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research-article

Stochastic Modeling and Analysis of Spindle Energy Consumption During Hard Milling

[+] Author and Article Information
Xingtao Wang

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

Robert Williams

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

Michael P. Sealy

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

Prahalada Rao

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

Y.B. Guo

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

1Corresponding author.

ASME doi:10.1115/1.4040728 History: Received December 15, 2017; Revised June 02, 2018

Abstract

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 spindle motor energy consumption 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 energy consumption 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. Results suggest that effective tool wear monitoring may be achieved by focusing on low-level frequencies highlighted by DDS methodology.

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