Development of a machining allowance model for rectangular pocket  milling operations

Authors

  • Tran Thanh Tung Faculty of Engineering Mechanics and Automation, VNU University of Engineering and Technology, Hanoi, Vietnam https://orcid.org/0000-0002-3827-9006
    Competing Interests

    It does not express a conflict of interest.

  • Nguyen Thi Anh Faculty of Mechanical Engineering, Thuyloi University, Hanoi, Vietnam https://orcid.org/0009-0000-8903-6939
    Competing Interests

    It does not express a conflict of interest.

  • Nguyen Xuan Quynh School of Mechanical Engineering, Ha Noi University of Science and Technology, Hanoi, Vietnam https://orcid.org/0000-0002-6815-7579
    Competing Interests

    It does not express a conflict of interest.

  • Tran Vu Minh School of Mechanical Engineering, Ha Noi University of Science and Technology, Hanoi, Vietnam https://orcid.org/0000-0003-0621-9217
    Competing Interests

    It does not express a conflict of interest.

DOI:

https://doi.org/10.37636/recit.v9n3e498

Keywords:

Machining allowance, Pocket milling, Multi-objective optimization, Energy consumption, Cutting force

Abstract

Machining allowance plays a critical role in determining productivity, energy consumption, cutting force, and dimensional accuracy in pocket milling operations, yet it is commonly selected empirically in industrial practice. This study proposes an analytical, optimization-based framework for determining an efficient machining allowance for rectangular pocket milling. Mathematical models are developed to quantify total energy consumption, machining time, and peripheral cutting force as functions of cutting parameters and allowance distribution between rough and finish milling stages. A multi-objective optimization problem is formulated and solved using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to simultaneously minimize energy consumption, machining time, and cutting force. Experimental validation is conducted on polymer workpieces and compared with  CAM-recommended parameters. The results show that the balanced Pareto-optimal solution reduces machining time from 5 min 58.52 s to 2 min 26.47 s, corresponding to an approximate 59% reduction, and decreases the peripheral cutting force from 698.18 N to 382.73 N, corresponding to an approximate 45% reduction, while achieving the target pocket depth of approximately 22 mm and eliminating the ~2 mm over-machining observed under CAM-default settings. A minimum-energy solution further reduces energy consumption from 0.44 kWh to 0.267 kWh, but at the cost of a significantly increased cutting force (1203.45 N).  These results indicate that treating machining allowance as an optimization variable enables systematic trade-offs among productivity, energy consumption, cutting force, and dimensional accuracy, offering a practical alternative to conventional CAM-based parameter selection.

Downloads

Download data is not yet available.

References

[1] A. De Bartolomeis, D. A. Axinte, S. J. Pickering, and L. Zhou, "Future research directions in the machining of Inconel 718," Journal of Materials Processing Technology, vol. 297, p. 117260, 2021, doi: 10.1016/j.jmatprotec.2021.117260.

[2] N. T. Anh, N. X. Quynh, and T. T. Tung, "A Milling Technique for the Fabrication of Mechanical Parts with Thin-Walled Ribs," Eng. Technol. Sci. Res., vol. 15, no. 4, pp. 24815–24819, Aug. 2025, doi: 10.48084/etasr.11136.

[3] M. Soori, M. Asmael, and D. Solyalı, "Sustainable CNC machining operations, a review," Sustainable Operations and Computers, vol. 5, pp. 73–87, 2024, doi: 10.1016/j.susoc.2024.01.001.

[4] M. Hourmand, A. A. D. Sarhan, and M. Sayuti, "A Comprehensive Review on Machining of Titanium Alloys," Arab J Sci Eng, vol. 46, pp. 7087–7123, 2021, doi: 10.1007/s13369-021-05420-1.

[5] N. T. Anh and T. T. Tung, "Development and validation of finite element model of milling thin-walled part," Applications in Engineering Science, vol. 25, p. 100285, 2026, doi: 10.1016/j.apples.2025.100285.

[6] G. Wang, W. -L. Li, C. Jiang, and H. Ding, "Machining Allowance Calculation for Robotic Edge Milling an Aircraft Skin Considering the Deformation of Assembly Process," IEEE/ASME Transactions on Mechatronics, vol. 27, no. 5, pp. 3350–3361, Oct. 2022, doi: 10.1109/TMECH.2021.3131309.

