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MAKİNE ÖĞRENMESİ İLE ÜRETİM PERFORMANSI TAHMİNLEMESİ

Year 2025, Volume: 28 Issue: 1, 65 - 79, 03.03.2025

Semih Göksu , Bülent Sezen , Yavuz Selim Balcıoğlu

Abstract

Bu çalışmanın amacı, makine öğrenmesi algoritmaları kullanarak üretim performansının tahmin edilmesidir. Üretim sistemleri, çeşitli makineler, parametreler ve set değerler üzerinden çalışmakta olup, her bir üretim koşulu değişkenlik gösterebilmektedir. Gelişen teknolojiler sayesinde, bu değişkenliklerin kontrol altına alınması, üretim koşullarının optimize edilmesi ve birbirini etkileyen süreçlerden çıkarım yapılması mümkün hale gelmiştir. Bu bağlamda, makine öğrenmesi, istatistiksel metotlarla veri setleri üzerinden üretim performansının tahmin edilmesine olanak tanıyan önemli bir araçtır. Çalışmada, aynı ürün grubuna ait 2 yıllık veri kullanılarak üretim performansını tahmin etmek amacıyla Karar Ağacı, Lineer Regresyon, Lasso Regresyon, XGBoost, Destek Vektör Regresyonu ve LSTM algoritmaları uygulanmıştır. Bu algoritmalar, üç farklı senaryo üzerinden değerlendirilmiş ve üretim performansını en doğru şekilde tahmin edebilecek modelin belirlenmesi hedeflenmiştir. Elde edilen sonuçlar, basit ve karmaşık modellerin performanslarını karşılaştırarak üretim süreçlerinin iyileştirilmesine yönelik pratik öneriler sunmaktadır.

