<?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>2304-0106</journal-id>
<journal-title><![CDATA[Anales de la Academia de Ciencias de Cuba]]></journal-title>
<abbrev-journal-title><![CDATA[Anales de la ACC]]></abbrev-journal-title>
<issn>2304-0106</issn>
<publisher>
<publisher-name><![CDATA[Academia de Ciencias de Cuba]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2304-01062023000300009</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Herramientas inteligentes para el acercamiento de los procesos de fabricación mecánica al paradigma de Industria 4.0]]></article-title>
<article-title xml:lang="en"><![CDATA[Smart tools for bringing mechanical manufacturing processes closer to the Industry 4.0 paradigm]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Quiza Sardiñas]]></surname>
<given-names><![CDATA[Ramón]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Haber Guerra]]></surname>
<given-names><![CDATA[Rodolfo Elías]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rivas Santana]]></surname>
<given-names><![CDATA[Marcelino]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Beruvides López]]></surname>
<given-names><![CDATA[Gerardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Villalonga Jaén]]></surname>
<given-names><![CDATA[Alberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[La Fé Perdomo]]></surname>
<given-names><![CDATA[Iván]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cruz Hernández]]></surname>
<given-names><![CDATA[Yarens Joaquín]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez Rodríguez]]></surname>
<given-names><![CDATA[Roberto]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[del Risco Alfonso]]></surname>
<given-names><![CDATA[Ricardo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de Matanzas  ]]></institution>
<addr-line><![CDATA[ Matanzas]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Centro de Automática y Robótica  ]]></institution>
<addr-line><![CDATA[ Madrid]]></addr-line>
<country>España</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad de Holguín  ]]></institution>
<addr-line><![CDATA[ Holguín]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad de Camagüey Ignacio Agramonte Loynaz  ]]></institution>
<addr-line><![CDATA[ Camagüey]]></addr-line>
<country>Cuba</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Academia de Ciencias de Cuba  ]]></institution>
<addr-line><![CDATA[ La Habana]]></addr-line>
<country>Cuba</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2023</year>
</pub-date>
<volume>13</volume>
<numero>3</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2304-01062023000300009&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2304-01062023000300009&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2304-01062023000300009&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN  Introducción:  La inteligencia artificial es un componente básico de la llamada Industria 4.0, por lo que su introducción en los procesos tecnológicos constituye una contribución significativa a su acercamiento a este paradigma. El presente trabajo tiene como objetivo desarrollar y validar un grupo de tecnologías basadas en herramientas de computación blanda aplicadas a procesos de fabricación mecánica, con el propósito de acercarlos al paradigma de Industria 4.0.  Métodos:  Dentro de las herramientas desarrolladas, puestas a punto y aplicadas, se incluyeron tanto técnicas de aprendizaje automático, tales como las redes neuronales artificiales, los sistemas borrosos y el aprendizaje profundo para tareas de modelado y detección de patrones, como de metaheurísticas inspiradas en la naturaleza para optimización. Las aplicaciones han estado dirigidas a procesos de fabricación, tales como la soldadura, el maquinado y el micromaquinado, resolviendo problemas como la detención de fallos, el control de calidad y la optimización de procesos en función de su sostenibilidad.  Resultados:  En todos los casos de estudio considerados, las técnicas aplicadas mostraron ser plenamente efectivas para la solución de los problemas planteados, tanto los enfocados a la modelación y el reconocimiento de patrones, como aquellos dirigidos a la optimización de procesos. Además de ser ampliamente validados en condiciones de laboratorio similares a las industriales, las tecnologías desarrolladas fueron introducidas en la práctica productiva de recipientes de gas licuado, demostrando impactos favorables tanto en lo económico (disminución en los costos de producción) como en lo ambiental (menores consumos de energía en el proceso de producción). Conclusiones: La efectividad mostrada por las herramientas de inteligencia artificial aplicadas a la solución de problemas prácticos de los procesos de manufactura, constituyen un valioso conjunto de saber hacer, con amplias posibilidades de ser aplicados a diversos sectores de la industria, permitiendo su incorporación a soluciones de Industria 4.0, como los gemelos digitales y los sistemas ciberfísicos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT  Introduction:  Artificial intelligence is a basic component of the so-called Industry 4.0, so its introduction into technological processes constitutes a significant contribution to its approach to this paradigm. The objective of this work is to develop and validate a group of technologies based on soft computing tools applied to mechanical manufacturing processes, with the purpose of bringing them closer to the Industry 4.0 paradigm.  Methods:  Among the tools developed, fine-tuned, and applied, both machine learning techniques were included, such as artificial neural networks, fuzzy systems, and deep learning for modeling and pattern detection tasks, as well as metaheuristics inspired by nature. for optimization. The applications have been aimed at manufacturing processes, such as welding, machining and micromachining, solving problems such as failure detection, quality control and process optimization based on their sustainability.  Results:  In all the case studies considered, the applied techniques proved to be fully effective for solving the problems posed, both those focused on modeling and pattern recognition, as well as those aimed at optimizing processes. In addition to being widely validated in laboratory conditions similar to industrial ones, the technologies developed were introduced into the productive practice of liquefied gas containers, demonstrating favorable impacts both economically (decreased production costs) and environmental (lower energy consumption in the production process). Conclusions: The effectiveness shown by the artificial intelligence tools applied to the solution of practical problems of manufacturing processes, constitute a valuable set of know-how, with wide possibilities of being applied to various sectors of the industry, allowing their incorporation into solutions. of Industry 4.0, such as digital twins and cyber-physical systems.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[procesos de fabricación]]></kwd>
<kwd lng="es"><![CDATA[inteligencia artificial]]></kwd>
<kwd lng="es"><![CDATA[Industria 4.0]]></kwd>
<kwd lng="es"><![CDATA[modelación]]></kwd>
<kwd lng="es"><![CDATA[optimización.]]></kwd>
<kwd lng="en"><![CDATA[manufacturing processes]]></kwd>
<kwd lng="en"><![CDATA[artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[Industry 4.0]]></kwd>
<kwd lng="en"><![CDATA[modelling]]></kwd>
<kwd lng="en"><![CDATA[optimization.]]></kwd>
</kwd-group>
</article-meta>
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