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Proceedings Paper

In-process and post-process measurements of drill wear for control of the drilling process
Author(s): Tien-I Liu; George Liu; Zhiyu Gao
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Paper Abstract

Optical inspection was used in this research for the post-process measurements of drill wear. A precision toolmakers’ microscope was used. Indirect index, cutting force, is used for in-process drill wear measurements. Using in-process measurements to estimate the drill wear for control purpose can decrease the operation cost and enhance the product quality and safety. The challenge is to correlate the in-process cutting force measurements with the post-process optical inspection of drill wear. To find the most important feature, the energy principle was used in this research. It is necessary to select only the cutting force feature which shows the highest sensitivity to drill wear. The best feature selected is the peak of torque in the drilling process. Neuro-fuzzy systems were used for correlation purposes. The Adaptive-Network-Based Fuzzy Inference System (ANFIS) can construct fuzzy rules with membership functions to generate an input-output pair. A 1x6 ANFIS architecture with product of sigmoid membership functions can in-process measure the drill wear with an error as low as 0.15%. This is extremely important for control of the drilling process. Furthermore, the measurement of drill wear was performed under different drilling conditions. This shows that ANFIS has the capability of generalization.

Paper Details

Date Published: 15 November 2011
PDF: 8 pages
Proc. SPIE 8321, Seventh International Symposium on Precision Engineering Measurements and Instrumentation, 83213T (15 November 2011); doi: 10.1117/12.905465
Show Author Affiliations
Tien-I Liu, California State Univ., Sacramento (United States)
George Liu, California State Univ., Long Beach (United States)
Zhiyu Gao, California State Univ., Sacramento (United States)

Published in SPIE Proceedings Vol. 8321:
Seventh International Symposium on Precision Engineering Measurements and Instrumentation
Kuang-Chao Fan; Man Song; Rong-Sheng Lu, Editor(s)

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