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Research Papers

Stochastic Modeling and Analysis of Spindle Power During Hard Milling With a Focus on Tool Wear

[+] Author and Article Information
Xingtao Wang

Department of Mechanical and
Materials Engineering,
University of Nebraska-Lincoln,
W317.6 NH,
Lincoln, NE 68588-0526
e-mail: xingtao.wang@huskers.unl.edu

Robert E. Williams

Department of Mechanical and
Materials Engineering,
University of Nebraska-Lincoln,
W317.6 NH,
Lincoln, NE 68588-0526
e-mail: rwilliams2@unl.edu

Michael P. Sealy

Department of Mechanical and
Materials Engineering,
University of Nebraska-Lincoln,
W306 NH,
Lincoln, NE 68588-0526
e-mail: sealy@unl.edu

Prahalad K. Rao

Department of Mechanical and
Materials Engineering,
University of Nebraska-Lincoln,
NH East 303E,
Lincoln, NE 68588-0526
e-mail: rao@unl.edu

Yuebin Guo

Department of Mechanical Engineering,
The University of Alabama,
286 Hardaway Hall, 401 7th Avenue,
Tuscaloosa, AL 35401
e-mail: yguo@eng.ua.edu

1Corresponding author.

Manuscript received December 15, 2017; final manuscript received June 2, 2018; published online August 31, 2018. Assoc. Editor: Karl R. Haapala.

J. Manuf. Sci. Eng 140(11), 111011 (Aug 31, 2018) (8 pages) Paper No: MANU-17-1785; doi: 10.1115/1.4040728 History: Received December 15, 2017; Revised June 02, 2018

The rapid development of modern science and technology brings with it a high demand for manufacturing quality. The surface integrity of a machined part is a critical factor which needs to be considered in the selection of the appropriate machining processes. Surface integrity is also tightly linked with tool wear. Tool wear is one of the most significant and necessary parameters to be considered for machining sustainability. By monitoring and predicting tool wear, it is possible to improve sustainability by reducing the scrap rate due to poor surface integrity. In this work, data-dependent systems (DDS), a stochastic modeling and analysis technique, was applied to study the power of spindle motor during a hard milling operation. The objective was to correlate the spindle power to tool wear conditions using DDS analysis. The spindle power was monitored, and the time series trends were decomposed to study the frequency variation with different severities of tool wear conditions and processing parameters. Analysis of variance (ANOVA) was also used to determine factors significant to the power by a spindle motor. Experiments indicate that low-level frequency of spindle power is correlated with the amount of tool wear, cutting speed, and feed per tooth. The results suggest that effective tool wear monitoring may be achieved by focusing on low-level frequencies highlighted by DDS methodology.

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Figures

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Fig. 1

Flank wear morphology along minor cutting edge: (a) fresh tool, (b) VB = 0.08 (t = 40 min), (c) VB = 0.10 mm (t = 100 min), (d) VB = 0.15 (t = 140 min), and (e) VB = 0.20 mm (t = 160 min)

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Fig. 2

Machine and power analyzer setup

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Fig. 3

Spindle power curves during milling

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Fig. 4

Milling experimental setup

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Fig. 5

Main effects plot for spindle power

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Fig. 6

Data-dependent system methodology

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Fig. 7

Decomposed frequencies distribution

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Fig. 8

Relationship between tool wear and low-level frequencies

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Fig. 9

Relationship between tool wear and medium-level frequencies

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Fig. 10

Relationship between tool wear and high-level frequencies

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Fig. 11

Milling path comparison between sharp insert and worn insert

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Fig. 12

Relationship between cutting speed and low-level frequencies

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Fig. 13

Relationship between cutting speed and medium-level frequencies

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Fig. 14

Relationship between feed per tooth and low-level frequencies

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