Keywords

makine öğrenmesi performans regresyon tahminleme üretim parametreleri

References

  • Adesiyan, A. (2021). Performance Prediction Of Production Lines Using Machine Learning Algorithm. https://doi.org/10.14293/s2199-1006.1.sor-.ppa7be8.v1.
  • Aktaş, B., & Aydın, C. (2018). Talaşlı imalat sektöründe zaman serileri kullanarak üretim etkililiğinin tahmini. Bilişim Teknolojileri Dergisi, 11(4), 407-416.
  • Al-Aomar, R., & Al-Okaily, A. (2006). A GA-based parameter design for single machine turning process with high-volume production. Comput. Ind. Eng., 50, 317-337. https://doi.org/10.1016/j.cie.2006.02.003.
  • Angayarkanni, G., & Hemalatha, S. (2023). Evaluating the performance of supervised machine learning algorithms for predicting multiple diseases: A comparative study. 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS).
  • Angelopoulos, A., Michailidis, E. T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., & Zahariadis, T. (2019). Tackling faults in the Industry 4.0 era—a survey of machine-learning solutions and key aspects. Sensors (Basel, Switzerland).
  • Batra, R., Abbi, P., Sharma, R., Agarwal, H., & Bhulania, P. (2023). Production prediction using machine learning. 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN), 399-404. https://doi.org/10.1109/SPIN57001.2023.10116936.
  • Ben-Moshe, D. (2021). IDENTIFICATION OF LINEAR REGRESSIONS WITH ERRORS IN ALL VARIABLES. Econometric Theory, 37(4), 633–663. https://doi.org/10.1017/S0266466620000250
  • Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. https://doi.org/10.5555/1162264
  • Chandra Prabha, S., & Lakshmi, S. (2023). Data analysis and machine learning-based modeling for real-time production. The Scientific Temper. https://doi.org/10.58414/scientifictemper.2023.14.2.22.
  • Chen, Y., Zhou, Y., & Zhang, Y. (2020). Collaborative Production Planning with Unknown Parameters using Model Predictive Control and Machine Learning. 2020 Chinese Automation Congress (CAC), 2185-2190. https://doi.org/10.1109/CAC51589.2020.9326614.
  • Chen, Y., Zhou, Y., & Zhang, Y. (2021). Machine Learning-Based Model Predictive Control for Collaborative Production Planning Problem with Unknown Information. Electronics. https://doi.org/10.3390/electronics10151818.
  • Chen, Z., & Fan, W. (2021). A freeway travel time prediction method based on an XGBoost model. Sustainability, 13(15), 8577. https://doi.org/10.3390/su13158577
  • Cinar, Z., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M. B. A., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in Industry 4.0. Sustainability.
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. https://doi.org/10.1007/BF00994018
  • Gültepe, Y. (2019). Makine öğrenmesi algoritmaları ile hava kirliliği tahmini üzerine karşılaştırmalı bir değerlendirme. Avrupa Bilim ve Teknoloji Dergisi, (16), 8-15.
  • Hitam, N. A., & Ismail, A. R. (2018). Comparative performance of machine learning algorithms for cryptocurrency forecasting. Indonesian Journal of Electrical Engineering and Computer Science.
  • James, G., Witten, D., Hastie, T., Tibshirani, R., James, G., Witten, D., ... & Tibshirani, R. (2021). Linear model selection and regularization. An introduction to statistical learning: with applications in R, 225-288.
  • Jun, Z. (2021). The development and application of support vector machine. In Journal of Physics: Conference Series (Vol. 1748, No. 5, p. 052006). IOP Publishing.
  • Kathiresan, V., Dinesh, G., Sarveshwaran, V., Jayakanth, J. J., & Kiruthika, M. (2023). Comparative analysis of diverse classification algorithms of machine learning by using various quality metrics. 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA).
  • Khdoudi, A., Masrour, T., & Mazgualdi, C. (2019). Using Machine Learning Algorithms for the Prediction of Industrial Process Parameters Based on Product Design. , 728-749. https://doi.org/10.1007/978-3-030-36671-1_67.
  • Klusowski, J. M., & Tian, P. M. (2024). Large scale prediction with decision trees. Journal of the American Statistical Association, 119(545), 525-537.
  • Leha, A., Pangercic, D., Rühr, T., & Beetz, M. (2009). Optimization of simulated production process performance using machine learning. 2009 IEEE Conference on Emerging Technologies & Factory Automation, 1-5. https://doi.org/10.1109/ETFA.2009.5347229.
  • Lutay, V. N., & Khusainov, N. S. (2021, November). The selective regularization of a linear regression model. In Journal of Physics: Conference Series (Vol. 2099, No. 1, p. 012024). IOP Publishing
  • Meng, L., McWilliams, B., Jarosinski, W., Park, H., Jung, Y., Lee, J., & Zhang, J. (2020). Machine Learning in Additive Manufacturing: A Review. JOM, 72, 2363 - 2377. https://doi.org/10.1007/s11837-020-04155-y.
  • Mezentsev, A., & Yasnitsky, L. (2022). Neural network model for determining the regulations parameters in the technological process of ore raw materials processing. Journal Of Applied Informatics. https://doi.org/10.37791/2687-0649-2022-17-6-56-67.
  • Mokrova, N., Mokrov, A., Safonova, A., & Vishnyakov, I. (2018). Machine Learning Methods for Production Cases Analysis. IOP Conference Series: Materials Science and Engineering, 317. https://doi.org/10.1088/1757-899X/317/1/012044.
  • Moura, M., Zio, E., Lins, I. D., & Droguett, E. (2011). Failure and reliability prediction by support vector machines regression of time series data. Reliability Engineering & System Safety, 96(12), 1527–1534.
  • Noshi, C., Eissa, M., & Abdalla, R. (2019). An Intelligent Data Driven Approach for Production Prediction. Day 4 Thu, May 09, 2019. https://doi.org/10.4043/29243-MS.
  • Rahul, R., Tiwari, M., Ivanov, D., & Dolgui, A. (2021). Machine learning in manufacturing and Industry 4.0 applications. International Journal of Production Research, 59(18), 4773-4778.
  • Ramachandran, K. M., & Tsokos, C. P. (2020). Mathematical statistics with applications in R. Academic Press.
  • Rimpault, X., Balazinski, M., & Chatelain, J. (2018). Fractal analysis application outlook for improving process monitoring and machine maintenance in Manufacturing 4.0. Journal of Manufacturing and Materials Processing.
  • Sevvanthi, K., Ganapathy, S., Penumadu, P., & Harichandrakumar, K. T. (2023). Comparing the predictive performance of a decision tree with logistic regression for oral cavity cancer mortality: A retrospective study. Cancer Research, Statistics, and Treatment, 6(1), 103-110.
  • Sittón-Candanedo, I., Hernández Nieves, E., Rodríguez, S., Santos-Martín, M., & González-Briones, A. (2018). Machine learning predictive model for Industry 4.0. In Proceedings of the 16th International Conference on Information Systems, Management, and Automation (pp. 501-510).
  • Strasser, S., Tripathi, S., & Kerschbaumer, R. (2018). An Approach for Adaptive Parameter Setting in Manufacturing Processes. , 24-32. https://doi.org/10.5220/0006894600240032.
  • Sun, Y., & Zhang, L. (2020). Parameter Identification for Multiple-Machine Bernoulli Lines using Statistical Learning Methods. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), 810-815. https://doi.org/10.1109/CASE48305.2020.9216960.
  • Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer. https://doi.org/10.1007/978-1-4757-2440-0
  • Zhang, P., Jia, Y., & Shang, Y. (2022). Research and application of XGBoost in imbalanced data. International Journal of Distributed Sensor Networks, 18(6), 15501329221106935.

