This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.
Jeevan Raju Boddu,
P Venkata Chalapathi,
G Murali,
- Research Scholar, Dept. of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
- Professor, Dept. of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
- Professor, Dept. of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
Abstract
The machining of polymer composite materials, particularly fibre-reinforced polymer-matrix composites, requires stable spindle-tool performance to avoid delamination, fibre pull-out, matrix cracking, thermal softening, poor surface integrity, and premature tool wear. In line with the scope of the Journal of Polymer & Composites, this study presents an integrated finite element, experimental, and machine learning framework for improving machining stability during end-milling of composite material systems. The spindle-tool assembly is modelled using Timoshenko beam theory to estimate tool-tip dynamic behaviour, while sine sweep modal tests are used to evaluate spindle vibration characteristics. Key design variables, namely tool overhang (TO) and bearing span (BS), are optimized to increase chatter-free machining regions in stability lobe diagrams relevant to composite cutting. Twenty-five experimental trials based on a Taguchi L25 orthogonal array are used to examine the influence of TO and BS on first natural frequency and average stable depth of cut. A Gradient Boosting Regressor model is trained to predict machining stability and identify the most influential design parameters. The predicted stability trends are validated through end-milling experiments, demonstrating that reduced tool overhang and optimized bearing span improve dynamic stiffness and support stable machining of polymer composite components. The proposed approach provides a practical route for selecting machining conditions for polymer-matrix and fibre-reinforced composite materials where vibration control, surface quality, and damage reduction are critical.
Keywords: Polymer composite materials; fibre-reinforced polymer composites; machining stability; chatter vibration; spindle-tool dynamics; finite element analysis; Timoshenko beam theory; stability lobe diagram; Gradient Boosting Regressor; process optimization.
References
1. Machado AR, da Silva LRR, Pimenov DY, de Souza FCR, Kuntoğlu M, de Paiva RL. Comprehensive review of advanced methods for improving the parameters of machining steels. Journal of Manufacturing Processes. 2024;125:111-142.
2. Mohanraj T, Kirubakaran ES, Madheswaran DK, Naren ML, Ibrahim M. Review of advances in tool condition monitoring techniques in the milling process. Measurement Science and Technology. 2024;35(9):092002.
3. Cheng Y, Wang Y, Lin J, Xu S, Zhang P. Research status of the influence of machining processes and surface modification technology on the surface integrity of bearing steel materials. The International Journal of Advanced Manufacturing Technology. 2023;125(7):2897-2923.
4. Rizzo A, Goel S, Grilli ML, Iglesias R, Jaworska L, Lapkovskis V, et al. The critical raw materials in cutting tools for machining applications: A review. Materials. 2020;13(6):1377.
5. Ding M. Research on precision and performance optimization methods for high-end CNC machine tools. Journal of Engineering Mechanics and Machinery. 2024;9(1):40-46.
6. Zhou Y, Jiang Y, Lu C, Huang J, Pei J, Xing T, et al. A review of 5-axis milling techniques for centrifugal impellers: Tool-path generation and deformation control. Journal of Manufacturing Processes. 2024;131:160-186.
7. Seemuang N. Non-destructive evaluation and condition monitoring of tool wear. Doctoral dissertation. University of Sheffield; 2016.
8. Bhuiyan MSH, Choudhury IA. Review of sensor applications in tool condition monitoring in machining. Comprehensive Materials Processing. 2014;13:539-569.
9. Caggiano A. Machining of fibre reinforced plastic composite materials. Materials. 2018;11(3):442.
10. Shaik JH, Srinivas J. Optimal design of spindle-tool system for improving the dynamic stability in end-milling process. Sādhanā. 2020;45:1-18.
11. Li X, Liu X, Yue C, Liang SY, Wang L. Systematic review on tool breakage monitoring techniques in machining operations. International Journal of Machine Tools and Manufacture. 2022;176:103882.
12. Raju CT, Hussain SJ, Yedukondalu G, Galal AM. Development of an effective chatter control system for an end mill spindle tool system. Journal of The Institution of Engineers (India): Series C. 2024;105(5):1065-1081.
13. Miao H, Wang C, Song W, Li C, Zhang X, Xu M. Coupling modeling of thermal-dynamics-milling process for spindle system considering nonlinear characteristics. Nonlinear Dynamics. 2024;112(8):6061-6099.
14. Weng L, Gao W, Zhang D, Huang T. Thermal analytical modeling of machine tool structural components via dual-layer equivalence. International Journal of Heat and Mass Transfer. 2024;221:125083.
15. Xu M, Zhang H, Miao H, Hao J, Li C, Song W, et al. Model-based vibration response analysis and experimental verification of lathe spindle-housing-belt system with rubbing. Mechanical Systems and Signal Processing. 2023;186:109841.
16. Abdelhakim D, Harrou F, Sun Y, Makhfi S, Habak M. Explainable machine learning for enhancing predictive accuracy of cutting forces in hard turning processes. The International Journal of Advanced Manufacturing Technology. 2024;1-23.
17. Jiao R, Nguyen BH, Xue B, Zhang M. A survey on evolutionary multiobjective feature selection in classification: Approaches, applications, and challenges. IEEE Transactions on Evolutionary Computation. 2023.
