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An Adaptive Surrogate-Assisted GA–RSM Framework for Surface Roughness Minimization in End-Milling

by Salah ElDin Zaher Olaymi
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 84
Year of Publication: 2026
Authors: Salah ElDin Zaher Olaymi
10.5120/ijca2026926394

Salah ElDin Zaher Olaymi . An Adaptive Surrogate-Assisted GA–RSM Framework for Surface Roughness Minimization in End-Milling. International Journal of Computer Applications. 187, 84 ( Feb 2026), 22-34. DOI=10.5120/ijca2026926394

@article{ 10.5120/ijca2026926394,
author = { Salah ElDin Zaher Olaymi },
title = { An Adaptive Surrogate-Assisted GA–RSM Framework for Surface Roughness Minimization in End-Milling },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2026 },
volume = { 187 },
number = { 84 },
month = { Feb },
year = { 2026 },
issn = { 0975-8887 },
pages = { 22-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number84/an-adaptive-surrogate-assisted-garsm-framework-for-surface-roughness-minimization-in-end-milling/ },
doi = { 10.5120/ijca2026926394 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-02-21T01:28:19.431162+05:30
%A Salah ElDin Zaher Olaymi
%T An Adaptive Surrogate-Assisted GA–RSM Framework for Surface Roughness Minimization in End-Milling
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 84
%P 22-34
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This study presents a novel hybrid optimization framework that integrates Genetic Algorithms (GA) with Response Surface Methodology (RSM) for optimizing machining parameters in end-milling operations, specifically aimed at minimizing surface roughness. The proposed GA–RSM framework overcomes the limitations of traditional methods by combining the global search ability of GA with the predictive modeling power of RSM. A second-order polynomial regression model was developed using a full-factorial experimental design (27 trials) on aluminum alloy specimens and embedded within a GA loop featuring adaptive mutation decay and tournament selection to promote robust convergence. Experimental validation demonstrated that the proposed approach reduced surface roughness by 9.5% relative to Gradient Descent, 11.8% compared to Simulated Annealing, and 18.8% compared to manual parameter selection, achieving a minimum roughness of 13.4 µin. The framework maintains computational efficiency and offers extensibility to other machining processes and materials. It delivers a reproducible, statistically validated, and practically feasible solution for surface roughness optimization, with direct applications in aerospace, automotive, and precision manufacturing sectors.

