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<p>Carbon fiber-reinforced polymer materials are widely used as raw materials in the aerospace industry. However, during machining, unavoidable machine-workpiece chatter occurs, which is difficult to monitor directly and leads to instability in machining precision. This work investigates the drilling of Carbon Fiber Polyetheretherketone (CF/PEEK) and employed Chladni Figures to visualize the chatter states, providing a quantitative method for analyzing dynamic chatter behavior. This approach helps reveal the underlying relationship between chatter intensity and machining performance. The characteristics of Chladni figures under various machining conditions were systematically examined through experiments, supplemented by simulation-assisted verification. Evaluation indices (Nodes) were defined to quantify the complexity of Chladni Figures and establish correlations with machining accuracy. Results indicate that superior roundness and surface roughness are achieved when Nodes ≤ 4, as demonstrated through Chladni Figures analysis. Furthermore, optimizing machining parameters alone proved insufficient in effectively controlling chatter frequency. To address this challenge, this paper proposes an innovative real-time monitoring approach for machine-workpiece chatter during CF-PEEK drilling. This methodology is expected to facilitate the transition of composite machining from an experience-dependent process to a data-driven framework, enabling the development of high-efficiency and low-damage composite machining technologies.</p>

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From experience to data: A Chladni Figure-Based framework for CF-PEEK chatter analysis

  • 期刊论文

  • 发表日期：

    2026年2月24日

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  • Lihua He,
  • Ru Zheng,
  • Yizhen Yang,
  • Zhipeng Zhang,
  • Xiaoping Hu

摘要

Carbon fiber-reinforced polymer materials are widely used as raw materials in the aerospace industry. However, during machining, unavoidable machine-workpiece chatter occurs, which is difficult to monitor directly and leads to instability in machining precision. This work investigates the drilling of Carbon Fiber Polyetheretherketone (CF/PEEK) and employed Chladni Figures to visualize the chatter states, providing a quantitative method for analyzing dynamic chatter behavior. This approach helps reveal the underlying relationship between chatter intensity and machining performance. The characteristics of Chladni figures under various machining conditions were systematically examined through experiments, supplemented by simulation-assisted verification. Evaluation indices (Nodes) were defined to quantify the complexity of Chladni Figures and establish correlations with machining accuracy. Results indicate that superior roundness and surface roughness are achieved when Nodes ≤ 4, as demonstrated through Chladni Figures analysis. Furthermore, optimizing machining parameters alone proved insufficient in effectively controlling chatter frequency. To address this challenge, this paper proposes an innovative real-time monitoring approach for machine-workpiece chatter during CF-PEEK drilling. This methodology is expected to facilitate the transition of composite machining from an experience-dependent process to a data-driven framework, enabling the development of high-efficiency and low-damage composite machining technologies.