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基于材料去除机制的K9玻璃微磨削力预测模型

Model for Predicting the Micro-Grinding Force of K9 Glass Based on Material Removal Mechanisms.

作者信息

Manea Hisham, Cheng Xiang, Ling Siying, Zheng Guangming, Li Yang, Gao Xikun

机构信息

School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, China.

Mechanical Engineering Department, Faculty of Engineering, Sana'a University, Sana'a 12544, Yemen.

出版信息

Micromachines (Basel). 2020 Oct 29;11(11):969. doi: 10.3390/mi11110969.

DOI:10.3390/mi11110969
PMID:33138138
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7693084/
Abstract

K9 optical glass has superb material properties used for various industrial applications. However, the high hardness and low fracture toughness greatly fluctuate the cutting force generated during the grinding process, which are the main factors affecting machining accuracy and surface integrity. With a view to further understand the grinding mechanism of K9 glass and improve the machining quality, a new arithmetical force model and parameter optimization for grinding the K9 glass are introduced in this study. Originally, the grinding force components and the grinding path were analyzed according to the critical depth of plowing, rubbing, and brittle tear. Thereafter, the arithmetical model of grinding force was established based on the geometrical model of a single abrasive grain, taking into account the random distribution of grinding grains, and this fact was considered when establishing the number of active grains participating in cutting N. It should be noted that the tool diameter changed with machining, therefore this change was taking into account when building the arithmetical force model during processing as well as the variable value of the maximum chip thickness a accordingly. Besides, the force analysis recommends how to control the processing parameters to achieve high surface and subsurface quality. Finally, the force model was evaluated by comparing theoretical results with experimental ones. The experimental values of surface grinding forces are in good conformity with the predicted results with changes in the grinding parameters, which proves that the mathematical model is reliable.

摘要

K9光学玻璃具有优良的材料性能,可用于各种工业应用。然而,高硬度和低断裂韧性会使磨削过程中产生的切削力大幅波动,这是影响加工精度和表面完整性的主要因素。为了进一步了解K9玻璃的磨削机理并提高加工质量,本研究引入了一种新的磨削K9玻璃的算术力模型和参数优化方法。首先,根据犁削、摩擦和脆性撕裂的临界深度分析了磨削力分量和磨削路径。此后,基于单个磨粒的几何模型,考虑磨粒的随机分布,建立了磨削力算术模型,并且在确定参与切削的有效磨粒数N时考虑了这一因素。需要注意的是,刀具直径会随加工过程而变化,因此在建立加工过程中的算术力模型时也考虑了这种变化以及最大切屑厚度a的变量值。此外,力分析给出了如何控制加工参数以实现高表面和亚表面质量的建议。最后,通过将理论结果与实验结果进行比较来评估力模型。表面磨削力的实验值与磨削参数变化时的预测结果吻合良好,这证明了该数学模型是可靠的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/379bb0441792/micromachines-11-00969-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/6e6a3fe89f4e/micromachines-11-00969-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/f4309f0ae7c2/micromachines-11-00969-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/28793c9045e9/micromachines-11-00969-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/90c31a48f2aa/micromachines-11-00969-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/509df6987462/micromachines-11-00969-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/4f9174e0fb2d/micromachines-11-00969-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/bfc38119bfbe/micromachines-11-00969-g007a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/385b5e3fafad/micromachines-11-00969-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/e23ebe06dd67/micromachines-11-00969-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/379bb0441792/micromachines-11-00969-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/6e6a3fe89f4e/micromachines-11-00969-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/f4309f0ae7c2/micromachines-11-00969-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/28793c9045e9/micromachines-11-00969-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/90c31a48f2aa/micromachines-11-00969-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/509df6987462/micromachines-11-00969-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/4f9174e0fb2d/micromachines-11-00969-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/bfc38119bfbe/micromachines-11-00969-g007a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/385b5e3fafad/micromachines-11-00969-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/e23ebe06dd67/micromachines-11-00969-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b777/7693084/379bb0441792/micromachines-11-00969-g010.jpg

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本文引用的文献

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