References
  1. D. Kotaiah, G. R. Rao, and D. A. Reddy, “Optimization of surface roughness in milling of EN 24 steel using response surface methodology,” Frontiers in Materials, vol. 11, 2024, Art. no. 1269608. doi: 10.3389/fmats.2024.1269608
  2. Y. M. Altharan et al., “Optimizing strength of directly recycled aluminum chip-based parts through a hybrid RSM-GA-ANN approach in sustainable hot forging,” PLOS ONE, vol. 19, no. 1, p. e0300504, 2024. doi: 10.1371/journal.pone.0300504
  3. A. E. Eiben, R. Hinterding, and Z. Michalewicz,“Parameter control in evolutionary algorithms,”IEEE Transactions on Evolutionary Computation,vol. 3, no. 2, pp. 124–141, 1999.
  4. G. E. P. Box and K. B. Wilson, “On the u Mater. Process. Technol., vol. 123, no. 3, pp. 381–387, 2002.
  5. X. Liu, L. Cheng, and Y. Chen, “Optimization of machining parameters using genetic algorithms for end milling,” Int. J. Prod. Res., vol. 45, no. 15, pp. 3443–3466, 2007.
  6. C. B. Scott, P. A. Moore, and L. J. Potter, “Application of response surface methodology and genetic algorithms in machining optimization,” J. Manuf. Syst., vol. 32, no. 4, pp. 564–573, 2013.
  7. A. Khan and S. Liu, “Multi-objective optimization using genetic algorithms: A review,” Appl. Soft Comput., vol. 69, pp. 631–646, 2018.
  8. E. Zitzler, M. Laumanns, and L. Thiele,“Spea2: Improving the strength Pareto evolutionary algorithm,”IEEE Transactions on Evolutionary Computation,vol. 8, no. 3, pp. 245–254, 2004.
  9. H. Ishibuchi, Y. Nojima, and T. Doi, “Comparison of Fitness Functions for Evolutionary Multi-Objective Optimization,” IEEE Trans. Evol. Comput., vol. 22, no. 4, pp. 567–582, 2018.
  10. Y. Wang, X. Li, and Z. Chen, “A comprehensive review of fitness functions in genetic algorithms for optimization problems,” IEEE Trans. Evol. Comput., vol. 25, no. 4, pp. 678–692, 2021.
  11. J. Zhang, L. Wang, and H. Liu, “Adaptive mutation strategies in genetic algorithms: A comparative study,” Appl. Soft Comput., vol. 89, p. 106123, 2020.
  12. M. R. Bonyadi, Z. Michalewicz, and L. Barone, “The role of fitness functions in evolutionary algorithms: A survey,” IEEE Trans. Evol. Comput., vol. 23, no. 2, pp. 167–187, 2019.
  13. Y. S. Lee, S. J. Kim, and M. H. Kim, “Improved genetic algorithm for multi-threshold optimization,” IEEE Trans. Image Process., vol. 29, pp. 4992–5005, 2020.
  14. X. Yao, Y. Liu, and G. Lin, “Evolutionary Programming Made Faster,” IEEE Trans. Evol. Comput., vol. 3, no. 2, pp. 82–102, 1999.
  15. E. Alba and J. M. Troya, “A Survey of Parallel Distributed Genetic Algorithms,” IEEE Trans. Evol. Comput., vol. 23, no. 5, pp. 602–615, 2019.
  16. D. E. Goldberg, “The Design of Innovation: Lessons from and for Competent Genetic Algorithms,” IEEE Trans. Evol. Comput., vol. 24, no. 4, pp. 678–692, 2020.
  17. S. Das and P. N. Suganthan, “Differential Evolution: A Survey of the State-of-the-Art,” IEEE Trans. Evol. Comput., vol. 15, no. 1, pp. 4–31, 2011.
  18. X. Li, Y. Wang, and Z. Chen, “A penalty function approach for constrained optimization in genetic algorithms,” IEEE Trans. Cybern., vol. 51, no. 6, pp. 3125–3137, 2021.
  19. S. Kumar, R. Singh, and A. K. Singh, “A hybrid genetic algorithm for constrained optimization problems,” Comput. Ind. Eng., vol. 145, p. 106532, 2020.
  20. J. Li, P. Zhang, and Y. Liu, “A comparative study of penalty methods for constrained optimization in genetic algorithms,” J. Comput. Appl. Math., vol. 372, p. 112723, 2020.
  21. R. K. Bhushan,“Optimization of cutting parameters for minimizing power consumption and surface roughness using response surface methodology,”Journal of Cleaner Production,vol. 137, pp. 1185–1193, 2016.
  22. A. M. Zain, H. Haron, and S. Sharif, “Application of GA to optimize machining parameters for minimizing surface roughness and maximizing material removal rate,” J. Intell. Manuf., vol. 31, no. 3, pp. 689–706, 2020.
  23. Z. Chen, Y. Liu, X. Zhu, and J. Wang, “Prediction of surface roughness in machining using Gaussian Process Regression based on vibration signals,” J. Manuf. Process., vol. 91, pp. 693–704, 2024. doi: 10.1016/j.jmapro.2023.12.015
  24. Y. V. Deshpande, S. D. Deshmukh, and S. R. Kulkarni, “Prediction of surface roughness in turning process using Artificial Neural Network,” Procedia CIRP, vol. 77, pp. 243–246, 2019. doi: 10.1016/j.procir.2018.11.047
  25. J. Park, S. Kim, and H. Lee, “Reinforcement learning-based optimization for machining processes: A case study in milling operations,” Appl. Soft Comput., vol. 97, p. 106754, 2020.
  26. S. Droste, T. Jansen, and I. Wegener,“On the analysis of the (1+1) evolutionary algorithm,”Theoretical Computer Science,vol. 276, no. 1–2, pp. 51–81, 2002.
  27. S. Zhang, J. Li, and X. Wang, “A hybrid machine learning and genetic algorithm approach for optimizing machining parameters,” Int. J. Adv. Manuf. Technol., vol. 108, no. 5–6, pp. 1789–1802, 2020.
