改进了通用螺旋圆柱铣刀的封闭切削力模型,并将其应用于切削功率和能量需求建模

IF 1.9 3区 工程技术 Q3 ENGINEERING, MANUFACTURING
C. Ozoegwu
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引用次数: 2

摘要

为提高通用螺旋铣刀切削力的精度,对原有的封闭模型进行了升级,并演示了升级后的模型在切削力封闭建模中的应用。结果表明,所提出的模型对传统的固定螺旋角铣刀具有较好的数值精度,而原有的模型对固定螺旋角铣刀具有较好的数值精度。升级封闭模型、等效原始封闭模型和等效数值方法的误差分别为0.00%、12.15%和50.66%。在谐波情况下,改进后的封闭模型对变螺旋刀具具有更高的精度适用性。升级封闭模型、等效原始封闭模型和等效数值方法的典型误差分别为1.37%、4.84%和9.94%。利用所建立的封闭切削力模型,建立了通用螺旋圆柱铣刀的封闭切削功率模型,并应用于平均切削功率的数值计算。评估平均切削功率(与公布的值一致)和主轴速度的数据集,然后用于铣床平均功率需求的经验校准。模型的高拟合优度体现在[公式:见文本]的高值为0.9980、0.9834和0.9472,平均百分比误差(MPE)的低值为- 0.1247、- 0.4137和- 0.6242。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Upgraded closed-form cutting force models for general-helix cylindrical milling tools with application to cutting power and energy demand modeling
This paper upgrades the original closed-form models of cutting force for general-helix milling tools for higher accuracy and demonstrates an application of the upgraded models in closed-form modeling of cutting power. The proposed models are shown to be numerically exact for the conventional fixed helix angle milling tools while the original models are not even though they are more accurate than the numerical methods. Errors of 0.00%, 12.15%, and 50.66% are recorded for an upgraded closed-form model, the equivalent original closed-form model, and an equivalent numerical method. Higher accurate applicability of the upgraded closed-form models to variable helix tools is also demonstrated for the harmonic case. Typical errors of 1.37%, 4.84%, and 9.94% are recorded for an upgraded closed-form model, the equivalent original closed-form model, and an equivalent numerical method. The proposed closed-form cutting force models are used to formulate new closed-form cutting power models for general-helix cylindrical milling tools which are applied in numerical evaluation of average cutting power. Evaluated data sets of average cutting power (seen to agree with published values) and spindle speed are then used in empirical calibration of average milling machine power demand. The high goodness-of-fit of the models with three published measured data sets are reflected in the high [Formula: see text] values of 0.9980, 0.9834, and 0.9472 and low mean percentage errors (MPE) of −0.1247, −0.4137, and −0.6242.
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来源期刊
CiteScore
5.10
自引率
30.80%
发文量
167
审稿时长
5.1 months
期刊介绍: Manufacturing industries throughout the world are changing very rapidly. New concepts and methods are being developed and exploited to enable efficient and effective manufacturing. Existing manufacturing processes are being improved to meet the requirements of lean and agile manufacturing. The aim of the Journal of Engineering Manufacture is to provide a focus for these developments in engineering manufacture by publishing original papers and review papers covering technological and scientific research, developments and management implementation in manufacturing. This journal is also peer reviewed. Contributions are welcomed in the broad areas of manufacturing processes, manufacturing technology and factory automation, digital manufacturing, design and manufacturing systems including management relevant to engineering manufacture. Of particular interest at the present time would be papers concerned with digital manufacturing, metrology enabled manufacturing, smart factory, additive manufacturing and composites as well as specialist manufacturing fields like nanotechnology, sustainable & clean manufacturing and bio-manufacturing. Articles may be Research Papers, Reviews, Technical Notes, or Short Communications.
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