Authors:
Wael H. A. Shaheen,Marwan A. Salman,Sadoon R. Daham,Kareem N. Salloomi,Wisam T. Abbood,Yahya M. Hamad,DOI NO:
https://doi.org/10.26782/jmcms.2026.04.00004Keywords:
Residual Stress,High-Speed End Milling,Thermo-Mechanical Finite Element,X-Ray Diffraction,Design of Experiments/Response Surface Methodology,Multi-Objective Optimization,Abstract
This study presents a transient thermo-mechanical finite element framework for high-speed end milling of AISI 4340 steel. The model couples moving heat sources, rate- and temperature-dependent plasticity, and adaptive mesh refinement (AMR) triggered by temperature gradient, plastic strain rate, and contact pressure. It is integrated with a design of experiments/response surface methodology using cutting speed (VC), feed per tooth (fZ), radial depth of cut/width of cut (ae), axial depth of cut (ap), and coolant mode. Responses include peak interface temperature per tooth (Tpeak), predicted surface residual stress (?_xx^"surf" ), and depth of compressive residual stress layer (dcomp). Experiments provide X-ray diffraction-based surface/depth profiles and arithmetic mean surface roughness (Ra). AMR is applied in this study to minimize the cut compute cost by 41-52% and error by 35-45%. Across 12 validation cuts, root mean square errors were 24 °C of Tpeak, 33 MPa of ?_xx^"surf" , 0.07 µm of Ra, and 22 MPa of dcomp. The response surface methodology and analysis of variance identified VC as the main driver of thermal load, while fZ, ae, and ap controlled the sign and depth of the residual field; coolant modified heat partition. Multi-objective desirability optimization with a material removal rate constraint yielded a balanced minimum quantity lubrication. Overall, exit-edge cooling and subsurface plasticity jointly set residual sign and magnitude; AMR is essential to resolve these gradients efficiently. The framework offers a reproducible route for residual stress-aware process planning in fatigue-critical AISI 4340 components while preserving throughput and is readily transferable to allied high-strength steels.Refference:
I. Abas M. et al. (2020). Experimental investigation and statistical evaluation of optimized cutting process parameters and cutting conditions to minimize cutting forces and shape deviations in Al6026-T9. Materials, 13(19), 4327. 10.3390/ma13194327
II. Abdelaal, A. F., Chakrobarty, A., Sakib, M. N., Arka, A. M., & Sabuz, E. H. (2025). Porosity, residual stress, wear properties and impact toughness of additively manufactured low-alloy steel: A review. Next Materials, 9, 101288. 10.1016/j.nxmate.2025.101288
III. Akbar, F., Mativenga, P. T., & Sheikh, M. A. (2010). An experimental and coupled thermo-mechanical finite element study of heat partition effects in machining. The International Journal of Advanced Manufacturing Technology, 46(5), 491-507. DOI: 10.1007/s00170-009-2117-5
IV. Anand K. S., Inigo F. I., Kalim D., and Rajkumar V., Additively Manufactured Smart Materials and Structures Design, Processing, and Applications, Elsevier, 2025.
V. Andrew P. K. and Robert E., Statistics for Biomedical Engineers and Scientists: How to Visualize and Analyze Data, Academic Press, 2019.
VI. Bag, R., Panda, A., Sahoo, A. K., & Kumar, R. (2019). A perspective review on surface integrity and its machining behavior of AISI 4340 hardened alloy steel. Materials Today: Proceedings, 18, 3532-3538.
VII. Binali, R., Patange, A. D., Kunto?lu, M., Mikolajczyk, T., & Salur, E. (2022). Energy saving by parametric optimization and advanced lubri-cooling techniques in the machining of composites and superalloys: A systematic review. Energies, 15(21), 8313.
VIII. Bonito, A., Canuto, C., Nochetto, R. H., and Veeser, A. (2024). Adaptive finite element methods: A survey of theory and applications in mechanics. Acta Numerica, 33, 165–290. DOI: 10.1017/S0962492924000011
IX. Cybellium, Heat Transfer Exam Study Essentials: A Comprehensive Guide to Heat Transfer Concepts, Cybellium Ltd, 2024.
