<?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>0121-750X</journal-id>
<journal-title><![CDATA[Ingeniería]]></journal-title>
<abbrev-journal-title><![CDATA[ing.]]></abbrev-journal-title>
<issn>0121-750X</issn>
<publisher>
<publisher-name><![CDATA[Universidad Distrital Francisco José de Caldas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-750X2023000100204</article-id>
<article-id pub-id-type="doi">10.14483/23448393.18934</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Aplicación de Deep Learning para la identificación de defectos superficiales utilizados en control de calidad de manufactura y producción industrial: una revisión de la literatura]]></article-title>
<article-title xml:lang="en"><![CDATA[Application of Deep Learning for the Identification of Surface Defects Used in Manufacturing Quality Control and Industrial Production: A Literature Review]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Aparicio Pico]]></surname>
<given-names><![CDATA[Lilia Edith]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Amaya Marroquín]]></surname>
<given-names><![CDATA[Oscar Julián]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Devia Lozano]]></surname>
<given-names><![CDATA[Paola Andrea]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Distrital Francisco José de Caldas Facultad de Ingeniería ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Distrital Francisco José de Caldas Facultad de Ingeniería ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Distrital Francisco José de Caldas Facultad de Ingeniería ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2023</year>
</pub-date>
<volume>28</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-750X2023000100204&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-750X2023000100204&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-750X2023000100204&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Contexto:  Este artículo contiene un análisis de las aplicaciones de las distintas técnicas de Deep Learning y Machine Learning utilizadas en un gran rango de industrias para garantizar el control de la calidad en productos terminados mediante la identificación de los defectos superficiales.  Métodos:  Se desarrolló una revisión sistemática de las tendencias y las aplicaciones de Deep Learning en procesos de calidad. Tras consultar varias bases de datos, se filtraron y clasificaron los artículos por industria y técnica específica de trabajo aplicada para su posterior análisis de utilidad y funcionamiento.  Resultados:  Los resultados muestran por medio de casos de éxito la adaptabilidad y el potencial de aplicabilidad de esta técnica de inteligencia artificial a casi cualquier etapa de proceso de cualquier producto, esto debido al manejo de técnicas complementarias que se ajustan a las diferentes particularidades de los datos, los procesos de producción y los requerimientos de calidad.  Conclusiones:  El Deep Learning, en complemento con técnicas como Machine Learning o Transfer Learning, genera herramientas automatizadas, precisas y confiables para controlar la calidad de producción de todas las industrias.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Context:  This article contains an analysis of the applications of different Deep Learning and Machine Learning techniques used in a wide rangen of industries to ensure quality control in finished products through the identification of surface defects.  Method:  A systematic review of the trends and applications of Deep Learning in quality processes carried out. After consulting several databases, the articles were filtered and classified by industry and specific work technique applied to later analyze their usefulness and performance.  Results:  The results show by means of success cases the adaptability and potential applicability of this artificial intelligence technique to almost any process stage of any product, due to the handling of complementary techniques that adjust to the different particularities of the data, production processes, and quality requirements.  Conclusions:  Deep Learning, complemented with techniques such as Machine Learning or Transfer Learning, generates automated, accurate, and reliable tools to control the quality of production in all industries.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[control de calidad en producción]]></kwd>
<kwd lng="es"><![CDATA[Deep Learning]]></kwd>
<kwd lng="es"><![CDATA[defectos superficiales]]></kwd>
<kwd lng="es"><![CDATA[Machine Learning.]]></kwd>
<kwd lng="en"><![CDATA[production quality control]]></kwd>
<kwd lng="en"><![CDATA[deep learning]]></kwd>
<kwd lng="en"><![CDATA[surface defects]]></kwd>
<kwd lng="en"><![CDATA[machine learning.]]></kwd>
</kwd-group>
</article-meta>
</front><back>
<ref-list>
<ref id="B1">