[7] B. Wu, Y. Zhang, G. Liu, and Z. Wang, "Feedrate optimization method based on machining allowance optimization and constant power constraint," Int J Adv Manuf Technol, vol. 115, pp. 3345–3360, 2021, doi: 10.1007/s00170-021-07381-z.

[8] M. Belhadj, R. Kromer, S. Werda, S. Leleu, and A. El Mansori, "Effect of cold metal transfer-based wire arc additive manufacturing parameters on geometry and machining allowance," Int J Adv Manuf Technol, vol. 131, pp. 739–748, 2024, doi: 10.1007/s00170-023-11835-x.

[9] B. Burhanudin, M. Margono, E. Suryono, N. T. Atmoko, and Z. Zainuddin, "The Effect of Finishing Allowance and Milling Methode on Surface Roughness in the Finishing Process of Al5052 and Al7075," KEM, vol. 935, pp. 63–71, Nov. 2022, doi: 10.4028/p-yt2516.

[10] I. Daniyan, K. Mpofu, and F. Fameso, "Computer-aided modelling and experimental evaluation of the pocket milling operation for alloy tool steel (AISI D3)," Int J Adv Manuf Technol, vol. 122, pp. 4453–4466, 2022, doi: 10.1007/s00170-022-09979-3.

[11] R. Mellacheruvu and M. Venkateswara Rao, "Application of artificial neural networks and genetic algorithm for optimizing process parameters in pocket milling of AA7075," Journal of Scientific & Industrial Research, vol. 81, no. 9, pp. 911–921, 2022, doi: 10.56042/jsir.v81i09.55874.

[12] Z. Chen, C. H. E. N., X. C. H. E. N., and Y. L. I. U., "Framework and development of data-driven physics based model with application in dimensional accuracy prediction in pocket milling," Chinese Journal of Aeronautics, vol. 34, no. 6, pp. 162–177, 2021, doi: 10.1016/j.cja.2020.09.011.

[13] Z. Duan, C. Li, W. Ding, Y. Ning, and M. Yang, "Milling Force Model for Aviation Aluminum Alloy: Academic Insight and Perspective Analysis," Chin. J. Mech. Eng., vol. 34, no. 1, p. 18, 2021, doi: 10.1186/s10033-021-00536-9.

[14] N. T. Anh and T. T. Tung, "Cutting force prediction in end milling processes: Analytical models and applications," Applications in Engineering Science, vol. 23, p. 100250, 2025, doi: 10.1016/j.apples.2025.100250.

[15] B. Yan, B. Wang, L. Zhu, C. Zhang, P. Wang, and G. Zhao, "Towards high milling accuracy of turbine blades: A review," Mechanical Systems and Signal Processing, vol. 170, p. 108727, 2022, doi: 10.1016/j.ymssp.2021.108727.

[16] J. H. Navarro-Devia, Y. Chen, and D. V. Dao, "Chatter detection in milling processes—a review on signal processing and condition classification," Int J Adv Manuf Technol, vol. 125, pp. 3943–3980, 2023, doi: 10.1007/s00170-023-10969-2.

[17] M. S. El-Eskandarany, A. Al-Hazza, L. A. Al-Hajji, N. Ali, A. A. Al-Duweesh, M. Banyan, and F. Al-Ajmi, "Mechanical Milling: A Superior Nanotechnological Tool for Fabrication of Nanocrystalline and Nanocomposite Materials," Nanomaterials, vol. 11, no. 10, p. 2484, 2021, doi: 10.3390/nano11102484.

[18] Rahul A. Mali, T. V. K. Gupta, and J. Ramkumar, "A comprehensive review of free-form surface milling—Advances over a decade," Journal of Manufacturing Processes, vol. 62, pp. 132–167, 2021, doi: 10.1016/j.jmapro.2020.12.014.

[19] Z. H. A. O. Guolong, "Cutting force model and damage formation mechanism in milling of 70wt% Si/Al composite," Chinese Journal of Aeronautics, vol. 36, no. 7, pp. 114–128, 2023, doi: 10.1016/j.cja.2022.07.018.

