Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes

Penedo, Francisco and Haber Guerra, Rodolfo E. and Gajate, Agustín and Toro Matamoros, Raúl Mario del (2012). Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes. "IEEE Transactions on Industrial Informatics", v. 8 (n. 4); pp. 811-818. ISSN 1551-3203. https://doi.org/10.1109/TII.2012.2205699.

Description

Title: Hybrid incremental modeling based on least squares and fuzzy K-NN for monitoring tool wear in turning processes
Author/s:
  • Penedo, Francisco
  • Haber Guerra, Rodolfo E.
  • Gajate, Agustín
  • Toro Matamoros, Raúl Mario del
Item Type: Article
Título de Revista/Publicación: IEEE Transactions on Industrial Informatics
Date: November 2012
ISSN: 1551-3203
Volume: 8
Subjects:
Faculty: Centro de Automática y Robótica (CAR) UPM-CSIC
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

There is now an emerging need for an efficient modeling strategy to develop a new generation of monitoring systems. One method of approaching the modeling of complex processes is to obtain a global model. It should be able to capture the basic or general behavior of the system, by means of a linear or quadratic regression, and then superimpose a local model on it that can capture the localized nonlinearities of the system. In this paper, a novel method based on a hybrid incremental modeling approach is designed and applied for tool wear detection in turning processes. It involves a two-step iterative process that combines a global model with a local model to take advantage of their underlying, complementary capacities. Thus, the first step constructs a global model using a least squares regression. A local model using the fuzzy k-nearest-neighbors smoothing algorithm is obtained in the second step. A comparative study then demonstrates that the hybrid incremental model provides better error-based performance indices for detecting tool wear than a transductive neurofuzzy model and an inductive neurofuzzy model.

More information

Item ID: 21244
DC Identifier: http://oa.upm.es/21244/
OAI Identifier: oai:oa.upm.es:21244
DOI: 10.1109/TII.2012.2205699
Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6224180
Deposited by: Memoria Investigacion
Deposited on: 06 Nov 2013 19:03
Last Modified: 21 Apr 2016 11:23
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