Status of art and prospects in cutting force modeling and control optimization strategies for carbon fiber reinforced polymer (CFRP)

Carbon Fiber Reinforced Polymer (CFRP) is widely used in aerospace, automotive, and other high-performance industries due to its superior mechanical properties. However, its heterogeneous and anisotropic structure presents significant machining challenges, including high cutting forces, severe tool wear, and poor surface integrity. This paper investigates cutting force modeling and optimization strategies in CFRP machining, focusing on material removal mechanisms, cutting force models, and advanced techniques for efficient, low-damage processing. Key findings highlight that material removal is primarily governed by brittle fiber fracture, matrix cracking, and fiber-matrix debonding. Cutting force models, from macro to micro scale, offer insights into tool-CFRP interactions, although computational complexity remains a challenge. Optimization strategies such as tool trajectory control, Minimum Quantity Lubrication (MQL), and Ultrasonic Vibration-Assisted Machining (UVAM) effectively reduce cutting forces and improve surface quality. Laser-Assisted Machining (LAM) also aids machinability by enabling localized thermal softening. Future research should focus on multi-scale modeling, real-time force monitoring, and intelligent control strategies to optimize CFRP machining further. The integration of digital twin technology and hybrid machining approaches holds promise for improving efficiency and precision. This paper provides a theoretical foundation and practical guidance for advancing CFRP machining in intelligent manufacturing and precision engineering.

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Publication Details

Journal
Journal of Thermoplastic Composite Materials
Published
2025-05-24
DOI
https://doi.org/10.1177/08927057251344579
Citations
1
Primary Topic
Tribology and Wear Analysis
Type
article
Field-Weighted Citation Impact
0.86

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article

Status of art and prospects in cutting force modeling and control optimization strategies for carbon fiber reinforced polymer (CFRP)

Zhongqun Li, Qiang Shen, Wenjing Wu, Helin Fan et al.
1 citations
Journal of Thermoplastic Composite Materials
Tribology and Wear Analysis
0.86
article

Status of art and prospects in cutting force modeling and control optimization strategies for carbon fiber reinforced polymer (CFRP)

Zhongqun Li, Qiang Shen, Wenjing Wu, Helin Fan, Zhaopeng Zeng
article en
1 citations

Abstract

Carbon Fiber Reinforced Polymer (CFRP) is widely used in aerospace, automotive, and other high-performance industries due to its superior mechanical properties. However, its heterogeneous and anisotropic structure presents significant machining challenges, including high cutting forces, severe tool wear, and poor surface integrity. This paper investigates cutting force modeling and optimization strategies in CFRP machining, focusing on material removal mechanisms, cutting force models, and advanced techniques for efficient, low-damage processing. Key findings highlight that material removal is primarily governed by brittle fiber fracture, matrix cracking, and fiber-matrix debonding. Cutting force models, from macro to micro scale, offer insights into tool-CFRP interactions, although computational complexity remains a challenge. Optimization strategies such as tool trajectory control, Minimum Quantity Lubrication (MQL), and Ultrasonic Vibration-Assisted Machining (UVAM) effectively reduce cutting forces and improve surface quality. Laser-Assisted Machining (LAM) also aids machinability by enabling localized thermal softening. Future research should focus on multi-scale modeling, real-time force monitoring, and intelligent control strategies to optimize CFRP machining further. The integration of digital twin technology and hybrid machining approaches holds promise for improving efficiency and precision. This paper provides a theoretical foundation and practical guidance for advancing CFRP machining in intelligent manufacturing and precision engineering.

Journal of Thermoplastic Composite MaterialsVol. 38(12)
Robotic Technology (United States) (US), Hunan University of Technology (CN), Beihang University (CN)
Natural Science Foundation of Hunan Province
Openalex Percentile: Top 30%
Tribology and Wear Analysis
0.86
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