Considering the Bottom Edge Cutting Effect of the Carbon Fiber Reinforced Polymer Milling Force Prediction Model and Optimization of Machining Parameters

The milling force plays a pivotal role in CFRP milling. Modeling of the milling force is helpful to explore the changing law, optimize the processing parameters, and then reduce the appearance of defects. However, most of the existing models ignore the effect of the bottom edge. In this paper, the prediction of milling force in CFRP milling processes is taken as the research object. By analyzing the milling mechanism and considering the end milling cutter's bottom cutting edge, the prediction model of milling force was established. Based on the experimental data and simulation data of milling force, the milling force coefficient was obtained by inverse calculation. Subsequently, the predicted cutting force was compared with the experimental cutting force, showing a maximum error of 14.5%, which is within a reasonable range, and the correctness of the model was verified. Furthermore, combined with the delamination damage and the milling force prediction model, a multi-objective optimization model of milling parameters was established, and the genetic algorithm was used to solve the model. The unidirectional carbon fiber plate with a fiber direction angle of 45° was selected as the optimization example. The minimum delamination damage was obtained under the cutting conditions of a spindle speed of 4903.1569 r/min, feed rate per tooth of 0.01 mm/z, and an axial depth of cut of 0.5 mm, and the experimental verification was carried out. The feasibility of the genetic algorithm in CFRP milling parameter optimization modeling was also verified.

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Publication Details

Journal
Materials
Published
2024-11-28
DOI
https://doi.org/10.3390/ma17235844
Citations
3
Primary Topic
Advanced machining processes and optimization
Type
article
Field-Weighted Citation Impact
0.64
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article

Considering the Bottom Edge Cutting Effect of the Carbon Fiber Reinforced Polymer Milling Force Prediction Model and Optimization of Machining Parameters

Yu‐Shu Lai, Guixing Wang, Yifan Yang, Yiwei Zhang et al.
3 citations
Materials
Advanced machining processes and optimization
0.64
article

Considering the Bottom Edge Cutting Effect of the Carbon Fiber Reinforced Polymer Milling Force Prediction Model and Optimization of Machining Parameters

Yu‐Shu Lai, Guixing Wang, Yifan Yang, Yiwei Zhang, Mengke Yan
article en
3 citations

Abstract

The milling force plays a pivotal role in CFRP milling. Modeling of the milling force is helpful to explore the changing law, optimize the processing parameters, and then reduce the appearance of defects. However, most of the existing models ignore the effect of the bottom edge. In this paper, the prediction of milling force in CFRP milling processes is taken as the research object. By analyzing the milling mechanism and considering the end milling cutter's bottom cutting edge, the prediction model of milling force was established. Based on the experimental data and simulation data of milling force, the milling force coefficient was obtained by inverse calculation. Subsequently, the predicted cutting force was compared with the experimental cutting force, showing a maximum error of 14.5%, which is within a reasonable range, and the correctness of the model was verified. Furthermore, combined with the delamination damage and the milling force prediction model, a multi-objective optimization model of milling parameters was established, and the genetic algorithm was used to solve the model. The unidirectional carbon fiber plate with a fiber direction angle of 45° was selected as the optimization example. The minimum delamination damage was obtained under the cutting conditions of a spindle speed of 4903.1569 r/min, feed rate per tooth of 0.01 mm/z, and an axial depth of cut of 0.5 mm, and the experimental verification was carried out. The feasibility of the genetic algorithm in CFRP milling parameter optimization modeling was also verified.

MaterialsVol. 17(23)
Southwest Petroleum University (CN), Chongqing Three Gorges University (CN)
Openalex Percentile: Top 34%
Advanced machining processes and optimization
0.64
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