Hybrid Modeling of Cutting Forces in Turning of Engineering Polymers Using Kienzle Approach and Data Augmentation-Enhanced Random Forest Algorithm

Ali Osman Er

doi:10.47897/bilmes.1912992

EN TR

Hybrid Modeling of Cutting Forces in Turning of Engineering Polymers Using Kienzle Approach and Data Augmentation-Enhanced Random Forest Algorithm

Abstract

In this study, the turning processes of Polyamide 6 (PA 6) and High-Density Polyethylene (HDPE) engineering polymers were analyzed by integrating experimental, mathematical, and machine learning-based methods. Within the framework of a Taguchi L8 experimental design, the effects of cutting speed (Vc), cutting depth (ap), and feed rate (f) on cutting forces (Fc) were investigated. ANOVA results revealed that cutting depth was the most dominant factor for both materials (44.49% for PA 6 and 64.49% for HDPE), while PA 6 was found to be twice as sensitive to feed rate variations compared to HDPE.Through the Kienzle mathematical approach, specific cutting force coefficients (kc1.1) were calculated as 152.45 N/mm^2 for PA 6 and 74.89 N/mm^2 for HDPE. However, to overcome the predictive limitations of traditional models caused by the viscoelastic and non-linear behavior of polymers, the Random Forest (RF) regression algorithm was incorporated into the process. To enhance the model's generalization capability under limited data conditions, a Data Augmentation technique was employed.The findings demonstrated that the RF model, supported by data augmentation, achieved remarkably high average accuracy rates of 99.09% for HDPE and 91.53% for PA 6. This research proves that hybrid approaches, where traditional physical models are supported by machine learning, provide a high-reliability force prediction infrastructure for the precision manufacturing of polymer-based components and digital twin applications.

Keywords

Polyamide 6, HDPE, Kienzle model, random forest, machine learning, data augmentation, cutting force, size effect

Supporting Institution

Kırıkkale University

Project Number

This research received no specific grant.

Ethical Statement

The authors declare that this study does not require ethical approval as it does not involve human or animal subjects.

Thanks

The author would like to thank Kırıkkale University for providing the laboratory facilities and support during the experimental phase of this study.

Hybrid Modeling of Cutting Forces in Turning of Engineering Polymers Using Kienzle Approach and Data Augmentation-Enhanced Random Forest Algorithm

Abstract

In this study, the turning processes of Polyamide 6 (PA 6) and High-Density Polyethylene (HDPE) engineering polymers were analyzed by integrating experimental, mathematical, and machine learning-based methods. Within the framework of a Taguchi L8 experimental design, the effects of cutting speed (Vc), cutting depth (ap), and feed rate (f) on cutting forces (Fc) were investigated. ANOVA results revealed that cutting depth was the most dominant factor for both materials (44.49% for PA 6 and 64.49% for HDPE), while PA 6 was found to be twice as sensitive to feed rate variations compared to HDPE.Through the Kienzle mathematical approach, specific cutting force coefficients (kc1.1) were calculated as 152.45 N/mm^2 for PA 6 and 74.89 N/mm^2 for HDPE. However, to overcome the predictive limitations of traditional models caused by the viscoelastic and non-linear behavior of polymers, the Random Forest (RF) regression algorithm was incorporated into the process. To enhance the model's generalization capability under limited data conditions, a Data Augmentation technique was employed.The findings demonstrated that the RF model, supported by data augmentation, achieved remarkably high average accuracy rates of 99.09% for HDPE and 91.53% for PA 6. This research proves that hybrid approaches, where traditional physical models are supported by machine learning, provide a high-reliability force prediction infrastructure for the precision manufacturing of polymer-based components and digital twin applications.

Keywords

Polyamide 6, HDPE, Kienzle model, random forest, machine learning, data augmentation, cutting force, size effect

Supporting Institution

Kırıkkale University

Project Number

This research received no specific grant.

Ethical Statement

The authors declare that this study does not require ethical approval as it does not involve human or animal subjects.

Thanks

The author would like to thank Kırıkkale University for providing the laboratory facilities and support during the experimental phase of this study.

