<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-7743</journal-id>
<journal-title><![CDATA[Ingeniería, investigación y tecnología]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. invest. y tecnol.]]></abbrev-journal-title>
<issn>1405-7743</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Ingeniería]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-77432020000200101</article-id>
<article-id pub-id-type="doi">10.22201/fi.25940732e.2020.21n2.011</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Diseños ortogonales de Taguchi fraccionados]]></article-title>
<article-title xml:lang="en"><![CDATA[Fractional Taguchi orthogonal designs]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Naranjo-Palacios]]></surname>
<given-names><![CDATA[Fernando]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rios-Lira]]></surname>
<given-names><![CDATA[Armando Javier]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pantoja-Pacheco]]></surname>
<given-names><![CDATA[Yaquelin Verenice]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Tapia-Esquivias]]></surname>
<given-names><![CDATA[Moisés]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Tecnológico Nacional de México Instituto Tecnológico de Celaya Departamento de Ingeniería Industrial]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Tecnológico Nacional de México Instituto Tecnológico de Celaya Departamento de Ingeniería Industrial]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<aff id="Af3">
<institution><![CDATA[,Tecnológico Nacional de México Instituto Tecnológico de Celaya Departamento de Ingeniería Industrial]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Tecnológico Nacional de México Instituto Tecnológico de Celaya Departamento de Ingeniería Industrial]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2020</year>
</pub-date>
<volume>21</volume>
<numero>2</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-77432020000200101&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-77432020000200101&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-77432020000200101&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El diseño de experimentos es una herramienta utilizada para descubrir cómo entran en juego distintas variables de un proceso en la obtención de un producto. Existen dos enfoques principales para realizar experimentación, el enfoque clásico y el enfoque de Taguchi. Los diseños de Taguchi son diseños ortogonales que se especializan en estimar efectos principales e interacciones de control por ruido, dejando en segundo plano las interacciones de control por control. Los arreglos ortogonales de Taguchi fueron diseñados de tal manera que un arreglo específico puede ser utilizado para diferentes números de factores, por ejemplo, el L32 se utiliza cuando existen de 16 a 31 factores y requiere de 32 experimentos. Cuando el número de columnas disponibles excede al número de factores que se desea investigar, las columnas sobrantes se utilizan comúnmente para estimar interacciones. Sin embargo, en casos en que el investigador está solo interesado en los efectos principales, correr el arreglo completo podría ser algo innecesario y costoso. La presente investigación tiene como objetivo fraccionar los arreglos ortogonales de Taguchi L8, L12, L16 y L32 de tal forma que la fracción generada sirva únicamente para estimar efectos principales y las corridas restantes se agreguen solo en caso de ser requeridas. El método propuesto se basa en búsqueda exhaustiva y utiliza como criterios de selección la D-optimalidad, los factores de inflación de varianza (FIV) y el índice de balance general (IBG). Únicamente arreglos ortogonales de Taguchi de dos niveles se consideraron para esta investigación. Los resultados de la investigación se traducen en ahorros significativos de recursos, reducción del tiempo de experimentación y del número de corridas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The design of experiments is a tool used to discover how different variables in a process come into play to obtain a product. There are two main approaches to perform experimentation, the classic approach and the Taguchi approach. Taguchi experiments are orthogonal arrays that specialize in estimating main effects and control by noise interactions, leaving in second place control by control interactions. The Taguchi orthogonal arrays were designed in such a way that a specific array can be used for different numbers of factors, for example, the L32 is used when there are 16 to 31 factors and requires 32 experiments. When the number of available columns exceeds the number of factors that we wish to investigate, the remaining columns are used commonly to estimate interactions. Nevertheless, in cases in which the experimenter is interested only in estimating main effects, running the full array could be unnecessary and expensive. This research proposes a method to fractionate the Taguchi orthogonal arrays L8, L12, L16 and L32 in such a way that the fraction generated helps only to estimate main effects and the remaining runs can be added only in cases in which they are required. The proposed approach is based in exhaustive search and uses as selection criteria the D-optimality, variance inflation factors (VIF) and the general balance metric (GBM). Only two-level Taguchi orthogonal arrays were considered for this research. The results of this research translate into significant savings in resources, reduction in experimentation time and reduction in the number of runs.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Experimentos]]></kwd>
<kwd lng="es"><![CDATA[diseño]]></kwd>
<kwd lng="es"><![CDATA[robusto]]></kwd>
<kwd lng="es"><![CDATA[Taguchi]]></kwd>
<kwd lng="es"><![CDATA[fracciones]]></kwd>
<kwd lng="en"><![CDATA[Experiments]]></kwd>
<kwd lng="en"><![CDATA[design]]></kwd>
<kwd lng="en"><![CDATA[robust]]></kwd>
<kwd lng="en"><![CDATA[Taguchi]]></kwd>
<kwd lng="en"><![CDATA[fractions]]></kwd>
</kwd-group>
</article-meta>
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