Modeling micro-end-milling operations. Part I: analytical cutting force model

Bao, W.Y.; Tansel, İbrahim N.

doi:10.1016/s0890-6955(00)00054-7

Cited by 216 publications

(115 citation statements)

References 4 publications

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“…However it is difficult to apply these researches to micro end-milling processes because micro end mill size is quietly smaller than macro scale end mills. For this minimum chip thickness effect was studied by considering more significant size of tool tip nose radius than macro tools [5]- [6]. If micro end-milling phenomenon would be observed in a microscopic view point, the minimum chip thickness maybe certainly considered.…”

Section: Cutting Force Prediction Modelmentioning

confidence: 99%

“…However, micro end-milling processes were known as different phenomenon as respect to macro end-milling. Many past researches concerned complicated different models for micro end-milling from macro end-milling [5]- [7]. However, cutting force models for both macro and micro end milling were fundamentally based on measured cutting forces for a series of experimental machining processes.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Cutting Force and Tool Deflection in Micro Flat End Milling

Gye¹,

Song²,

Lim³

et al. 2013

IJMMM

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Abstract-This paper presents an investigation of prediction of cutting force and tool deflection in micro flat end milling. To predict cutting forces specific cutting force coefficients KT and KR were used. In fact, various cutting forces prediction models were proposed in past researches for conventional sized machining processes. However, micro end-milling processes were known as different phenomenon as respect to macro end-milling. Many past researches concerned complicated different models for micro end-milling from macro end-milling. However, cutting force models for both macro and micro end milling were fundamentally based on measured cutting forces for a series of experimental machining processes. Then, cutting force model proposed by Tlusty, which was developed for macro end-milling, was applied for micro flat end milling because this model is relatively simple. Finite element method was used to predict tool deflection based on predicted cutting forces. Predicted tool deflection amounts and actual machined profiles were compared each other in order to check out the differences between them.Index Terms-Cutting force, micro end-mill, finite element method, tool deflection.

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Section: Cutting Force Prediction Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Cutting Force and Tool Deflection in Micro Flat End Milling

Gye¹,

Song²,

Lim³

et al. 2013

IJMMM

View full text Add to dashboard Cite

show abstract

“…The helix angle plays an important role on tool life [6]. Bao and Tansel [7], [8] modified their analytical model [1] to represent tool wear.…”

Section: Cutting Edge Radiusmentioning

confidence: 99%

Next generation spindles for micromilling.

Pathak

Payne

Gill

et al. 2004

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There exists a wide variety of important applications for micro-and meso-scale mechanical systems in the commercial and defense sectors, which require high-strength materials and complex geometries that cannot be produced using current MEMS fabrication technologies. Micromilling has great potential to fill this void in MEMS technology by adding the capability of free form machining of complex 3D shapes from a wide variety and combination of traditional, well-understood engineering alloys, glasses and ceramics. Inefficiencies in micromilling result from the relationships between a cutting tool's breaking strength, the applied cutting force, and the metal removal rate. Because machining times in mesofeatures scale inversely to the part size, a feature 1/10 th as large will take 10 times as long to machine. Also, required chip sizes of 1 m or less are cut with tools having edge radius of 2-3 m, the cutting edge effectively has a highly negative rake angle, cutting forces are increased significantly causing chip loads to be further reduced and the machining takes even longer than predicted above. However, cutting forces do not increase with cutting speed, so faster spindles with reduced tool runout are the path to achieve efficient mesoscale milling.This research explored the development of new ultra-high speed micromilling spindles. A novel air-bearing spindle design is discussed that will run at very high speeds (450,000 rpm) and provide very minimal runout allowing the best use of micromilling cutters and reducing overall 3 machining time drastically. Two generations of this spindle design were completed; one with an air bearing supported tool shaft and one with a novel rolling element bearing supported tool shaft. Both designs utilized friction-drive systems that relied on diameter differences between the drive wheel (operating at speeds up to 90,000 rpm) and the tool shaft to achieve high rotational tool speeds. Runout, stiffness, and machining tests were conducted with the spindle designs and though they both showed promise for ultra-high speed machining, runout issues in the friction drive and in the stock tools kept the system from achieving sustained machining capability.4

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“…An analytical force model for micro-end-milling including tool run-out and tool wear effects was investigated in a series of papers by Bao et al see [4][5][6]. In addition, Li et al [24] proposed a model of a three dimensional cutting force for micro-end-milling operation which includes a new nominal uncut chip thickness algorithm.…”

Section: Introductionmentioning

confidence: 99%

A statistical filtering method for denoising of micro-force measurements

Kazimierski

Piotrowska-Kurczewski

Böhmermann

et al. 2016

Int J Adv Manuf Technol

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Precise measurement of mechanical forces is crucial to efficient micro-manufacturing. The quality of such measurements depends heavily on the properties of the noise inevitably accompanying every measurement process. In the micro-range, the signal-to-noise ratio tends to be very low, and the noise dynamic varies for different frequencies. In result, common denoising methods that assume white noise perform poorly in this setting. In this paper, a novel, easily implementable denoising method based on a local statistic of the measured data's spectrum is proposed. By testing it on a representative dataset, it is shown that the proposed method is robust and stable. Particularly, it allows for an efficient retrieval of the force signal encountered in micro-milling processes.

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Modeling micro-end-milling operations. Part I: analytical cutting force model

Cited by 216 publications

References 4 publications

Prediction of Cutting Force and Tool Deflection in Micro Flat End Milling

Prediction of Cutting Force and Tool Deflection in Micro Flat End Milling

Next generation spindles for micromilling.

A statistical filtering method for denoising of micro-force measurements

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