Research Papers

Automated Sensor Selection and Fusion for Monitoring and Diagnostics of Plunge Grinding

[+] Author and Article Information
Niranjan Subrahmanya, Yung C. Shin

Mechanical Engineering, Purdue University, West Lafayette, IN 47907

J. Manuf. Sci. Eng 130(3), 031014 (Jun 05, 2008) (11 pages) doi:10.1115/1.2927439 History: Received November 20, 2006; Revised March 06, 2008; Published June 05, 2008

This paper deals with the development of an online monitoring system based on feature-level sensor fusion and its application to OD plunge grinding. Different sensors are used to measure acoustic emission, spindle power, and workpiece vibration signals, which are used to monitor three of the most common faults in grinding—workpiece burn, chatter, and wheel wear. Although a number of methods have been reported in recent literature for monitoring these faults, they have not found widespread application in industry as no single method or feature has been shown to be successful for all setups and for all wheel-workpiece combinations. This paper proposes a systematic approach, which allows the development and deployment of process-monitoring systems via automated sensor and feature selection combined with parameter-free model training, both of which are especially crucial for implementation in industry. The proposed algorithm makes use of “sparsity-promoting” penalty terms to encourage sensor and feature selection while the “hyperparameters” of the algorithm are tuned using an approximation of the leave-one-out error. Experimental results obtained for monitoring burn, chatter, and wheel wear from a plunge grinding test bed show the effectiveness of the proposed method.

Copyright © 2008 by American Society of Mechanical Engineers
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Figure 1

Overall structure of the proposed process monitoring module

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Figure 2

Instrumentation setup of the test bed

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Figure 3

Experiments based on burn threshold model

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Figure 4

Chatter workpiece dimensions (units: in.) (Ref. 35)

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Figure 5

(a) Vibration amplitude at different work speed—infeed combinations (b) Vibration amplitude at different work speed—plunge width combinations.

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Figure 6

Grinding wheel wear versus work material removal at different values of heq

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Figure 7

Distribution of data using Top 3 features for burn monitoring. Each feature has been standardized so that it has zero mean and unit standard deviation.

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Figure 8

Distribution of data using Top 3 features for chatter monitoring. Each feature has been standardized so that it has zero mean and unit standard deviation.

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Figure 9

Wheel wear prediction using only the top ranked feature (mean power)




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