<label>[1]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pastor-López]]></surname>
<given-names><![CDATA[I.]]></given-names>
</name>
</person-group>
<source><![CDATA[&#8220;Machine-learning-based surface defect detection and categorisation in high-precision foundry&#8221;]]></source>
<year>2012</year>
<conf-name><![CDATA[ 2012 7th IEEE Conference on Industrial Electronics and Applications (ICIEA)]]></conf-name>
<conf-loc> </conf-loc>
<page-range>325-32</page-range></nlm-citation>
</ref>
<ref id="B2">
<label>[2]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rodríguez González]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
</person-group>
<source><![CDATA[Sistema automatizado de detección de defectos en piezas metálicas mediante ensayos no destructivos con ultrasonidos]]></source>
<year>2012</year>
<publisher-name><![CDATA[Universidad de Cantabria]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B3">
<label>[3]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yang]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Using deep learning to detect defects in manufacturing: a comprehensive survey and current challenges&#8221;]]></article-title>
<source><![CDATA[Materials]]></source>
<year>2020</year>
<volume>13</volume>
<numero>24</numero>
<issue>24</issue>
</nlm-citation>
</ref>
<ref id="B4">
<label>[4]</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Martínez-Soriano]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Solana-González]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Vanti]]></surname>
<given-names><![CDATA[A. A.]]></given-names>
</name>
</person-group>
<source><![CDATA[&#8220;Analítica de datos aplicada a los costes de no calidad en procesos productivos&#8221;]]></source>
<year>2020</year>
</nlm-citation>
</ref>
<ref id="B5">
<label>[5]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Monitoring of assembly process using deep learning technology&#8221;]]></article-title>
<source><![CDATA[Sensors]]></source>
<year>2020</year>
<volume>20</volume>
<numero>15</numero>
<issue>15</issue>
</nlm-citation>
</ref>
<ref id="B6">
<label>[6]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lokrantz]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
<name>
<surname><![CDATA[Gustavsson]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
<name>
<surname><![CDATA[Jirstrand]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Root cause analysis of failures and quality deviations in manufacturing using machine learning&#8221;]]></article-title>
<source><![CDATA[Procedia CIRP]]></source>
<year>2018</year>
<volume>72</volume>
<page-range>1057-62</page-range></nlm-citation>
</ref>
<ref id="B7">
<label>[7]</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ri-Xian]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Ming-Hai]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Xian-Bao]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
</person-group>
<source><![CDATA[&#8220;Defects detection based on deep learning and transfer learning&#8221;]]></source>
<year>2015</year>
</nlm-citation>
</ref>
<ref id="B8">
<label>[8]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mula Cruz]]></surname>
<given-names><![CDATA[F. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Conesa Pastor]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<source><![CDATA[&#8220;Aplicación de sistemas inteligentes al control de calidad de la producción de piezas en serie mediante la reconstrucción de imágenes&#8221;]]></source>
<year>2020</year>
<conf-name><![CDATA[ XXIV Congreso Internacional de Dirección e Ingeniería de Proyectos]]></conf-name>
<conf-loc> </conf-loc>
</nlm-citation>
</ref>
<ref id="B9">
<label>[9]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Carvalho]]></surname>
<given-names><![CDATA[T. P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;A systematic literature review of machine learning methods applied to predictive maintenance&#8221;]]></article-title>
<source><![CDATA[Comput. Ind. Eng.]]></source>
<year>2019</year>
<volume>137</volume>
</nlm-citation>
</ref>
<ref id="B10">
<label>[10]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Herrero Moretón]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Estudio de las aplicaciones de Machine Learning y Deep Learning en el ámbito de la logística y la fabricación]]></source>
<year>2019</year>
<publisher-name><![CDATA[Universidad de Valladolid]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B11">
<label>[11]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Peres]]></surname>
<given-names><![CDATA[R. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Barata]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Leitao]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Garcia]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Multistage quality control using machine learning in the automotive industry&#8221;]]></article-title>
