Neural network design and feature selection using principal component analysis and Taguchi method for identifying wood veneer defects

Baris Yuce, Ernesto Mastrocinque, Michael Sylvester Packianather, Duc Pham, Alfredo Lambiase, Fabio Fruggiero

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)
92 Downloads (Pure)

Abstract

Nowadays, ensuring high quality can be considered the main strength for a company’s success. Especially, in a period of economic recession, quality control is crucial from the operational and strategic point of view. There are different quality control methods and it has been proven that on the whole companies using a continuous improvement approach, eliminating waste and maximizing productive flow, are more efficient and produce more with lower costs. This paper presents a method to optimize the quality control stage for a wood manufacturing firm. The method is based on the employment of the principal component analysis in order to reduce the number of critical variables to be given as input for an artificial neural network (ANN) to identify wood veneer defects. The proposed method allows the ANN classifier to identify defects in real time and increase the response speed during the quality control stage so that veneers with defects do not pass through the whole production cycle but are rejected at the beginning.

Original languageEnglish
Pages (from-to)291-308
Number of pages18
JournalProduction & Manufacturing Research
Volume2
Issue number1
Early online date13 May 2014
DOIs
Publication statusPublished - 2014

Keywords

  • ANN
  • feature extraction
  • PCA
  • Taguchi analysis

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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