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Research Papers

Robust Tool Wear Monitoring Using Systematic Feature Selection in Turning Processes With Consideration of Uncertainties

[+] Author and Article Information
Bin Zhang

School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: zhan1881@purdue.edu

Christopher Katinas

School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: ckatinas@purdue.edu

Yung C. Shin

Fellow ASME
School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: shin@purdue.edu

1Corresponding author.

Manuscript received November 9, 2017; final manuscript received May 2, 2018; published online June 4, 2018. Assoc. Editor: Dragan Djurdjanovic.

J. Manuf. Sci. Eng 140(8), 081010 (Jun 04, 2018) (12 pages) Paper No: MANU-17-1696; doi: 10.1115/1.4040267 History: Received November 09, 2017; Revised May 02, 2018

This paper describes a robust tool wear monitoring scheme for turning processes using low-cost sensors. A feature normalization scheme is proposed to eliminate the dependence of signal features on cutting conditions, cutting tools, and workpiece materials. In addition, a systematic feature selection procedure in conjunction with automated signal preprocessing parameter selection is presented to select the feature set that maximizes the performance of the predictive tool wear model. The tool wear model is built using a type-2 fuzzy basis function network (FBFN), which is capable of estimating the uncertainty bounds associated with tool wear measurement. Experimental results show that the tool wear model built with the selected features exhibits high accuracy, generalized applicability, and exemplary robustness: The model trained using 4140 steel turning test data could predict the tool wear for Inconel 718 turning with a root-mean-square error (RMSE) of 7.80 μm and requests tool changes with a 6% margin on average. Furthermore, the developed method was successfully applied to tool wear monitoring of Ti–6Al–4V alloy despite different mechanisms of tool wear, i.e., crater wear instead of flank wear.

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Figures

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Fig. 1

Overall architecture and data flow of the proposed tool wear monitoring system

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Fig. 2

Flow chart of the coupled feature and preprocessing parameter selection

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Fig. 3

Instrumentation diagram of the test bed

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Fig. 4

Comparison of the mean power feature before and after normalization

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Fig. 5

Mean spindle power with different moving average window sizes

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Fig. 6

The correlation of y-direction vibration RMS against tool wear

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Fig. 7

Statistical significance of y-direction vibration features (Freq Ctrd: Frequency centroid, PSD MoI: PSD moment of inertia)

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Fig. 8

Tool wear prediction for Inconel 718 using the optimal feature set

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Fig. 9

Comparison of the type-2 FBFN prediction upper bounds and tool wear measurement upper bounds when the tool change alert issued

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Fig. 10

Tool change margin with respect to number of classes

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Fig. 11

Crater wear prediction for Ti–6Al–4V

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