<source><![CDATA[IEEE Access]]></source>
<year>2019</year>
<volume>7</volume>
<page-range>79908-16</page-range></nlm-citation>
</ref>
<ref id="B12">
<label>[12]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Kang]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Catal]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Tekinerdogan]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Machine learning applications in production lines: A systematic literature review&#8221;]]></article-title>
<source><![CDATA[Comput. Ind. En]]></source>
<year>2020</year>
<volume>149</volume>
</nlm-citation>
</ref>
<ref id="B13">
<label>[13]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Roggo]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Deep learning for continuous manufacturing of pharmaceutical solid dosage form&#8221;]]></article-title>
<source><![CDATA[Eur J Pharm Biopharm]]></source>
<year>2020</year>
<volume>153</volume>
<page-range>95-105</page-range></nlm-citation>
</ref>
<ref id="B14">
<label>[14]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Morimoto]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Purwanto]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
<name>
<surname><![CDATA[Suzuki]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Hashimoto]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Optimization of heat treatment for fruit during storage using neural networks and genetic algorithms&#8221;]]></article-title>
<source><![CDATA[Comput. Electron. Agric.]]></source>
<year>1997</year>
<volume>19</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>87-101</page-range></nlm-citation>
</ref>
<ref id="B15">
<label>[15]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Nakano]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Application of neural networks to the color grading of apples&#8221;]]></article-title>
<source><![CDATA[Comput. Electron. Agric]]></source>
<year>1997</year>
<volume>18</volume>
<numero>2-3</numero>
<issue>2-3</issue>
<page-range>105-16</page-range></nlm-citation>
</ref>
<ref id="B16">
<label>[16]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Lin]]></surname>
<given-names><![CDATA[W. C.]]></given-names>
</name>
<name>
<surname><![CDATA[Hill]]></surname>
<given-names><![CDATA[B. D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Neural network modelling of fruit colour and crop variables to predict harvest dates of greenhouse-grown sweet peppers&#8221;]]></article-title>
<source><![CDATA[Can. J. Plant Sci]]></source>
<year>2007</year>
<volume>87</volume>
<numero>1</numero>
<issue>1</issue>
</nlm-citation>
</ref>
<ref id="B17">
<label>[17]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Contreras Ayala]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<source><![CDATA[Procesamiento de imágenes de fruto de palma de aceite mediante técnicas de Machine Learning para la clasificación de fruto y prediccion de la calidad de aceite de palma]]></source>
<year>2018</year>
<publisher-name><![CDATA[Universidad de los Andes]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B18">
<label>[18]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Medina Tobón]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<source><![CDATA[Conteo de flores y frutos para el monitoreo del cultivo de aguacate Hass por medio de imágenes utilizando Machine Learning]]></source>
<year>2021</year>
<publisher-name><![CDATA[Universidad de los Andes]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B19">
<label>[19]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pallares]]></surname>
<given-names><![CDATA[C. J.]]></given-names>
</name>
<name>
<surname><![CDATA[Lallemand]]></surname>
<given-names><![CDATA[K. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Visbal]]></surname>
<given-names><![CDATA[F. D.]]></given-names>
</name>
</person-group>
<source><![CDATA[Control preventivo de sigatoka negra en cultivo banano apoyado en redes convolucionales]]></source>
<year></year>
<publisher-name><![CDATA[Universidad del Norte]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B20">
<label>[20]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Dawei]]></surname>
<given-names><![CDATA[W.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Recognition pest by image-based transfer learning&#8221;]]></article-title>
<source><![CDATA[J. Sci. Food Agric]]></source>
<year>2019</year>
<volume>99</volume>
<numero>10</numero>
<issue>10</issue>
<page-range>4524-31</page-range></nlm-citation>
</ref>
<ref id="B21">
<label>[21]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Escobar]]></surname>
<given-names><![CDATA[C. A.]]></given-names>
</name>
<name>
