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    Fault diagnosis within multistage machining processes using linear discriminant analysis: a case study in automotive industry

    , Article Quality Technology and Quantitative Management ; Volume 14, Issue 2 , 2017 , Pages 129-141 ; 16843703 (ISSN) Bazdar, A ; Baradaran Kazemzadeh, R ; Akhavan Niaki, S. T ; Sharif University of Technology
    Taylor and Francis Ltd  2017
    Abstract
    Statistical process control provides useful tools to improve the quality of multistage machining processes, specifically in continuous manufacturing lines, where product characteristics are measured at the final station. In order to reduce process errors, variation source identification has been widely applied in machining processes. Although statistical estimation and pattern matching-based methods have been utilized to monitor and diagnose machining processes, most of these methods focus on stage-by-stage inspection using complex models and patterns. However, because of the existence of high rate alarms and the complexity of the machining processes, a surrogate modelling is needed to solve...