Research Papers

Quality and Inspection of Machining Operations: Tool Condition Monitoring

[+] Author and Article Information
John T. Roth

 Penn State Erie, Erie, PA 16563

Dragan Djurdjanovic

 University of Texas, Austin, TX 78712

Xiaoping Yang

 Cummins Inc., Columbus, IN 47202

Laine Mears, Thomas Kurfess

 Clemson University, Clemson, SC 29634

J. Manuf. Sci. Eng 132(4), 041015 (Aug 03, 2010) (16 pages) doi:10.1115/1.4002022 History: Received January 06, 2009; Revised June 03, 2010; Published August 03, 2010; Online August 03, 2010

Tool condition monitoring (TCM) is an important aspect of condition based maintenance (CBM) in all manufacturing processes. Recent work on TCM has generated significant successes for a variety of cutting operations. In particular, lower cost and on-board sensors in conjunction with enhanced signal processing capabilities and improved networking has permitted significant enhancements to TCM capabilities. This paper presents an overview of TCM for drilling, turning, milling, and grinding. The focus of this paper is on the hardware and algorithms that have demonstrated success in TCM for these processes. While a variety of initial successes are reported, significantly more research is possible to extend the capabilities of TCM for the reported cutting processes as well as for many other manufacturing processes. Furthermore, no single unifying approach has been identified for TCM. Such an approach will enable the rapid expansion of TCM into other processes and a tighter integration of TCM into CBM for a wide variety of manufacturing processes and production systems.

Copyright © 2010 by American Society of Mechanical Engineers
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Figure 1

Concept of CBM as transformation of sensing data into information about equipment condition and further into maintenance and operational decisions

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Figure 2

Performance assessment and diagnosis through overlapping of signature distributions

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Figure 3

Concept of feature-based performance prediction with prediction confidence intervals

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Figure 4

Generic sensor fusion architecture described in Ref. 7 for ANN application to tool condition monitoring

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Figure 5

Application of Fourier analysis on two frequency hopping signals

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Figure 6

Binomial joint TFD of the two frequency hopping signals identical to those analyzed in Fig. 5

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Figure 7

Neural network prediction of turning flank wear versus independent experiment (30)

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Figure 8

Comparison of prediction errors for ERNN and match matrix based prediction

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Figure 9

Development of AE process signal over drill life (38)

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Figure 10

Drive current of spindle after analog filtering for OK cutting and broken insert cases (54)

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Figure 11

Scatter plot of TFD indicator versus cut conditions and tool wear VB (74)

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Figure 12

Measurement principle of (87)

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Figure 13

Neural network architecture for learning system of grinding SPLs (90)



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