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A fuzzy-nets tool-breakage detection system for end-milling operations

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Abstract

This paper describes a new approach, the fuzzy-nets system, for monitoring tool breakage in end-milling operations. The fuzzy-nets tool-breakage detection (FNTBD) system has a self-learning capability to generate rule bases and to fine tune the term sets of each linguistic variable to the appropriate level of granularity. A self-learning algorithm for developing the FNTBD system consists of five steps:

  1. 1.

    Divide the input space into fuzzy regions.

  2. 2.

    Generate fuzzy rules from given data pairs through experimentation.

  3. 3.

    Avoid conflicting rules based on top-down or bottom-up methodologies.

  4. 4.

    Develop a combined fuzzy rule base.

  5. 5.

    Determine a mapping system based on the fuzzy rule base.

Learning is accomplished by fine-tuning the parameters in the fuzzy-nets system within the on-line learning capability. Following establishment of the rule base, the performance of the FNTBD system is examined for an end-milling operation. It was observed and verified experimentally that this new FNTBD approach can successfully detect tool breakage in end-milling operations.

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Authors and Affiliations

  1. Department of Industrial Education and Technology, Iowa State University, 211 I. Ed. II, 50011-3130, Ames, Iowa, USA

    Joseph C. Chen

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  1. Joseph C. Chen
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Chen, J.C. A fuzzy-nets tool-breakage detection system for end-milling operations. Int J Adv Manuf Technol 12, 153–164 (1996). https://doi.org/10.1007/BF01351194

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