PREDICT OF PRODUCTION PERFORMANCE WITH MACHINE LEARNING

Year 2025, Volume: 28 Issue: 1, 65 - 79, 03.03.2025

Semih Göksu , Bülent Sezen , Yavuz Selim Balcıoğlu

Abstract

The aim of this study is to predict production performance using machine learning algorithms. Production systems operate based on various machines, parameters, and set values, with each production condition potentially exhibiting variability. Advances in technology have made it possible to control these variabilities, optimize production conditions, and derive insights from interconnected processes. In this context, machine learning serves as a valuable tool that enables the prediction of production performance through statistical methods applied to datasets. In this study, two years of data from the same product group were used to predict production performance through the application of Decision Tree, Linear Regression, Lasso Regression, XGBoost, Support Vector Regression, and LSTM algorithms. These algorithms were evaluated across three different scenarios, with the goal of identifying the model that can most accurately predict production performance. The results provide practical insights into improving production processes by comparing the performance of simple and complex models.

Keywords

machine learning performance regression prediction production parameters

References

  • Adesiyan, A. (2021). Performance Prediction Of Production Lines Using Machine Learning Algorithm. https://doi.org/10.14293/s2199-1006.1.sor-.ppa7be8.v1.
  • Aktaş, B., & Aydın, C. (2018). Talaşlı imalat sektöründe zaman serileri kullanarak üretim etkililiğinin tahmini. Bilişim Teknolojileri Dergisi, 11(4), 407-416.
  • Al-Aomar, R., & Al-Okaily, A. (2006). A GA-based parameter design for single machine turning process with high-volume production. Comput. Ind. Eng., 50, 317-337. https://doi.org/10.1016/j.cie.2006.02.003.
  • Angayarkanni, G., & Hemalatha, S. (2023). Evaluating the performance of supervised machine learning algorithms for predicting multiple diseases: A comparative study. 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS).
  • Angelopoulos, A., Michailidis, E. T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., & Zahariadis, T. (2019). Tackling faults in the Industry 4.0 era—a survey of machine-learning solutions and key aspects. Sensors (Basel, Switzerland).
  • Batra, R., Abbi, P., Sharma, R., Agarwal, H., & Bhulania, P. (2023). Production prediction using machine learning. 2023 10th International Conference on Signal Processing and Integrated Networks (SPIN), 399-404. https://doi.org/10.1109/SPIN57001.2023.10116936.
  • Ben-Moshe, D. (2021). IDENTIFICATION OF LINEAR REGRESSIONS WITH ERRORS IN ALL VARIABLES. Econometric Theory, 37(4), 633–663. https://doi.org/10.1017/S0266466620000250
  • Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. https://doi.org/10.5555/1162264
  • Chandra Prabha, S., & Lakshmi, S. (2023). Data analysis and machine learning-based modeling for real-time production. The Scientific Temper. https://doi.org/10.58414/scientifictemper.2023.14.2.22.
  • Chen, Y., Zhou, Y., & Zhang, Y. (2020). Collaborative Production Planning with Unknown Parameters using Model Predictive Control and Machine Learning. 2020 Chinese Automation Congress (CAC), 2185-2190. https://doi.org/10.1109/CAC51589.2020.9326614.
  • Chen, Y., Zhou, Y., & Zhang, Y. (2021). Machine Learning-Based Model Predictive Control for Collaborative Production Planning Problem with Unknown Information. Electronics. https://doi.org/10.3390/electronics10151818.
  • Chen, Z., & Fan, W. (2021). A freeway travel time prediction method based on an XGBoost model. Sustainability, 13(15), 8577. https://doi.org/10.3390/su13158577
  • Cinar, Z., Abdussalam Nuhu, A., Zeeshan, Q., Korhan, O., Asmael, M. B. A., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in Industry 4.0. Sustainability.
  • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. https://doi.org/10.1007/BF00994018
  • Gültepe, Y. (2019). Makine öğrenmesi algoritmaları ile hava kirliliği tahmini üzerine karşılaştırmalı bir değerlendirme. Avrupa Bilim ve Teknoloji Dergisi, (16), 8-15.
  • Hitam, N. A., & Ismail, A. R. (2018). Comparative performance of machine learning algorithms for cryptocurrency forecasting. Indonesian Journal of Electrical Engineering and Computer Science.
  • James, G., Witten, D., Hastie, T., Tibshirani, R., James, G., Witten, D., ... & Tibshirani, R. (2021). Linear model selection and regularization. An introduction to statistical learning: with applications in R, 225-288.
  • Jun, Z. (2021). The development and application of support vector machine. In Journal of Physics: Conference Series (Vol. 1748, No. 5, p. 052006). IOP Publishing.
