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离散刃立铣刀切削力建模与加工性能研究

Research on Cutting Force Modeling and Machining Performance of Discrete-Edge End Mill.

作者信息

Song Ming, Zheng Minli, Gao Siyuan, Dong Baojuan, Zhu Jianping

机构信息

Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, 52 Xuefu Road, Harbin 150080, China.

Rongcheng Campus, Harbin University of Science and Technology, 2006 College Road, Weihai 264200, China.

出版信息

Micromachines (Basel). 2025 Aug 10;16(8):923. doi: 10.3390/mi16080923.

DOI:10.3390/mi16080923
PMID:40872430
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12388261/
Abstract

To address the challenges of complex cutting force formation and low prediction accuracy in discrete-edge end mills, this study proposes a precise cutting force modeling method based on an effective chip slot function. An effective chip slot function is established to quantitatively characterize the dynamic variation of cutting edge engagement along different axial positions. Based on the instantaneous uncut chip thickness theory by Altintas, a high-precision cutting force model suitable for discrete-edge tools is developed. Experimental results show that the proposed model achieves an average prediction error of 4.82%, with a maximum error below 10%, demonstrating its high accuracy and practical applicability. Comparative experiments with conventional continuous-edge end mills under identical machining conditions indicate that the discrete-edge tool can reduce cutting forces ( by 7.2%, by 3.2%), significantly suppress cutting vibrations (fluctuation coefficients reduced by 13.5% and 21.9%, respectively), and lower surface roughness to approximately one-sixth of that produced by conventional tools. The results confirm that discrete-edge end mills exhibit notable advantages in machining stability, cutting force control, and surface quality, providing a solid theoretical foundation for the design and process optimization of high-performance cutting tools.

摘要

为应对离散刃立铣刀复杂切削力形成和预测精度低的挑战,本研究提出一种基于有效切屑槽函数的精确切削力建模方法。建立有效切屑槽函数以定量表征沿不同轴向位置切削刃参与度的动态变化。基于阿尔廷塔斯的瞬时未切削切屑厚度理论,开发了适用于离散刃刀具的高精度切削力模型。实验结果表明,所提出的模型平均预测误差为4.82%,最大误差低于10%,证明了其高精度和实际适用性。在相同加工条件下与传统连续刃立铣刀的对比实验表明,离散刃刀具可降低切削力(分别降低7.2%和3.2%),显著抑制切削振动(波动系数分别降低13.5%和21.9%),并将表面粗糙度降低至传统刀具所产生表面粗糙度的约六分之一。结果证实,离散刃立铣刀在加工稳定性、切削力控制和表面质量方面具有显著优势,为高性能切削刀具的设计和工艺优化提供了坚实的理论基础。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/860e44cd2dd2/micromachines-16-00923-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/b8fe48d3ae6b/micromachines-16-00923-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/fdf379bff6d7/micromachines-16-00923-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/a66c7e83ef5c/micromachines-16-00923-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/713cc8c9ab1a/micromachines-16-00923-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/2e9eb3272d57/micromachines-16-00923-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/fb97124fb1d7/micromachines-16-00923-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/440d81aece55/micromachines-16-00923-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/48233da4ccd6/micromachines-16-00923-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/8fb442aae3fd/micromachines-16-00923-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/e6905495b7c9/micromachines-16-00923-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/860e44cd2dd2/micromachines-16-00923-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/b8fe48d3ae6b/micromachines-16-00923-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/fdf379bff6d7/micromachines-16-00923-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/a66c7e83ef5c/micromachines-16-00923-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/713cc8c9ab1a/micromachines-16-00923-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/2e9eb3272d57/micromachines-16-00923-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/fb97124fb1d7/micromachines-16-00923-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/440d81aece55/micromachines-16-00923-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/48233da4ccd6/micromachines-16-00923-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/8fb442aae3fd/micromachines-16-00923-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/e6905495b7c9/micromachines-16-00923-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f8fc/12388261/860e44cd2dd2/micromachines-16-00923-g011.jpg

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