X. Davel, C., Bassiri-Gharb, N., & Correa-Baena, J. P. (2025). Machine learning in X-ray diffraction for materials discovery and characterization. Matter, 8(9). 10.1016/j.matt.2025.102272
XI. Deepanraj, B., Senthilkumar, N., Hariharan, G., Tamizharasan, T., & Tefera Bezabih, T. (2022). Numerical modelling, simulation, and analysis of the end?milling process using DEFORM?3D with experimental validation. Advances in Materials Science and Engineering, 2022, 5692298. 10.1155/2022/5692298
XII. Imad M., Hosseini S., Kishawy H., & Yussefian N. (2020). 3D finite element simulation of cutting forces in milling hardened steels. Progress in Canadian Mechanical Engineering, 5, 103–110. https://librarydocs.vre3.upei.ca/islandora/object/csme2020:103
XIII. Kaimkuriya, A., Sethuraman, B., & Gupta, M. (2024). Effect of physical parameters on fatigue life of materials and alloys: A critical review. Technologies, 12(7), 100. 10.3390/technologies12070100
XIV. Khattab, A., & Felh?, C. (2024). Progress and challenges in plunge milling: a review of current practices and future directions. Cutting & Tools in Technological System, 101, 51-65. 10.20998/2078-7405.2024.101.05
XV. Lallit, A., Ken K., and Sanjay G., Introduction to Mechanics of Solid Materials, Oxford University Press, 2023.
XVI. Liu, D., Luo, M., Pelayo, G. U., Trejo, D. O., & Zhang, D. (2021). Position-oriented process monitoring in milling of thin-walled parts. Journal of Manufacturing Systems, 60, 360-372. 10.1016/j.jmsy.2021.06.010
XVII. Mirzaei A. H., Haghi P., & Shokrieh M. M. (2024). Prediction of fatigue life of laminated composites by integrating artificial neural network model and non-dominated sorting genetic algorithm. International Journal of Fatigue, 188, 108528. 10.1016/j.ijfatigue.2024.108528
XVIII. Muaz, M. and Khan, S. H. (2021). Failure mechanics analysis of AISI 4340 steel using finite element modeling of the milling process. The Journal of Strain Analysis for Engineering Design, 57(7), 582-595. 10.1177/03093247211058038
XIX. Ren, F. et al. (2025). Metallene: Ångström?scale 2D metals. Advanced Materials, e12683. 10.1002/adma.202512683
XX. Robert L. K., Interaction Effects in Linear and Generalized Linear Models Examples and Applications Using Stata, SAGE Publications, 2018.
XXI. Sharma, M., Alkhazaleh, H. A., Askar, S., Haroon, N. H., Almufti, S. M., & Al Nasar, M. R. (2024). FEM-supported machine learning for residual stress and cutting force analysis in micro end milling of aluminum alloys. International Journal of Mechanics and Materials in Design, 20(5), 1077-1098. 10.1007/s10999-024-09713-9
XXII. Shukla, S. (2020). Rapid in-line residual stress analysis from a portable two-dimensional X-ray diffractometer. Measurement, 157, 107672. 10.1016/j.measurement.2020.107672
XXIII. Sun et al. (2022). Material properties and machining characteristics under high strain rate in ultra-precision and ultra-high-speed machining process: a review. The International Journal of Advanced Manufacturing Technology, 120(11), 7011-7042. 10.1007/s00170-022-09111-5
XXIV. Umbrello, D., Saoubi, R. M., and Outeiro, J. C. M. (2007). The influence of Johnson-Cook material constants on finite element simulation of machining of AISI 316L Steel. International Journal of Machine Tools and Manufacture, 47(3-4), 462-470. 10.1016/j.ijmachtools.2006.06.006
XXV. Vadim S., Mechanics of Materials in Modern Manufacturing Methods and Processing Techniques, Elsevier, 2020.
XXVI. Wang et al. (2015). Large deformation finite element analyses in geotechnical engineering. Computers and Geotechnics, 65, 104-114. 10.1016/j.compgeo.2014.12.005
XXVII. Wimmer, M., Schoop, J., & Zaeh, M. F. (2025). In-situ characterization and modeling of machining-induced residual stresses in peripheral milling of Ti–6Al–4V with rounded cutting edges. Production Engineering, 19(3), 511-524. 10.1007/s11740-024-01323-w
XXVIII. Winiarski, B., Benedetti, M., Fontanari, V., Allahkarami, M., Hanan, J., & Withers, P. J. (2016). High spatial resolution evaluation of residual stresses in shot peened specimens containing sharp and blunt notches by micro-hole drilling, micro-slot cutting and micro-X-ray diffraction methods. Experimental Mechanics, 56(8), 1449-1463. 10.1007/s11340-016-0182-x
XXIX. Zainul H., Machining Processes and Machines Fundamentals, Analysis, and Calculations, CRC Press, 2020.
XXX. Zhou, R. (2024). Modeling and simulation of residual stress in metal cutting process: A review. Advances in Mechanical Engineering, 16(12). 10.1177/16878132241307714