<surname><![CDATA[Morales-Menendez]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Machine learning techniques for quality control in high conformance manufacturing environment&#8221;]]></article-title>
<source><![CDATA[Adv. Mech. Eng]]></source>
<year>2018</year>
<volume>10</volume>
<numero>2</numero>
<issue>2</issue>
</nlm-citation>
</ref>
<ref id="B22">
<label>[22]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Muñoz Amaya]]></surname>
<given-names><![CDATA[O. A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Diseño de un sistema de visión artificial para el análisis de calidad y producción de rosas]]></source>
<year>2018</year>
<publisher-name><![CDATA[Universidad Pedagógica Nacional]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B23">
<label>[23]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alfatiyah]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Bastuti]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Kurnia]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Implementation of statistical quality control to reduce defects in Mabell Nugget products (Case study at PT. Petra Sejahtera Abadi)&#8221;]]></article-title>
<source><![CDATA[IOP Conf. Ser. Mater. Sci. Eng]]></source>
<year>2019</year>
<volume>852</volume>
</nlm-citation>
</ref>
<ref id="B24">
<label>[24]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mei]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Wen]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Automatic fabric defect detection with a multi-scale convolutional denoising autoencoder network model&#8221;]]></article-title>
<source><![CDATA[Sensors]]></source>
<year>2018</year>
<volume>18</volume>
<numero>4</numero>
<issue>4</issue>
</nlm-citation>
</ref>
<ref id="B25">
<label>[25]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mak]]></surname>
<given-names><![CDATA[K. L.]]></given-names>
</name>
<name>
<surname><![CDATA[Peng]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
<name>
<surname><![CDATA[Yiu]]></surname>
<given-names><![CDATA[K. F. C.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Fabric defect detection using morphological filters&#8221;]]></article-title>
<source><![CDATA[Image Vis. Comput]]></source>
<year>2009</year>
<volume>27</volume>
<numero>10</numero>
<issue>10</issue>
<page-range>1585-92</page-range></nlm-citation>
</ref>
<ref id="B26">
<label>[26]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hoyos Montes]]></surname>
<given-names><![CDATA[Y. A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Detección de defectos en fibras textiles utilizando algoritmos de Deep Learning]]></source>
<year>2020</year>
<publisher-name><![CDATA[Universidad de Antioquia]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B27">
<label>[27]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Molina]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Solanes]]></surname>
<given-names><![CDATA[J. E.]]></given-names>
</name>
<name>
<surname><![CDATA[Arnal]]></surname>
<given-names><![CDATA[L.]]></given-names>
</name>
<name>
<surname><![CDATA[Tornero]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;On the detection of defects on specular car body surfaces&#8221;]]></article-title>
<source><![CDATA[Robot. Comput. Integr. Manuf]]></source>
<year>2017</year>
<volume>48</volume>
<page-range>263-78</page-range></nlm-citation>
</ref>
<ref id="B28">
<label>[28]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Yue]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Wang]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Chen]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Cai]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Jin]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Automatic detection of feather defects using Lie group and fuzzy Fisher criterion for shuttlecock production&#8221;]]></article-title>
<source><![CDATA[Mech. Syst. Signal Process]]></source>
<year>2020</year>
<volume>141</volume>
</nlm-citation>
</ref>
<ref id="B29">
<label>[29]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ko]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Machine learning-based anomaly detection via integration of manufacturing, inspection and after-sales service data&#8221;]]></article-title>
<source><![CDATA[Ind. Manag. Data Syst]]></source>
<year>2017</year>
<volume>117</volume>
<numero>5</numero>
<issue>5</issue>
<page-range>927-45</page-range></nlm-citation>
</ref>
<ref id="B30">
<label>[30]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Fong]]></surname>
<given-names><![CDATA[T. H. Y.]]></given-names>
</name>
</person-group>
<source><![CDATA[Identifying Product Defects by Applying a Predictive Model to Customer Reviews]]></source>
<year>2020</year>
<publisher-name><![CDATA[George Washington University]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B31">
