 128 (2018) 34-44.
  • [35] M. Kuntoğlu, A. Aslan, D. Y. Pimenov, K. Giasin, T. Mikolajczyk, S. Sharma, “Modeling of cutting parameters and tool geometry for multi-criteria optimization of surface roughness and vibration via response surface methodology in turning of AISI 5140 steel. Materials, 13(19) (2020) 4242.
  • [36] I. Ragai, A. S. Abdalla, H. Abdeltawab, F. Qian, J. Ma, Toward smart manufacturing: Analysis and classification of cutting parameters and energy consumption patterns in turning processes. Journal of Manufacturing Systems, 2022
  • [37] H. Guo, Y. Zhang, K. Zhu, Interpretable deep learning approach for tool wear monitoring in high-speed milling. Computers in Industry, 138 (2022) 103638.
  • [38] E. Kim, T. Bui, J. Yuan, S. C. Mouli, B. Ribeiro, R. A. Yeh, M. B. Jun, Online real-time machining chatter sound detection using convolutional neural network by adopting expert knowledge. Manufacturing Letters, 41 (2024) 1386-1397
  • [39] J. Xie, P. Hu, J.Chen, W. Han, R. Wang, Deep learning-based instantaneous cutting force modeling of three-axis CNC milling. International Journal of Mechanical Sciences, 246 (2023) 108153.
  • [40] Z. Weiguo, Z. Jichao, G. Baosu, T. Weixiang, W. Fenghe, An optimized convolutional neural network for chatter detection in the milling of thin-walled parts, Int. J. Adv. Manuf. Technol. 106 (9) (2020) 3881–3895.
  • [41] W. Zhang, F. Teng, J. Li, Z. Zhang, L. Niu, D. Zhang, Z. Zhang, Denoising method based on CNN-LSTM and CEEMD for LDV signals from accelerometer shock testing. Measurement, 216 (2023) 112951.
  • [42] J. Duan, X. Zhang, T. Shi, A hybrid attention-based paralleled deep learning model for tool wear prediction. Expert Systems with Applications, 211 (2023), 118548.
There are 42 citations in total.

Details

Primary Language Turkish
Subjects Manufacturing Processes and Technologies (Excl. Textiles)
Journal Section Tasarım ve Teknoloji
Authors

Duygu Gürkan 0000-0002-2917-3330

Ahmet Mavi 0000-0003-0339-2639

İhsan Korkut 0000-0002-5001-4449

Project Number FGA-2023-8959
Early Pub Date May 16, 2025
Publication Date June 30, 2025
Submission Date February 5, 2025
Acceptance Date April 28, 2025
Published in Issue Year 2025 Volume: 13 Issue: 2

Cite

APA Gürkan, D., Mavi, A., & Korkut, İ. (2025). Titreşim Optimizasyonu için Akıllı Cnc Router Sistem Tasarımı. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji, 13(2), 462-470. https://doi.org/10.29109/gujsc.1633204
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