A Review on Surface Error Compensation in Thin-Walled Component Machining

Volume: 10 | Issue: 02 | Year 2024 | Subscription
International Journal of Structural Mechanics and Finite Elements
Received Date: 11/12/2024
Acceptance Date: 11/14/2024
Published On: 2024-11-22
First Page: 1
Last Page: 11

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By: Rajeeve Mohan, Santhu Varghese Thomas, Rahul Krishnan, Johan George Cherian, Navish Kumar, Amal R, and Manikandan Hareendran

Associate Professor, Department of Mechanical Engineering, Mangalam College of Engineering (APJAKTU), Kottayam, Kerala, India
Assistant Professor, Department of Mechanical Engineering, Mangalam College of Engineering (APJAKTU), Kottayam, Kerala, India
Assistant Professor, Department of Mechanical Engineering, Mangalam College of Engineering (APJAKTU), Kottayam, Kerala, India
Associate Professor, Department of Mechanical Engineering, Mangalam College of Engineering (APJAKTU), Kottayam, Kerala, India
Associate Professor, Department of Mechanical Engineering, Mangalam College of Engineering (APJAKTU), Kottayam, Kerala, India
Associate Professor, Department of Mechanical Engineering, Mangalam College of Engineering (APJAKTU), Kottayam, Kerala, India
Professor, Department of Mechanical Engineering, Mangalam College of Engineering (APJAKTU), Kottayam, Kerala, India

Abstract
*Author for CorrespondenceManikandan HareendranE-mail: [email protected] 1,4,5,6Associate Professor, Department of Mechanical Engineering, Mangalam College of Engineering (APJAKTU), Kottayam, Kerala, India2,3Assistant Professor, Department of Mechanical Engineering, Mangalam College of Engineering (APJAKTU), Kottayam, Kerala, India7Professor, Department of Mechanical Engineering, Mangalam College of Engineering (APJAKTU), Kottayam, Kerala, India Received Date: November 12, 2024Accepted Date: November 14, 2024Published Date: November 22, 2024 Citation: Rajeeve K. Mohana, Santhu Varghese Thomas, Rahul Krishnan, Johan George Cherian, Navish Kumar, Amal R., Manikandan Hareendrana. A Review on Surface Error Compensation in Thin-Walled Component Machining. International Journal of Structural Mechanics and Finite Elements. 2024; 10(2): 1–11p.

Abstract

This review examines advancements in surface error compensation methods for machining thin-walled components, drawing from research published between 2000 and 2025 in high-impact journals. Thin-walled components, commonly used in aerospace, automotive, and electronics, present unique challenges in machining due to their susceptibility to deflection, chatter, and thermal deformation, often resulting in significant surface errors. The objective of this review is to assess how compensation techniques have evolved to address these challenges, focusing on methods that improve machining accuracy, surface quality, and efficiency. The review categorizes compensation strategies into three primary types: model-based, in-process, and hybrid approaches. Model-based methods, such as finite element analysis (FEA) and machining dynamics models, predict potential errors and adjust cutting paths or parameters pre-emptively. In-process methods leverage real-time data from sensors to dynamically modify tool paths, improving adaptability. Hybrid approaches combine predictive models with real-time adaptive control, often integrating machine learning to enhance predictive accuracy and responsiveness. Findings indicate a shift toward hybrid and AI-driven solutions in recent years, with machine learning models increasingly supporting dynamic error compensation. These advancements are making compensation systems faster, more adaptable, and effective, enhancing precision in manufacturing processes. Future trends suggest further integration of smart machining technologies, particularly with Industry 4.0 frameworks, which promise even greater adaptability and cost-efficiency for thin-walled component machining.

Keywords[CD1] : Efficiency, Surface error compensation, Deflection, Thermal deformation, Surface quality

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How to cite this article: Rajeeve Mohan, Santhu Varghese Thomas, Rahul Krishnan, Johan George Cherian, Navish Kumar, Amal R, and Manikandan Hareendran, A Review on Surface Error Compensation in Thin-Walled Component Machining. International Journal of Structural Mechanics and Finite Elements. 2024; 10(02): 1-11p.

How to cite this URL: Rajeeve Mohan, Santhu Varghese Thomas, Rahul Krishnan, Johan George Cherian, Navish Kumar, Amal R, and Manikandan Hareendran, A Review on Surface Error Compensation in Thin-Walled Component Machining. International Journal of Structural Mechanics and Finite Elements. 2024; 10(02): 1-11p. Available from:https://journalspub.com/publication/uncategorized/article=16064

Refrences:

  1. Tsai JS, Lee MY, Liang MW. Prediction and compensation of surface errors in thin-walled part machining. Int J Adv Manuf Technol. 2003;21(9):678–84.
  2. Izamshah RAR, Mo J, Ding SL. Finite element analysis of machining thin-wall parts. Key Engineering Materials. 2011 Mar 31;458:283–8.
  3. Liu Z, Gao X, Chen J. Modeling and optimization of thin-walled aerospace components using FEA and material heterogeneity. Aerospace Science and Technology. 2018;78:301–10.
  4. Tlusty J, Polacek M. The stability of machine tools against self-excited vibrations in machining. Int Res Prod Eng ASME. 1963; p. 465–74.
  5. Gao W, Zhao H, Li R. Enhanced dynamic models for multi-axis machining of thin-walled aerospace parts. Precision Engineering. 2021;70:235–45.
  6. Zhang Y, Wang F, Li Z. AI-enhanced FEA for real-time deformation prediction in thin-walled machining. J Manuf Process. 2023;79:412–21.
  7. Smith GD, Turner T, Patel A. Real-time adaptive control in thin-walled component machining. J Manuf Sci Eng. 2002;124:377–86.
  8. Li Y, Huang L, Liu P. Adaptive control system with predictive feedback for high-speed machining. CIRP Ann. 2019;68(1):125–8.
  9. Kim T, Park S. Tool path modification using surface roughness data for improved finish in titanium alloys. J Mater Process Technol. 2017;252:30–9.
  10. Yu D, Lin X, Huang Z. Real-time path adjustment algorithms for thin-walled part machining. Int J Mach Tools Manuf. 2022;128:73–80.
  11. Zhao M, Chen T, Lee Y. Multi-sensor integration for improved accuracy in thin-walled machining. Sens Actuators A Phys. 2020;306:111979.
  12. Nguyen Q, Zhang L, Wang X. Neural network-based sensor fusion for surface error prediction in thin-walled components. Int J Adv Manuf Technol. 2023;123:654–62.
  13. Huang Y, Sun J, Li M. Hybrid FEA and adaptive control systems for precision machining of thin-walled components. J Manuf Syst. 2021;58:33–41.
  14. Wang J, Chen R, Liu T. Machine learning-based adaptive control in thin-walled component machining. J Intell Manuf. 2022;33(5):1477–86.
  15. Liu Z, Zhao W, Lin J. Deep learning in real-time error prediction for thin-walled machining. Mach Learn Manuf. 2022;4:289–97.
  16. Singh A, Patel K. Autonomous error compensation using IIoT-enabled adaptive control systems. J Manuf Ind Eng. 2022;45(3):176–84.
  17. Zhang P, Chen H, Gao M. Digital twin-enabled tool deflection management in aerospace component machining. J Mech Eng Sci. 2023;237(2):128–35.
  18. Gupta R, Lee H, Santos M. Industry 4.0-driven error compensation for precision aerospace parts. J Smart Manuf Syst. 2023;7(4):224–30.

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