<label>[31]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Krummenacher]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Ong]]></surname>
<given-names><![CDATA[C. S.]]></given-names>
</name>
<name>
<surname><![CDATA[Koller]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Kobayashi]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Buhmann]]></surname>
<given-names><![CDATA[J. M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Wheel defect detection with machine learning&#8221;]]></article-title>
<source><![CDATA[IEEE Trans. Intell. Transp. Syst]]></source>
<year>2018</year>
<volume>19</volume>
<numero>4</numero>
<issue>4</issue>
<page-range>1176-87</page-range></nlm-citation>
</ref>
<ref id="B32">
<label>[32]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[de la Calle Herrero]]></surname>
<given-names><![CDATA[F. J.]]></given-names>
</name>
</person-group>
<source><![CDATA[Inspección superficial de productos largos en tiempo real basada en visión por computador]]></source>
<year></year>
<publisher-name><![CDATA[Universidad de Oviedo]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B33">
<label>[33]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Caggiano]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Machine learning-based image processing for on-line defect recognition in additive manufacturing&#8221;]]></article-title>
<source><![CDATA[CIRP Ann.]]></source>
<year>2019</year>
<volume>68</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>451-4</page-range></nlm-citation>
</ref>
<ref id="B34">
<label>[34]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhang]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
<name>
<surname><![CDATA[Shi]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Wu]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Prediction of surface roughness in extrusion-based additive manufacturing with machine learning&#8221;]]></article-title>
<source><![CDATA[Robot. Comput. Integr. Manuf]]></source>
<year>2019</year>
<volume>57</volume>
<page-range>488-95</page-range></nlm-citation>
</ref>
<ref id="B35">
<label>[35]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Villalba-Diez]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Deep learning for industrial computer vision quality control in the printing industry 4.0&#8221;]]></article-title>
<source><![CDATA[Sensors]]></source>
<year>2019</year>
<volume>19</volume>
<numero>18</numero>
<issue>18</issue>
</nlm-citation>
</ref>
<ref id="B36">
<label>[36]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[He]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Xu]]></surname>
<given-names><![CDATA[K.]]></given-names>
</name>
<name>
<surname><![CDATA[Zhou]]></surname>
<given-names><![CDATA[P.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Defect detection of hot rolled steels with a new object detection framework called classification priority network&#8221;]]></article-title>
<source><![CDATA[Comput. Ind. Eng]]></source>
<year>2019</year>
<volume>128</volume>
<page-range>290-7</page-range></nlm-citation>
</ref>
<ref id="B37">
<label>[37]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
</person-group>
<source><![CDATA[&#8220;Ensemble machine learning systems for the estimation of steel quality control&#8221;]]></source>
<year>2019</year>
<conf-name><![CDATA[ 2018 IEEE International Conference on Big Data (Big Data)]]></conf-name>
<conf-loc> </conf-loc>
<page-range>2245-52</page-range></nlm-citation>
</ref>
<ref id="B38">
<label>[38]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Roncancio Valencia]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Gayubo Rojo]]></surname>
<given-names><![CDATA[F.]]></given-names>
</name>
<name>
<surname><![CDATA[Gómez García Bermejo]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Zalama Casanova]]></surname>
<given-names><![CDATA[E.]]></given-names>
</name>
</person-group>
<source><![CDATA[&#8220;Detección e identificación de defectos superficiales en diversas clases de chapa laminada mediante visión por computador y redes neuronales&#8221;]]></source>
<year>2007</year>
<publisher-name><![CDATA[XXVIII Jornadas de Automática, Huelva, Comité Español de Automática]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B39">
<label>[39]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Shanmugamani]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Sadique]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Ramamoorthy]]></surname>
<given-names><![CDATA[B.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Detection and classification of surface defects of gun barrels using computer vision and machine learning&#8221;]]></article-title>
<source><![CDATA[Measurement]]></source>
<year>2015</year>
<volume>60</volume>
<page-range>222-30</page-range></nlm-citation>
