Intelligent multi-model system for surface roughness prediction in CNC turning of multiple materials

Rohmat Rohmat; Dianda Aryntya Firia Ferlania; Donni Frediansah Mukminin

doi:10.30811/jpl.v24i1.8185

Intelligent multi-model system for surface roughness prediction in CNC turning of multiple materials

Rohmat Rohmat, Dianda Aryntya Firia Ferlania, Donni Frediansah Mukminin

Abstract

Surface roughness is a critical indicator of machining quality that directly affects product performance and service life. However, most existing prediction studies focus on single-material machining and rely on a single predictive model, limiting their effectiveness in real industrial environments where multiple materials are commonly processed. To address this gap, this study proposes an intelligent multimodel system for surface roughness prediction in CNC turning of multiple materials. The experimental investigation was carried out using two commonly applied steels, ST41 and S45C, with 81 machining trials performed for each material. Vibration signals were recorded using a three-axis accelerometer and combined with machining parameters consisting of feed rate, spindle speed, and depth of cut. The acquired signals were analyzed in both time and frequency domains through Fourier transformation, resulting in the extraction of eighteen vibration-related features that were normalized and used as model inputs. Three prediction techniques, namely Multiple Linear Regression, Support Vector Regression, and Artificial Neural Networks, were developed and integrated within the proposed system. System performance was evaluated using Mean Absolute Percentage Error (MAPE) and statistically analyzed through one-way ANOVA and Tukey post-hoc tests. The results demonstrate that the ANN model consistently achieved the highest prediction accuracy, with MAPE values of 2.81% for S45C, 4.72% for ST41, and 4.42% for the combined-material dataset, outperforming the Regression and SVR models. These results confirm that the proposed intelligent multimodel system provides a robust, accurate, and practical solution for vibration-based surface roughness prediction in CNC turning of multiple materials.

Keywords

Intelligent multi-model; Prediction; Surface roughness; Multiple materials; CNC turning

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References

[1] P. T. B.Huang, M. M. W.Inderawati, R.Rohmat, andR.Sukwadi, “The development of an ANN surface roughness prediction system of multiple materials in CNC turning,” Int. J. Adv. Manuf. Technol., vol. 125, no. 3–4, pp. 1193–1211, 2023, doi: 10.1007/s00170-022-10709-y.

A. T.Nguyen, V. H.Nguyen, T. T.Le, andN. T.Nguyen, “a Hybridization of Machine Learning and Nsga-Ii for Multi-Objective Optimization of Surface Roughness and Cutting Force in Aisi 4340 Alloy Steel Turning,” J. Mach. Eng., vol. 23, no. 1, pp. 133–153, 2023, doi: 10.36897/jme/160172.

J.Chen, J.Lin, M.Zhang, andQ.Lin, “Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression,” Sensors, vol. 24, no. 7, 2024, doi: 10.3390/s24072117.

E.Stathatos, E.Tzimas, P.Benardos, andG. C.Vosniakos, “Convolutional Neural Networks for Raw Signal Classification in CNC Turning Process Monitoring,” Sensors, vol. 24, no. 5, 2024, doi: 10.3390/s24051390.

A.Tzotzis, P.Maropoulos, andP.Kyratsis, “A dynamic surface roughness prediction system based on machine learning for the 3D-printed carbon-fiber-reinforced-polymer ( CFRP ) turning,” J. Intell. Manuf., vol. 7075, 2025, doi: 10.1007/s10845-025-02602-8.

X.-Steel, “Statistical Analysis of Cutting Force and Vibration in Turning,” 2025.

V.Ganachari, A.Ašonja, S.Shirguppikar, andR. U.Kakade, “A Sustainable Manufacturing Approach : Experimental and Machine Learning-Based Surface Roughness Modelling in PMEDM,” pp. 1–15, 2026.

O.Ulkir andF.Kuncan, “Experimental Study and ANN Development for Modeling Tensile and Surface Quality of Fiber-Reinforced Nylon Composites,” pp. 1–23, 2025.

K.Li, B.Decost, M.Greenwood, andJ.Hattrick-simpers, “OPEN A critical examination of robustness and generalizability of machine learning prediction of materials properties,” pp. 1–9, 2021, doi: 10.1038/s41524-023-01012-9.

