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

B. Yuce, Ernesto Mastronique, M. S. Packianather, D. Pham, A. Lambiase, F. Fruggiero

    Research output: Contribution to journalArticle

    33 Citations (Scopus)
    47 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
    JournalProduction and Manufacturing Research
    Volume2
    Issue number1
    DOIs
    Publication statusPublished - 13 May 2014

    Bibliographical note

    This is an open-access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The moral rights of the named author(s) have been asserted.

    Keywords

    • ANN
    • PCA
    • feature extraction
    • Taguchi analysis

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