</ref>
<ref id="B40">
<label>[40]</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[García Peña]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
</person-group>
<source><![CDATA[Diseño e implementación de técnicas de Machine Learning para la detección de defectos superficiales en piezas sometidas a procesos de estampado o fundición]]></source>
<year>2021</year>
</nlm-citation>
</ref>
<ref id="B41">
<label>[41]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ferguson]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Ak]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Lee]]></surname>
<given-names><![CDATA[Y.-T. T.]]></given-names>
</name>
<name>
<surname><![CDATA[Law]]></surname>
<given-names><![CDATA[K. H.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Detection and segmentation of manufacturing defects with convolutional neural networks and transfer learning&#8221;]]></article-title>
<source><![CDATA[Smart Sustain. Manuf. Syst.]]></source>
<year>2018</year>
<volume>2</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>137-64</page-range></nlm-citation>
</ref>
<ref id="B42">
<label>[42]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Schorr]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Quality prediction of reamed bores based on process data and machine learning algorithm: A contribution to a more sustainable manufacturing&#8221;]]></article-title>
<source><![CDATA[Procedia Manuf]]></source>
<year>2020</year>
<volume>43</volume>
<page-range>519-26</page-range></nlm-citation>
</ref>
<ref id="B43">
<label>[43]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Rodríguez Collado]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Detección de defectos en tiempo real en una línea de fabricación de tableros mediante técnicas de reconocimiento de patrones]]></source>
<year>2019</year>
<publisher-name><![CDATA[Universidad de Valladolid]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B44">
<label>[44]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hoang]]></surname>
<given-names><![CDATA[N.-D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Image processing-based recognition of wall defects using machine learning approaches and steerable filters&#8221;]]></article-title>
<source><![CDATA[Comput. Intell. Neurosci]]></source>
<year>2018</year>
<volume>2018</volume>
</nlm-citation>
</ref>
<ref id="B45">
<label>[45]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Gong]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
<name>
<surname><![CDATA[Shao]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[Luo]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Li]]></surname>
<given-names><![CDATA[Z.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;A deep transfer learning model for inclusion defect detection of aeronautics composite materials&#8221;]]></article-title>
<source><![CDATA[Compos. Struct]]></source>
<year>2020</year>
<volume>252</volume>
</nlm-citation>
</ref>
<ref id="B46">
<label>[46]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pillai]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Using artificial intelligence to improve the quality and safety of radiation therapy&#8221;]]></article-title>
<source><![CDATA[J Am Coll Radiol]]></source>
<year>2019</year>
<volume>16</volume>
<numero>9</numero>
<issue>9</issue>
<page-range>1267-72</page-range></nlm-citation>
</ref>
<ref id="B47">
<label>[47]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Brito]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;A machine learning approach for collaborative robot smart manufacturing inspection for quality control systems&#8221;]]></article-title>
<source><![CDATA[Procedia Manuf]]></source>
<year>2020</year>
<volume>51</volume>
<page-range>11-8</page-range></nlm-citation>
</ref>
<ref id="B48">
<label>[48]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Ye]]></surname>
<given-names><![CDATA[R.]]></given-names>
</name>
<name>
<surname><![CDATA[Pan]]></surname>
<given-names><![CDATA[C.-S.]]></given-names>
</name>
<name>
<surname><![CDATA[Chang]]></surname>
<given-names><![CDATA[M.]]></given-names>
</name>
<name>
<surname><![CDATA[Yu]]></surname>
<given-names><![CDATA[Q.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Intelligent defect classification system based on deep learning&#8221;]]></article-title>
<source><![CDATA[Adv. Mech. Eng]]></source>
<year>2018</year>
<volume>10</volume>
<numero>3</numero>
<issue>3</issue>
</nlm-citation>
</ref>
<ref id="B49">
<label>[49]</label><nlm-citation citation-type="confpro">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Hanhirova]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Harjuhahto]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Harjuhahto]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