  • Kathiresan, V., Dinesh, G., Sarveshwaran, V., Jayakanth, J. J., & Kiruthika, M. (2023). Comparative analysis of diverse classification algorithms of machine learning by using various quality metrics. 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA).
  • Khdoudi, A., Masrour, T., & Mazgualdi, C. (2019). Using Machine Learning Algorithms for the Prediction of Industrial Process Parameters Based on Product Design. , 728-749. https://doi.org/10.1007/978-3-030-36671-1_67.
  • Klusowski, J. M., & Tian, P. M. (2024). Large scale prediction with decision trees. Journal of the American Statistical Association, 119(545), 525-537.
  • Leha, A., Pangercic, D., Rühr, T., & Beetz, M. (2009). Optimization of simulated production process performance using machine learning. 2009 IEEE Conference on Emerging Technologies & Factory Automation, 1-5. https://doi.org/10.1109/ETFA.2009.5347229.
  • Lutay, V. N., & Khusainov, N. S. (2021, November). The selective regularization of a linear regression model. In Journal of Physics: Conference Series (Vol. 2099, No. 1, p. 012024). IOP Publishing
  • Meng, L., McWilliams, B., Jarosinski, W., Park, H., Jung, Y., Lee, J., & Zhang, J. (2020). Machine Learning in Additive Manufacturing: A Review. JOM, 72, 2363 - 2377. https://doi.org/10.1007/s11837-020-04155-y.
  • Mezentsev, A., & Yasnitsky, L. (2022). Neural network model for determining the regulations parameters in the technological process of ore raw materials processing. Journal Of Applied Informatics. https://doi.org/10.37791/2687-0649-2022-17-6-56-67.
  • Mokrova, N., Mokrov, A., Safonova, A., & Vishnyakov, I. (2018). Machine Learning Methods for Production Cases Analysis. IOP Conference Series: Materials Science and Engineering, 317. https://doi.org/10.1088/1757-899X/317/1/012044.
  • Moura, M., Zio, E., Lins, I. D., & Droguett, E. (2011). Failure and reliability prediction by support vector machines regression of time series data. Reliability Engineering & System Safety, 96(12), 1527–1534.
  • Noshi, C., Eissa, M., & Abdalla, R. (2019). An Intelligent Data Driven Approach for Production Prediction. Day 4 Thu, May 09, 2019. https://doi.org/10.4043/29243-MS.
  • Rahul, R., Tiwari, M., Ivanov, D., & Dolgui, A. (2021). Machine learning in manufacturing and Industry 4.0 applications. International Journal of Production Research, 59(18), 4773-4778.
  • Ramachandran, K. M., & Tsokos, C. P. (2020). Mathematical statistics with applications in R. Academic Press.
  • Rimpault, X., Balazinski, M., & Chatelain, J. (2018). Fractal analysis application outlook for improving process monitoring and machine maintenance in Manufacturing 4.0. Journal of Manufacturing and Materials Processing.
  • Sevvanthi, K., Ganapathy, S., Penumadu, P., & Harichandrakumar, K. T. (2023). Comparing the predictive performance of a decision tree with logistic regression for oral cavity cancer mortality: A retrospective study. Cancer Research, Statistics, and Treatment, 6(1), 103-110.
  • Sittón-Candanedo, I., Hernández Nieves, E., Rodríguez, S., Santos-Martín, M., & González-Briones, A. (2018). Machine learning predictive model for Industry 4.0. In Proceedings of the 16th International Conference on Information Systems, Management, and Automation (pp. 501-510).
  • Strasser, S., Tripathi, S., & Kerschbaumer, R. (2018). An Approach for Adaptive Parameter Setting in Manufacturing Processes. , 24-32. https://doi.org/10.5220/0006894600240032.
  • Sun, Y., & Zhang, L. (2020). Parameter Identification for Multiple-Machine Bernoulli Lines using Statistical Learning Methods. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), 810-815. https://doi.org/10.1109/CASE48305.2020.9216960.
  • Vapnik, V. (1995). The Nature of Statistical Learning Theory. Springer. https://doi.org/10.1007/978-1-4757-2440-0
  • Zhang, P., Jia, Y., & Shang, Y. (2022). Research and application of XGBoost in imbalanced data. International Journal of Distributed Sensor Networks, 18(6), 15501329221106935.
There are 37 citations in total.

Details

Primary Language Turkish
Subjects Artificial Intelligence (Other)
Journal Section Computer Engineering
Authors

Semih Göksu 0009-0007-8158-6654

Bülent Sezen 0000-0001-7485-3194

Yavuz Selim Balcıoğlu 0000-0001-7138-2972

Publication Date March 3, 2025
Submission Date July 16, 2024
Acceptance Date October 30, 2024
Published in Issue Year 2025Volume: 28 Issue: 1

Cite

APA Göksu, S., Sezen, B., & Balcıoğlu, Y. S. (2025). MAKİNE ÖĞRENMESİ İLE ÜRETİM PERFORMANSI TAHMİNLEMESİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 65-79.
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