[20] José David Pérez-Ruiz, "On the relationship between cutting forces and anisotropy features in the milling of LPBF Inconel 718 for near net shape parts," International Journal of Machine Tools and Manufacture, vol. 170, p. 103801, 2021, doi: 10.1016/j.ijmachtools.2021.103801.

[21] Markus Brillinger, "Energy prediction for CNC machining with machine learning," CIRP Journal of Manufacturing Science and Technology, vol. 35, pp. 715–723, 2021, doi: 10.1016/j.cirpj.2021.07.014.

[22] Yuying Yang, "Mechanical performance of 316 L stainless steel by hybrid directed energy deposition and thermal milling process," Journal of Materials Processing Technology, vol. 291, p. 117023, 2021, doi: 10.1016/j.jmatprotec.2020.117023.

[23] Vincent Aizebeoje Balogun and Paul Tarisai Mativenga, "Modelling of direct energy requirements in mechanical machining processes," Journal of Cleaner Production, vol. 41, pp. 179–186, 2013, doi: 10.1016/j.jclepro.2012.10.015.

[24] K. He, H. Hong, R. Tang, and J. Wei, "Analysis of Multi-Objective Optimization of Machining Allowance Distribution and Parameters for Energy Saving Strategy," Sustainability, vol. 12, no. 16, p. 638, 2020, doi: 10.3390/su12060638.

[25] A. Pajaziti, O. Tafilaj, A. Gjelaj, and B. Berisha, "Optimization of Toolpath Planning and CNC Machine Performance in Time-Efficient Machining," Machines, vol. 13, no. 1, p. 65, 2025, doi: 10.3390/machines13010065.

[26] R. Saravanan and V. Janakiraman, "Study on Reduction of Machining Time in CNC Turning Centre by Genetic Algorithm," in International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007), Sivakasi, India, 2007, pp. 481–486, doi: 10.1109/ICCIMA.2007.92.

[27] K. He, R. Tang, Z. Zhang, and W. Sun, "Energy Consumption Prediction System of Mechanical Processes Based on Empirical Models and Computer-Aided Manufacturing," ASME J. Comput. Inf. Sci. Eng., vol. 16, no. 4, p. 041008, Dec. 2016, doi: 10.1115/1.4033921.

[28] Wu Deng, "An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems," Information Sciences, vol. 585, pp. 441–453, 2022, doi: 10.1016/j.ins.2021.11.052.

[29] W. Zheng and B. Doerr, "Approximation Guarantees for the Nondominated Sorting Genetic Algorithm II (NSGA-II)," IEEE Transactions on Evolutionary Computation, vol. 29, no. 4, pp. 891–905, Aug. 2025, doi: 10.1109/TEVC.2024.3402996.

[30] M. Babor, L. Pedersen, U. Kidmose, O. Paquet-Durand, and B. Hitzmann, "Application of Non-Dominated Sorting Genetic Algorithm (NSGA-II) to Increase the Efficiency of Bakery Production: A Case Study," Processes, vol. 10, no. 8, p. 1623, 2022, doi: 10.3390/pr10081623.

[31] M. Moshref, R. Al-Sayyed, and S. Al-Sharaeh, "An Enhanced Multi-Objective Non-Dominated Sorting Genetic Routing Algorithm for Improving the QoS in Wireless Sensor Networks," IEEE Access, vol. 9, pp. 149176–149195, 2021, doi: 10.1109/ACCESS.2021.3122526.

[32] Y. -C. Chang, K. -H. Chang, and C. -P. Zheng, "Application of a Non-Dominated Sorting Genetic Algorithm to Solve a Bi-Objective Scheduling Problem Regarding Printed Circuit Boards," Mathematics, vol. 10, no. 12, p. 2305, 2022, doi: 10.3390/math10122305.

Experimental setup for pocket milling on a polymer workpiece using a CNC milling machine.

Published

2026-07-08

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

How to Cite

Tran Thanh, T., Thi Anh, N., Xuan Quynh, N., & Vu Minh, T. (2026). Development of a machining allowance model for rectangular pocket  milling operations. Revista De Ciencias Tecnológicas, 9(3), 1-14. https://doi.org/10.37636/recit.v9n3e498

Similar Articles

11-20 of 36

You may also start an advanced similarity search for this article.