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加工参数和刀具磨损对微铣削表面均匀性的影响

Effect of Machining Parameters and Tool Wear on Surface Uniformity in Micro-Milling.

作者信息

Sun Zhanwen, To Suet

机构信息

State Key Laboratory in Ultra-Precision Machining Technology, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China.

出版信息

Micromachines (Basel). 2018 May 29;9(6):268. doi: 10.3390/mi9060268.

DOI:10.3390/mi9060268
PMID:30424201
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6187721/
Abstract

In micro-milling, the periodically varying chip thickness, which varies with tool rotation, leads to varying degrees of minimum chip thickness effect and ploughing effect during surface generation. This results in a change of roughness in the cross-sectional direction of the micro-grooves, giving a non-uniform surface quality. However, the factors influencing surface uniformity in micro-milling are not fully understood. In the present work, the effect of the machining parameters and tool wear on surface uniformity in micro-milling is theoretically and experimentally studied. A mathematical model is proposed to predict the varying surface roughness in the cross-sectional direction of the micro-grooves, which is experimentally validated by fabricating a set of 800 µm wide micro-grooves. The theoretical and experimental results reveal that, compared to the normally adopted Ra or Sa, the relative standard deviation of roughness (RSDS) is more appropriate to evaluating surface uniformity. When machining under small feed rates and small cutting depths, the surface uniformity deteriorates as the feed rate increases and improves as the cutting depth increases. The blunt cutting edge induced by tool wear enhances the surface uniformity and increases the surface roughness at the same time. This research furthers understanding of the various cutting mechanisms in micro-milling and can be applied to the optimization of machining parameters in micro-milling.

摘要

在微铣削中,随刀具旋转周期性变化的切屑厚度,会在表面生成过程中导致不同程度的最小切屑厚度效应和耕犁效应。这会导致微槽横截面方向上的粗糙度发生变化,从而使表面质量不均匀。然而,影响微铣削表面均匀性的因素尚未得到充分理解。在本研究中,从理论和实验两方面研究了加工参数和刀具磨损对微铣削表面均匀性的影响。提出了一个数学模型来预测微槽横截面方向上变化的表面粗糙度,并通过加工一组宽度为800 µm的微槽进行了实验验证。理论和实验结果表明,与通常采用的Ra或Sa相比,粗糙度相对标准偏差(RSDS)更适合评估表面均匀性。在小进给率和小切削深度下加工时,表面均匀性会随着进给率的增加而变差,随着切削深度的增加而改善。刀具磨损导致的钝切削刃会提高表面均匀性,同时增加表面粗糙度。本研究进一步加深了对微铣削中各种切削机制的理解,并可应用于微铣削加工参数的优化。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/cd904dc5031b/micromachines-09-00268-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/1c216be3d4dc/micromachines-09-00268-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/26958b611bb3/micromachines-09-00268-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/51a68632cf7c/micromachines-09-00268-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/c5c35b3000bc/micromachines-09-00268-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/cd3a94bd7d66/micromachines-09-00268-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/73361b3b6cb8/micromachines-09-00268-g006a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/9dd509b36ca3/micromachines-09-00268-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/c689185a54c2/micromachines-09-00268-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/f9724cca72b2/micromachines-09-00268-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/404c3562781f/micromachines-09-00268-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/4f08efd60977/micromachines-09-00268-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/cd904dc5031b/micromachines-09-00268-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/1c216be3d4dc/micromachines-09-00268-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/26958b611bb3/micromachines-09-00268-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/51a68632cf7c/micromachines-09-00268-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/c5c35b3000bc/micromachines-09-00268-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/cd3a94bd7d66/micromachines-09-00268-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/73361b3b6cb8/micromachines-09-00268-g006a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/9dd509b36ca3/micromachines-09-00268-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/c689185a54c2/micromachines-09-00268-g008a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/f9724cca72b2/micromachines-09-00268-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/404c3562781f/micromachines-09-00268-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/4f08efd60977/micromachines-09-00268-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8256/6187721/cd904dc5031b/micromachines-09-00268-g012.jpg

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