References

Xiao, K. Q., & Zhang, L. C. The role of viscous deformation in the machining of polymers. International Journal of Mechanical Sciences, 44(11), 2317-2336, 2002, doi: 10.1016/S0020-7403(02)00178-9.
Sheikh-Ahmad, J. Y. Machining of Polymer Composites. Springer Science & Business Media, ISBN 978-0-387-35539-9, 2009, doi: 10.1007/978-0-387-68619-6.
Jagtap, T. U., & Mandave, H. A. Machining of Plastics: A Review. International Journal of Engineering Research and General Science, 3(2), 2091-2730, 2015.
Davim, J. P., & Mata, F. A comparative evaluation of the turning of reinforced and unreinforced polyamide. International Journal of Advanced Manufacturing Technology, 33(9-10), 911-914, 2007, doi: 10.1007/s00170-006-0520-8.
Kaddeche, M., Chaoui, K., & Yallese, M. A. Cutting parameters effects on the machining of two high density polyethylene pipes resins. Mechanics & Industry, 13(5), 307-316, 2012, doi: 10.1051/meca/2012029.
Gaitonde, V. N., Karnik, S. R., Mata, F., & Davim, J. P. Modeling and Analysis of Machinability Characteristics in PA6 and PA66 GF30 Polyamides through Artificial Neural Network. Journal of Thermoplastic Composite Materials, 23(3), 313-336, 2010, doi: 10.1177/0892705709349319.
Madić, M., Radovanović, M., & Marković, D. Optimization of Surface Roughness When Turning Polyamide Using ANN-IHSA Approach. International Journal of Engineering and Technology, 1(4), 432-443, 2012.
Chabbi, A., Yallese, M. A., Nouioua, M., Meddour, I., Mabrouki, T., & Girardin, F. Modeling and optimization of turning process parameters during the cutting of polymer (POM C) based on RSM, ANN, and DF methods. International Journal of Advanced Manufacturing Technology, 91(5-8), 2267-2290, 2017, doi: 10.1007/s00170-016-9858-8.
Yan, Y., Mao, Y., Li, B., & Zhou, P. Machinability of the Thermoplastic Polymers: PEEK, PI, and PMMA. Polymers, 13(1), 69, 2021, doi: 10.3390/polym13010069.
Kaddeche, M., Boucherit, S., Belhadi, S., & Yallese, M. A. Comparative study of turning two engineering plastics (POM-C and PA-6) and optimisation using GA, SA, GRA and COPRAS with and without weighting (Entropie, critic, Swara, ROC). Research Square (Preprint), 2023, doi: 10.21203/rs.3.rs-2803990/v1.

Tongur, M., & Ataberk, N. Experimental Investigation of the Machinability of Basalt Fiber Reinforced Epoxy Matrix Composites and the Effect of Nanoparticle Additives on Machining Quality. Gazi Journal of Engineering Sciences, 11(2), 274-290, 2025, doi: 10.30855/gmbd.070525A11.
Preez, A., & Afonso, P. Machine learning in cutting processes as enabler for smart sustainable manufacturing. Procedia Manufacturing, 33, 810–817, 2019, doi: 10.1016/j.promfg.2019.04.102.
Mia, M., Razi, M. H., Ahmad, I., Mostafa, R., Rahman, S. M. S., & Dhar, N. R. Predictive modeling of turning operations under different cooling-lubricating conditions for sustainable manufacturing with machine learning techniques. Journal of Advanced Manufacturing Technology, 20, 162–180, 2024, doi: 10.1016/j.aci.2020.02.001.
Farooq, M. U., Kumar, R., Khan, A., Singh, J., Anwar, S., & Haber, R. Sustainable machining of Inconel 718 using minimum quantity lubrication: Artificial intelligence-based process modelling. Heliyon, 10(15), e34836, 2024, doi: 10.1016/j.heliyon.2024.e34836.
Brillinger, M., & Diestel, O. Energy prediction for CNC machining with machine learning. CIRP Journal of Manufacturing Science and Technology, 35, 715–723, 2021, doi: 10.1016/j.cirpj.2021.07.014.
Ishfaq, K., Anjum, I., Pruncu, C. I., Amjad, M., Kumar, M. S., & Maqsood, M. A. Progressing towards sustainable machining of steels: A detailed review. Materials, 14, 5162, 2021, doi: 10.3390/ma14185162.
Outeiro, J., Cheng, W., Chinesta, F., & Ammar, A. Modelling and optimization of machining of Ti-6Al-4V titanium alloy using machine learning and design of experiments methods. Journal of Manufacturing and Materials Processing, 6, 58, 2022, doi: 10.3390/jmmp6030058.
Salem, A., Hegab, H., Rahnamayan, S., & Kishawy, H. A. Multi-objective optimization and innovization-based knowledge discovery of sustainable machining process. Journal of Manufacturing Systems, 64, 636–647, 2022, doi: 10.1016/j.jmsy.2022.04.013.
Wu, P., He, Y., Li, Y., He, J., Liu, X., & Wang, Y. Multi-objective optimisation of machining process parameters using deep learning-based data-driven genetic algorithm and TOPSIS. Journal of Manufacturing Systems, 64, 40–52, 2022, doi: 10.1016/j.jmsy.2022.05.016.
Zhou, H., & Di, Y. Thematic analysis of sustainable ultra-precision machining by using text mining and unsupervised learning method. Journal of Manufacturing Systems, 62, 218–233, 2022, doi: 10.1016/j.jmsy.2021.11.013.
Korkmaz, M. E., Gupta, M. K., Niesłony, P., Sarikaya, M., & Kuntoğlu, M. Implementation of green cooling-lubrication strategies in metal cutting industries: A state of the art towards sustainable future and challenges. Journal of Cleaner Production, 123, 456–478, 2023, doi: 10.1016/j.susmat.2023.e00641.
Yan, Y., Mao, Y., Li, B., & Zhou, P. Machinability of the Thermoplastic Polymers: PEEK, PI, and PMMA. Polymers, 13, 69, 2021, doi: 10.3390/polym13010069.