E.Ayhan, S.Güner, M.Yurdakul, andY. T.İç, “Robust parameter design for EDM ‑ based,” J. Eng. Appl. Sci., pp. 1–20, 2025, doi: 10.1186/s44147-025-00618-8.

Y.Akiyama, M.Iwaki, Y.Komagamine, S.Minakuchi, andM.Kanazawa, “applied sciences Effect of Spindle Speed and Feed Rate on Surface Roughness and Milling Duration in the Fabrication of Milled Complete Dentures : An In Vitro Study,” 2023.

M.Kodrič, J.Korbar, M.Pogačar, andG.Čepon, “Development of a resource-efficient real-time vibration-based tool condition monitoring system using PVDF accelerometers,” Measurement, vol. 251, no. February, p. 117183, 2025, doi: 10.1016/j.measurement.2025.117183.

G.Apostolou et al., “Novel Framework for Quality Control in Vibration Monitoring of CNC Machining,” pp. 1–20, 2024.

N.Jouini, J. A.Ghani, andS.Yaqoob, “Optimized Machining Parameters for High-Speed Turning Process : A Comparative Study of Dry and Cryo + MQL Techniques,” pp. 1–18, 2025.

C.Srivabut, S.Rawangwong, S.Hiziroglu, andC.Homkhiew, “Composites Part C : Open Access Multi-objective optimization of turning process parameters and wood sawdust contents using response surface methodology for the minimized surface roughness of recycled plastic / wood sawdust composites,” Compos. Part C Open Access, vol. 14, no. May, p. 100477, 2024, doi: 10.1016/j.jcomc.2024.100477.

A. G.Tefera, D. K.Sinha, andG.Gupta, “Experimental investigation and optimization of cutting parameters during dry turning process of copper alloy,” J. Eng. Appl. Sci., pp. 1–26, 2023, doi: 10.1186/s44147-023-00314-5.

Y.Kikuchi et al., “Machine Learning to Predict Faricimab Treatment Outcome in Neovascular Age-Related Macular Degeneration,” Ophthalmol. Sci., vol. 4, no. 2, p. 100385, 2023, doi: 10.1016/j.xops.2023.100385.

V.Knights, O.Petrovska, J.Bunevska-talevska, andM.Prchkovska, “Machine Learning Models and Mathematical Approaches for Predictive IoT Smart Parking,” 2025.

R.Jafar, A.Awad, I.Hatem, K.Jafar, E.Awad, andI.Shahrour, “smart cities Multiple Linear Regression and Machine Learning for Predicting the Drinking Water Quality Index in Al-Seine Lake,” pp. 2807–2827, 2023.

M.Čistý, G.Doláková, andZ.Štefunková, “through Regression Analysis : Application in Slovakia,” Environ. Process., 2025, doi: 10.1007/s40710-025-00747-5.

L. M.Cozmuta, “Heliyon The application of multiple linear regression methods to FTIR spectra of fingernails for predicting gender and age of human subjects,” Heliyon, vol. 11, no. 4, p. e42815, 2025, doi: 10.1016/j.heliyon.2025.e42815.

B.Process, “Developing a Support Vector Regression ( SVR ) Model for Prediction of Main and Lateral Bending Angles in Laser Tube,” 2023.

B.Bargam, A.Boudhar, C.Kinnard, H.Bouamri, andK.Nifa, “Evaluation of the support vector regression ( SVR ) and the random forest ( RF ) models accuracy for streamflow prediction under a data ‑ scarce basin in Morocco,” Discov. Appl. Sci., 2024, doi: 10.1007/s42452-024-05994-z.

G.Duan, Y.Du, Y.Shang, H.Xue, andR.Zhang, “Research on Support Vector Regression Short-Time Traffic Flow Prediction Model for Secondary Roads Based on Associated Road Analysis,” 2025.

G.Kim andY.Bak, “Forward and Backpropagation-Based Artificial Neural Network Modeling Method for Power Conversion System,” pp. 1–17, 2025.

V.Kartal, “Prediction of monthly evapotranspiration by artificial neural network model development with Levenberg – Marquardt method in Elazig ,” Environ. Sci. Pollut. Res., vol. 31, no. 14, pp. 20953–20969, 2024, doi: 10.1007/s11356-024-32464-1.