<name>
<surname><![CDATA[Hirvisalo]]></surname>
<given-names><![CDATA[V.]]></given-names>
</name>
</person-group>
<source><![CDATA[&#8220;A machine learning based quality control system for power cable manufacturing&#8221;]]></source>
<year></year>
<conf-name><![CDATA[ 2019 IEEE 17th International Conference on Industrial Informatics (INDIN)]]></conf-name>
<conf-loc> </conf-loc>
<page-range>193-8</page-range></nlm-citation>
</ref>
<ref id="B50">
<label>[50]</label><nlm-citation citation-type="">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Alonso]]></surname>
<given-names><![CDATA[G.]]></given-names>
</name>
<name>
<surname><![CDATA[Rodríguez]]></surname>
<given-names><![CDATA[Y.]]></given-names>
</name>
</person-group>
<source><![CDATA[&#8220;Calidad + IA, software basado en inteligencia artificial para la gestión de la calidad en la producción de habanos&#8221;]]></source>
<year>2009</year>
</nlm-citation>
</ref>
<ref id="B51">
<label>[51]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mehta]]></surname>
<given-names><![CDATA[S.]]></given-names>
</name>
<name>
<surname><![CDATA[Patnaik]]></surname>
<given-names><![CDATA[K. S.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Improved prediction of software defects using ensemble machine learning techniques&#8221;]]></article-title>
<source><![CDATA[Neural Comput. Appl]]></source>
<year>2021</year>
<volume>33</volume>
<page-range>10551-62</page-range></nlm-citation>
</ref>
<ref id="B52">
<label>[52]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Pachón Espinel]]></surname>
<given-names><![CDATA[D. L.]]></given-names>
</name>
</person-group>
<source><![CDATA[Prototipo de sistema automatizado con visión artificial para la selección de empaques de plástico, vidrio y lata en el proceso de reciclaje]]></source>
<year>2019</year>
<publisher-name><![CDATA[Universidad de Cundinamarca Extensión Chía]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B53">
<label>[53]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Mendieta Martínez]]></surname>
<given-names><![CDATA[R. D.]]></given-names>
</name>
<name>
<surname><![CDATA[Velandia]]></surname>
<given-names><![CDATA[H.]]></given-names>
</name>
<name>
<surname><![CDATA[González]]></surname>
<given-names><![CDATA[J.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Clasificación de microorganismos en muestras de agua aplicando Deep Learning en imágenes de microscopia&#8221;]]></article-title>
<source><![CDATA[REDSI]]></source>
<year>2019</year>
<volume>2</volume>
<numero>2</numero>
<issue>2</issue>
</nlm-citation>
</ref>
<ref id="B54">
<label>[54]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Huertas]]></surname>
<given-names><![CDATA[A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Algoritmos de aprendizaje supervisado utilizando datos de monitoreo de condiciones: un estudio para el pronóstico de fallas en máquinas]]></source>
<year>2020</year>
<publisher-name><![CDATA[Universidad Santo Tomás]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B55">
<label>[55]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Zamora Hernández]]></surname>
<given-names><![CDATA[M. A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Arquitectura para el control visual de ensamblajes en Industria 4.0 basado en aprendizaje profundo]]></source>
<year>2020</year>
<publisher-name><![CDATA[Universidad de Alicante]]></publisher-name>
</nlm-citation>
</ref>
<ref id="B56">
<label>[56]</label><nlm-citation citation-type="journal">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Wuest]]></surname>
<given-names><![CDATA[T.]]></given-names>
</name>
<name>
<surname><![CDATA[Weimer]]></surname>
<given-names><![CDATA[D.]]></given-names>
</name>
<name>
<surname><![CDATA[Irgens]]></surname>
<given-names><![CDATA[C.]]></given-names>
</name>
<name>
<surname><![CDATA[Thoben]]></surname>
<given-names><![CDATA[K.-D.]]></given-names>
</name>
</person-group>
<article-title xml:lang=""><![CDATA[&#8220;Machine learning in manufacturing: Advantages, challenges, and applications&#8221;]]></article-title>
<source><![CDATA[Prod. Manuf. Res.]]></source>
<year>2016</year>
<volume>4</volume>
<numero>1</numero>
<issue>1</issue>
<page-range>23-45</page-range></nlm-citation>
</ref>
<ref id="B57">
<label>[57]</label><nlm-citation citation-type="book">
<person-group person-group-type="author">
<name>
<surname><![CDATA[Velasquez Ponce]]></surname>
<given-names><![CDATA[C. A.]]></given-names>
</name>
</person-group>
<source><![CDATA[Geología y optimización del control de calidad con el uso del analizador de fluorescencia por rayos X (XRF) en Unidad Minera Cerro Lindo, Chincha - Ica]]></source>
<year>2019</year>
<publisher-name><![CDATA[Universidad Nacional de San Agustín de Arequipa]]></publisher-name>
</nlm-citation>
</ref>
</ref-list>
</back>
</article>