18. Beik Khormizi MS. Hybrid prediction of optimal milling conditions. Doctoral dissertation. University of British Columbia; 2023.
19. Mayer J, Jochem R. Capability indices for digitized industries: A review and outlook of machine learning applications for predictive process control. Processes. 2024;12(8).
20. Knap A, Dvořáčková Š, Knápek T. Study of the machining process of GFRP materials by milling technology with coated tools. Coatings. 2022;12(9):1354.
21. Suzuki N, Kurata Y, Kato T, Hino R, Shamoto E. Identification of transfer function by inverse analysis of self-excited chatter vibration in milling operations. Precision Engineering. 2012;36:568-575.
22. Gagnol V, PhuLe T, Ray P. Modal identification of spindle-tool unit in high-speed machining. Mechanical Systems and Signal Processing. 2011;25:2388-2398.
23. Kim JD, Zverv I, Lee KB. Model of rotation accuracy of high-speed spindles on ball bearings. Journal of Scientific Research. 2010;2:477-484.
24. Cao H, Li B, He Z. Chatter stability of milling with speed-varying dynamics of spindles. International Journal of Machine Tools and Manufacture. 2012;52:50-58.
25. Zahedi A, Movahhedy MR. Thermo-mechanical modeling of high-speed spindles. Scientia Iranica B. 2012;19(2):282-293.
26. Tandon P, Khan MR. Three-dimensional modeling and finite element simulation of a generic end mill. Computer-Aided Design. 2009;41:106-114.
27. Rantatalo M, Aidanpaa JO, Goransson B, Norman P. Milling machine spindle analysis using FEM and non-contact spindle excitation and response measurement. International Journal of Machine Tools and Manufacture. 2007;47:1034-1045.
28. Sarhan AAD, Matsubara A. Investigation about the characterization of machine tool spindle stiffness for intelligent CNC end milling. Robotics and Computer-Integrated Manufacturing. 2015;34:133-139.
29. Kolar P, Sulitka M, Janota M. Simulation of dynamic properties of a spindle and tool system coupled with a machine tool frame. International Journal of Advanced Manufacturing Technology. 2011;54:11-20.
30. Cao H, Li B, He Z. Finite element model updating of machine-tool spindle systems. Journal of Vibration and Acoustics. 2013;135:024503.
31. Zaghbani I, Songmene V. Estimation of machine-tool dynamic parameters during machining operation through operational modal analysis. International Journal of Machine Tools and Manufacture. 2009;49:947-957.
32. Altin Karataş M, Gökkaya H. A review on machinability of carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP) composite materials. Defence Technology. 2018;14(4):318-326.
33. Ozkan D, Panjan P, Gok MS, Karaoglanli AC. Experimental study on tool wear and delamination in milling CFRPs with TiAlN- and TiN-coated tools. Coatings. 2020;10(7):623.
34. Murthy BRN, Rao US, Naik N, Potti SR, Nambiar S. Influence of machining parameters on delamination in abrasive waterjet machining of jute-fibre-reinforced polymer composites using Taguchi and response surface methodology. Journal of Composites Science. 2023;7(11):475.
35. Palanisamy S, Murugesan TM, Palaniappan M, Santulli C, Ayrilmis N. Use of hemp waste for the development of mycelium-grown matrix biocomposites: a concise bibliographic review. BioResources. 2023;18(4):8771-8780. doi:10.15376/biores.18.4.Palanisamy.
36. Ravichandran G, Ramasamy K, Manickaraj K, Kalidas S, Jayamani M, Mausam K, et al. Effect of Sal wood and Babool sawdust fillers on the mechanical properties of snake grass fiber-reinforced polyester composites. BioResources. 2025;20(4):8674-8694. doi:10.15376/biores.20.4.8674-8694.
37. Liew JJ, Yusof NH, Ang DTC. Enhancing flexibility and durability of PVC with liquid epoxidized natural rubber: innovative UV treatment to mitigate plasticizer migration. Vinyl Addit Technol. doi:10.1002/vnl.22167.
38. Pandiarajan P, Baskaran PG, Palanisamy S, Karuppusamy M, Marimuthu K, Rajan A, et al. Enhancing polyester composites with nano Aristida hystrix fibers: mechanical and microstructural insights. BioResources. 2025;20(4):9257-9281. doi:10.15376/biores.20.4.9257-9281.
39. Karuppusamy M, Kalidas S, Palanisamy S, Nataraj K, Nandagopal RK, Natarajan R, et al. Real-time monitoring in polymer composites: Internet of Things integration for enhanced performance and sustainability—a review. BioResources. 2025;20(3):8093-8118. doi:10.15376/biores.20.3.Karuppusamy.
40. Govindarajan PR, Shanmugavel R, Subramanian K, Palanisamy S, Santulli C, Fragassa C. Effect of stacking sequence on mechanical and water absorption characteristics of jute/banana/basalt fabric aluminium fibre laminates with diamond microexpanded mesh. Int J Polym Sci. 2024;2024:3835788. doi:10.1155/2024/3835788.
Journal of Polymer & Composites
| Volume | 14 | |
| 04 | ||
| Received | 10/08/2026 | |
| Accepted | 25/08/2026 | |
| Published | 01/09/2026 | |
| Publication Time | 22 Days |