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Cutting mechanisms in micro-endmilli...

Liu, Xinyu.

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Cutting mechanisms in micro-endmilling and their influence on surface generation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Cutting mechanisms in micro-endmilling and their influence on surface generation./
Author:	Liu, Xinyu.
Description:	190 p.
Notes:	Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 4067.
Contained By:	Dissertation Abstracts International67-07B.
Subject:	Engineering, Mechanical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3223660
ISBN:	9780542775062

Cutting mechanisms in micro-endmilling and their influence on surface generation.
Liu, Xinyu.

Cutting mechanisms in micro-endmilling and their influence on surface generation. - 190 p.

Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 4067.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.

In this research, the influence of the unique cutting mechanisms in micro-endmilling on surface generation have been studied. Comprehensive surface generation models for both the sidewall and floor surfaces have been developed that combine both deterministic and stochastic models by superposition. Six factors were identified as important on surface generation in micro-endmilling: (1) process kinematics; (2) process dynamics; (3) cutting edge geometry; (4) elastic recovery of the workpiece material; (5) minimum chip thickness effect and ploughing; (6) micro-burr formation. Factors (1)-(4) affect the deterministic surface roughness and factors (5)-(6) affect the stochastic surface roughness.

ISBN: 9780542775062Subjects--Topical Terms:

783786
Engineering, Mechanical.

Cutting mechanisms in micro-endmilling and their influence on surface generation.
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020 $a 9780542775062
035 $a (UnM)AAI3223660
035 $a AAI3223660
040 $a UnM $c UnM
100 1 $a Liu, Xinyu. $3 1920374
245 1 0 $a Cutting mechanisms in micro-endmilling and their influence on surface generation.
300 $a 190 p.
500 $a Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 4067.
500 $a Advisers: Shiv G. Kapoor; Richard E. DeVor.
502 $a Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2006.
520 $a In this research, the influence of the unique cutting mechanisms in micro-endmilling on surface generation have been studied. Comprehensive surface generation models for both the sidewall and floor surfaces have been developed that combine both deterministic and stochastic models by superposition. Six factors were identified as important on surface generation in micro-endmilling: (1) process kinematics; (2) process dynamics; (3) cutting edge geometry; (4) elastic recovery of the workpiece material; (5) minimum chip thickness effect and ploughing; (6) micro-burr formation. Factors (1)-(4) affect the deterministic surface roughness and factors (5)-(6) affect the stochastic surface roughness.
520 $a The deterministic sidewall surface generation model includes the effects of the process kinematics, dynamics, tool edge serration, and spindle runout. The stochastic model predicts the increased surface roughness generated from ploughing due to the minimum chip thickness effect. The deterministic floor surface generation model characterizes the 3D surface topography over the entire floor surface and considers the effects of the minimum chip thickness, the elastic recovery and the transverse vibration. The variation of the ploughing amount across the swept arc of the cutter due to the varying chip load conditions is accounted for in the stochastic model.
520 $a In order to account for the minimum chip thickness effect, an analytical model has been developed for the estimation of the minimum chip thickness for a variety of workpiece materials.
520 $a The surface generation models are experimentally calibrated and validated. The deterministic models are validated using large feedrate tests. The models predict the 3D surface roughness within 19% for sidewall surface and within 18% for floor surface. The stochastic portion of the observed surface roughness data is determined by filtering this data with the validated deterministic model. The stochastic models are then calibrated and validated using independent data sets with errors within 12%.
520 $a The validated models are used to study the effects of tool geometry, process conditions, spindle runout, process kinematics and dynamics on the machined surface roughness. One of the most important findings is that a feedrate of 1-1.5 times of the minimum chip thickness is a good starting point for process planning to achieve small surface roughness.
590 $a School code: 0090.
650 4 $a Engineering, Mechanical. $3 783786
690 $a 0548
710 2 0 $a University of Illinois at Urbana-Champaign. $3 626646
773 0 $t Dissertation Abstracts International $g 67-07B.
790 1 0 $a Kapoor, Shiv G., $e advisor
790 1 0 $a DeVor, Richard E., $e advisor
790 $a 0090
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3223660