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<p>Currently, additive manufacturing of carbon fiber reinforced composites (CFRPs) offers advantages such as high forming flexibility and material utilization, it still suffers from limitations in dimensional accuracy, surface quality, and feature formation. This paper explores a hybrid additive-subtractive manufacturing approach for CFRPs and proposes a collaborative process that integrates feature classification, manufacturing process design and path planning. Firstly, a feature tree of the model is constructed and feature types are classified. Based on the feature tree and the error-driven allowance allocation strategy, rational additive and subtractive processes partitioning is achieved, which improves machining efficiency and resource utilization. Furthermore, a planar deposition path planning method based on undirected graph of scanline is proposed to avoid additive defects affecting subtractive machining quality. The simulation and experimental validation show that the average dimensional deviations are 0.032&#xa0;mm and 0.121&#xa0;mm for the two case studies, with standard deviations of 0.124&#xa0;mm and 0.254&#xa0;mm, respectively. The proposed method demonstrates superiority in dimensional accuracy, manufacturing efficiency, and material utilization, providing a new insight for the high-efficiency and precision manufacturing of fiber-reinforced composites.</p>

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Research on hybrid additive-subtractive manufacturing process and path planning for carbon fiber composites

  • 期刊论文

  • 发表日期：

    2026年3月26日

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  • Yuanzheng Zuo,
  • Hongtao Wang,
  • Ning Dai

摘要

Currently, additive manufacturing of carbon fiber reinforced composites (CFRPs) offers advantages such as high forming flexibility and material utilization, it still suffers from limitations in dimensional accuracy, surface quality, and feature formation. This paper explores a hybrid additive-subtractive manufacturing approach for CFRPs and proposes a collaborative process that integrates feature classification, manufacturing process design and path planning. Firstly, a feature tree of the model is constructed and feature types are classified. Based on the feature tree and the error-driven allowance allocation strategy, rational additive and subtractive processes partitioning is achieved, which improves machining efficiency and resource utilization. Furthermore, a planar deposition path planning method based on undirected graph of scanline is proposed to avoid additive defects affecting subtractive machining quality. The simulation and experimental validation show that the average dimensional deviations are 0.032 mm and 0.121 mm for the two case studies, with standard deviations of 0.124 mm and 0.254 mm, respectively. The proposed method demonstrates superiority in dimensional accuracy, manufacturing efficiency, and material utilization, providing a new insight for the high-efficiency and precision manufacturing of fiber-reinforced composites.