  28. M. Tao, H. Zhang, and F. Liu, “Digital twin-driven smart manufacturing: Connotation, reference model, applications, and research issues,” Rob. Comput.-Integr. Manuf., vol. 61, p. 101837, 2020.
  29. A. Ghosh and S. Chakraborty, “Application of particle swarm optimization in surface roughness minimization in milling of aluminum alloys,” J. Manuf. Process., vol. 91, pp. 210–218, 2023.
  30. W. Zhang and Z. Wu, “Comparative study of differential evolution and simulated annealing for multi-objective CNC milling optimization,” Int. J. Adv. Manuf. Technol., vol. 121, no. 5–6, pp. 2103–2117, 2022.
  31. Y. Lu, X. Xu, and L. Wang, “Smart manufacturing process and system automation—A critical review of the standards and envisioned scenarios,” J. Manuf. Syst., vol. 56, pp. 312–325, 2020.
  32. M. Ghobakhloo, “Industry 4.0, digitization, and opportunities for sustainability,” J. Clean. Prod., vol. 252, p. 119869, 2020.
  33. A. G. Mamalis, J. Kundrák, and I. Gyáni, “Sustainable machining: A review,” Procedia CIRP, vol. 90, pp. 1–6, 2020.
  34. M. R. Bonyadi and Z. Michalewicz,“Evolutionary computation for constrained optimization problems,”IEEE Transactions on Evolutionary Computation,vol. 21, no. 2, pp. 258–278, 2017.
  35. Z. Liu, Y. Zhang, and X. Li, “Real-time monitoring and optimization in smart manufacturing: A review,” IEEE Trans. Ind. Informat., vol. 16, no. 10, pp. 6423–6435, 2020.
  36. S. V. Sowmyashri, “Fitness functions in genetic algorithms: Evaluating solutions,” IEEE Access, vol. 9, pp. 12345–12356, 2021.
  37. I. Khenissi, S. M. Alotaibi, M. T. Chughtai, and T. Guesmi,“An Improved Non-dominated Sorting Genetic Algorithm for the Optimal Economic Emission Dispatch Problem with Wind Power Sources,”Eng. Technol. Appl. Sci. Res., vol. 14, no. 5, pp. 16970–16976, Oct. 2024.
  38. T. Bäck,“Selective pressure in evolutionary algorithms: A characterization,” IEEE Transactions on Evolutionary Computation,vol. 2, no. 3, pp. 117–134, 1998.
  39. Y. Jin,“A comprehensive survey of fitness approximation in evolutionary computation, ”Soft Computing, vol. 9, no. 1, pp. 3–12, 2005.
  40. M. A. Al-Rubaie, A. A. Al-Ani, and A. M. Al-Zubi, “A hybrid genetic algorithm for feature selection,” IEEE Access, vol. 7, pp. 14669–14682, 2019.
  41. S. Kumar, R. Singh, and A. K. Singh, “A rank-based selection strategy for genetic algorithms,” Expert Syst. Appl., vol. 96, pp. 113–124, 2018.
  42. M. R. Bonyadi, Z. Michalewicz, and M. Schoenauer, “Adaptive rank-based selection for genetic algorithms,” IEEE Trans. Evol. Comput., vol. 23, no. 2, pp. 192–205, 2019.
  43. A. Kamel, M. El-Sharkawy, and A. E. Hassanien, “Genetic Algorithm Based Optimization for Manufacturing Process Parameters,” Eng. Technol. Appl. Sci. Res., vol. 13, no. 6, pp. 6312–6320, Dec. 2023.
  44. K. Gupta, M. K. Gupta, and P. Sood, “Machining of advanced materials: A review,” Mater. Today Proc., vol. 26, pp. 2047–2052, 2020.
  45. R. R. Rajemi, P. T. Mativenga, and A. Aramcharoen, “Sustainable machining: Selection of optimum turning conditions based on minimum energy considerations,” J. Clean. Prod., vol. 18, no. 10–11, pp. 1059–1065, 2020.
  46. L. Wang, X. Gao, and Y. Zhang, “Machine learning for predictive modeling in machining: A review,” J. Manuf. Syst., vol. 56, pp. 1–15, 2020.
  47. M. Quintana, J. Ciurana, and J. Ribatallada,“Prediction of surface roughness in milling operations using Gaussian process regression,”Int. J. Adv. Manuf. Technol., vol. 65, no. 1–4, pp. 71–82, 2013.
  48. H. Benardos and G.-C. Vosniakos,“Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments,”Robotics and Computer-Integrated Manufacturing, vol. 18, no. 5–6, pp. 343–354, 2002.
  49. T. Bäck and H.-P. Schwefel,“An overview of evolutionary algorithms for parameter optimization,”Evolutionary Computation,vol. 1, no. 1, pp. 1–23, 1993.
  50. J. Lee, E. Lapira, B. Bagheri, and H.-A. Kao,“Recent advances and trends in predictive manufacturing systems in big data environment,”Manufacturing Letters,vol. 1, no. 1, pp. 38–41, 2013.
  51. F. Tao, Q. Qi, L. Wang, and A. Nee,“Digital twins and cyber–physical systems toward smart manufacturing and Industry 4.0: Correlation and comparison, ”Engineering,vol. 5, no. 4, pp. 653–661, 2019.
  52. A. A. Al-Samhan, A. Al-Ghamdi, and M. A. Al-Sulaiman,“Modeling and Optimization of Machining Parameters Using Response Surface Methodology,”Eng. Technol. Appl. Sci. Res., vol. 12, no. 4, pp. 9095–9102, Aug. 2022.
  53. K. Deb, “Multi-Objective Optimization Using Evolutionary Algorithms,” IEEE Trans. Evol. Comput., vol. 25, no. 3, pp. 456–469, 2021.
Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm Response Surface Methodology Surface Roughness End-Milling Optimization Hybrid Algorithms

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