H.Begi, “Comparing MLR and ANN models for school building electrical energy prediction in Osijek-Baranja County in Croatia,” vol. 12, no. March, pp. 3595–3606, 2024, doi: 10.1016/j.egyr.2024.09.039.

J. B.Deb, C.Varela, F.Faysal, Y.Wang, andC.Maiti, “Deep Artificial Neural Network Modeling of the Ablation Performance of Ceramic Matrix Composites in the Hydrogen Torch Test,” 2025.

P. M.Learning, “Milling Surface Roughness Prediction Based on,” no. Learning, P. M. (2023). Milling Surface Roughness Prediction Based on., 2023.

K. S.Pieczarka, “Estimating Energy Consumption During Soil Cultivation Using Geophysical Scanning and Machine Learning Methods,” pp. 1–19, 2025.

M. Y. T. S.Gul, “Comprehensive comparison between artificial intelligence and multiple regression : prediction of Palmerston North ’ s temperature,” no. 2025, 2024.

Y. C.Lin, K.DaWu, W. C.Shih, P. K.Hsu, andJ. P.Hung, “Prediction of surface roughness based on cutting parameters and machining vibration in end milling using regression method and artificial neural network,” Appl. Sci., vol. 10, no. 11, 2020, doi: 10.3390/app10113941.

M.Abdullah andS.Said, “Performance Evaluation of Machine Learning Regression Models for Rainfall Prediction,” Iran. J. Sci. Technol. Trans. Civ. Eng., no. 0123456789, 2024, doi: 10.1007/s40996-024-01691-4.

Q. Qin, X. Wang, S. Dai, Y. Zhong, and S. Wei, “Machine learning-based prediction of mechanical properties for large bearing housing castings,” Materials, vol. 18, no. 17, Art. no. 4036, 2025, doi: 10.3390/ma18174036.

L. Li, W. Sun, L. Y. Gómez-Zamorano, Z. Liu, W. Zhang, and H. Ma, “From research trend to performance prediction: Metaheuristic-driven machine learning optimization for cement pastes containing bio-based phase change materials,” Polymers, vol. 17, no. 18, Art. no. 2541, 2025, doi: 10.3390/polym17182541.

M. Petković, “Modeling and prediction of surface roughness in hybrid manufacturing—milling after FDM using artificial neural networks,” Applied Sciences, vol. 14, no. 14, Art. no. 5980, 2024, doi: 10.3390/app14145980.

P. Bober, K. Zgodavová, M. Čička, M. Mihaliková, and J. Brindza, “Predictive quality analytics of surface roughness in turning operation using polynomial and artificial neural network models,” Processes, vol. 12, no. 1, Art. no. 206, 2024, doi: 10.3390/pr12010206.

A. Kosarac, S. Tabaković, C. Mladjenović, and M. Željković, “Next-gen manufacturing: Machine learning for surface roughness prediction in Ti-6Al-4V biocompatible alloy machining,” Journal of Manufacturing and Materials Processing, vol. 7, no. 6, Art. no. 202, 2023, doi: 10.3390/jmmp7060202.

S. Mane and R. B. Patil, “Predictive modeling of surface roughness and cutting temperature using response surface methodology and artificial neural network in hard turning of AISI 52100 steel with minimal cutting fluid application,” Machines, vol. 13, no. 4, Art. no. 266, 2025, doi: 10.3390/machines13040266.

M. S. El-Asfoury, M. Baraya, E. El Shrief, K. Abdelgawad, M. Sultan, and A. Abass, “AI-based prediction of ultrasonic vibration-assisted milling performance,” Sensors, vol. 24, no. 17, Art. no. 5509, 2024, doi: 10.3390/s24175509.

A. E. Muñoz-Zavala, J. E. Macías-Díaz, D. Alba-Cuéllar, and J. A. Guerrero-Díaz-de-León, “A literature review on some trends in artificial neural networks for modeling and simulation with time series,” Algorithms, vol. 17, no. 2, Art. no. 76, 2024, doi: 10.3390/a17020076.

DOI: http://dx.doi.org/10.30811/jpl.v24i1.8185