Details

Primary Language

Turkish

Subjects

Optimization Techniques in Mechanical Engineering, Industrial Engineering, Machining, Optimization in Manufacturing

Journal Section

Research Article

Authors

Ali Osman Er ^*
0000-0002-9637-8926
Türkiye

Publication Date

June 19, 2026

Submission Date

April 28, 2026

Acceptance Date

June 15, 2026

Published in Issue

Year 2026 Volume: 10 Number: 1

DOI

https://doi.org/10.47897/bilmes.1912992

IZ

https://izlik.org/JA58CZ44UD

APA

Er, A. O. (2026). Hybrid Modeling of Cutting Forces in Turning of Engineering Polymers Using Kienzle Approach and Data Augmentation-Enhanced Random Forest Algorithm. International Scientific and Vocational Studies Journal, 10(1), 26-39. https://doi.org/10.47897/bilmes.1912992

AMA

1.Er AO. Hybrid Modeling of Cutting Forces in Turning of Engineering Polymers Using Kienzle Approach and Data Augmentation-Enhanced Random Forest Algorithm. ISVOS. 2026;10(1):26-39. doi:10.47897/bilmes.1912992

Chicago

Er, Ali Osman. 2026. “Hybrid Modeling of Cutting Forces in Turning of Engineering Polymers Using Kienzle Approach and Data Augmentation-Enhanced Random Forest Algorithm”. International Scientific and Vocational Studies Journal 10 (1): 26-39. https://doi.org/10.47897/bilmes.1912992.

EndNote

Er AO (June 1, 2026) Hybrid Modeling of Cutting Forces in Turning of Engineering Polymers Using Kienzle Approach and Data Augmentation-Enhanced Random Forest Algorithm. International Scientific and Vocational Studies Journal 10 1 26–39.

IEEE

[1]A. O. Er, “Hybrid Modeling of Cutting Forces in Turning of Engineering Polymers Using Kienzle Approach and Data Augmentation-Enhanced Random Forest Algorithm”, ISVOS, vol. 10, no. 1, pp. 26–39, June 2026, doi: 10.47897/bilmes.1912992.

ISNAD

Er, Ali Osman. “Hybrid Modeling of Cutting Forces in Turning of Engineering Polymers Using Kienzle Approach and Data Augmentation-Enhanced Random Forest Algorithm”. International Scientific and Vocational Studies Journal 10/1 (June 1, 2026): 26-39. https://doi.org/10.47897/bilmes.1912992.

JAMA

1.Er AO. Hybrid Modeling of Cutting Forces in Turning of Engineering Polymers Using Kienzle Approach and Data Augmentation-Enhanced Random Forest Algorithm. ISVOS. 2026;10:26–39.

MLA

Er, Ali Osman. “Hybrid Modeling of Cutting Forces in Turning of Engineering Polymers Using Kienzle Approach and Data Augmentation-Enhanced Random Forest Algorithm”. International Scientific and Vocational Studies Journal, vol. 10, no. 1, June 2026, pp. 26-39, doi:10.47897/bilmes.1912992.

Vancouver

1.Ali Osman Er. Hybrid Modeling of Cutting Forces in Turning of Engineering Polymers Using Kienzle Approach and Data Augmentation-Enhanced Random Forest Algorithm. ISVOS. 2026 Jun. 1;10(1):26-39. doi:10.47897/bilmes.1912992

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Hybrid Modeling of Cutting Forces in Turning of Engineering Polymers Using Kienzle Approach and Data Augmentation-Enhanced Random Forest Algorithm

Hybrid Modeling of Cutting Forces in Turning of Engineering Polymers Using Kienzle Approach and Data Augmentation-Enhanced Random Forest Algorithm

Abstract

Keywords

Supporting Institution

Project Number

Ethical Statement

Thanks

Hybrid Modeling of Cutting Forces in Turning of Engineering Polymers Using Kienzle Approach and Data Augmentation-Enhanced Random Forest Algorithm

Abstract

Keywords

Supporting Institution

Project Number

Ethical Statement

Thanks

References

Details

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Authors

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Acceptance Date

Published in Issue

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IZ

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