{"id":41490,"date":"2023-01-18T12:06:14","date_gmt":"2023-01-18T11:06:14","guid":{"rendered":"https:\/\/www.he-arc.ch\/?post_type=he-arc_project&#038;p=41490"},"modified":"2023-01-18T12:19:46","modified_gmt":"2023-01-18T11:19:46","slug":"thermomac","status":"publish","type":"he-arc_project","link":"https:\/\/www.he-arc.ch\/en\/projets-recherche\/thermomac\/","title":{"rendered":"TherMoMac"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\">Contexte et enjeux<\/h2>\n\n\n\n<p>Le but principal de ce projet est de r\u00e9duire fortement la phase de pr\u00e9chauffe des machines-outils en \u00ab \u00e9quipant \u00bb ces machines avec un syst\u00e8me cyber-physique capable de pr\u00e9dire en temps r\u00e9el l\u2019erreur thermique et d\u2019estimer la compensation thermique correspondante. <\/p>\n\n\n\n<p>L\u2019erreur thermique est la d\u00e9viation spatiale, caus\u00e9e par des ph\u00e9nom\u00e8nes thermiques, des outils d\u2019enl\u00e8vement de copeaux par rapport \u00e0 leurs positions th\u00e9oriques. La compensation thermique repr\u00e9sente la correction de positions spatiales des outils concern\u00e9s afin d\u2019annuler l\u2019effet de cette erreur thermique. Ceci permet de :<\/p>\n\n\n\n<ul>\n<li>Garantir la pr\u00e9cision d\u2019usinage en toute situation thermique de la machine<\/li>\n\n\n\n<li>Fortement r\u00e9duire, voire supprimer, la phase de pr\u00e9chauffe<\/li>\n\n\n\n<li>Fortement r\u00e9duire le taux de rebut<\/li>\n\n\n\n<li>R\u00e9duire l\u2019impact \u00e9cologique des machines par l\u2019augmentation de la productivit\u00e9 et la suppression des rebuts.<\/li>\n\n\n\n<li>Retour aux fabricants sur le comportement thermom\u00e9canique des machines et adaptation du design des machines futures<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Objectifs<\/h2>\n\n\n\n<p>Le projet vise \u00e0 concevoir, d\u00e9velopper et valider un syst\u00e8me cyber-physique permettant la correction automatique et en temps r\u00e9el de l\u2019erreur thermique. <\/p>\n\n\n\n<p>Concr\u00e8tement les objectifs suivants sont vis\u00e9s&nbsp;:<\/p>\n\n\n\n<ul>\n<li>Mettre au point une m\u00e9thodologie d\u2019analyse et de compr\u00e9hension du comportement thermique des machines. Il s\u2019agit d\u2019un c\u00f4t\u00e9 d\u2019identifier et de caract\u00e9riser les sources thermiques de la machine-outil cible.<\/li>\n\n\n\n<li>Mettre en place une m\u00e9thodologie et syst\u00e8me de mesures du comportement thermique ainsi que de l\u2019impact de ce comportement sur la qualit\u00e9 et la pr\u00e9cision d\u2019usinage. L\u2019objectif est de concevoir, r\u00e9aliser et valider un syst\u00e8me de mesures permettant de relever en temps r\u00e9els les indicateurs pertinents \u00e0 la mod\u00e9lisation et \u00e0 la pr\u00e9diction de l\u2019erreur thermique.<\/li>\n\n\n\n<li>Elaborer des mod\u00e8les thermom\u00e9caniques. Il s\u2019agit ici d\u2019\u00e9tablir les mod\u00e8les qui d\u00e9crivent la d\u00e9formation des composants critiques du point de vue d\u2019usinage en fonction des variations thermiques \u00e0 l\u2019int\u00e9rieur de la machine en se basant sur des techniques d\u2019\u00e9l\u00e9ments finis. Ces mod\u00e8les permettent par la suite de d\u00e9duire la matrice de correction thermique.&nbsp; Une campagne d\u2019exp\u00e9riences\/mesures nous a permet de valider et d\u2019affiner les mod\u00e8les.<\/li>\n\n\n\n<li>Mettre au point des mod\u00e8les d\u2019apprentissage automatique de l\u2019erreur thermique. Une m\u00e9thode bas\u00e9e sur l\u2019apprentissage par r\u00e9seau de neurones (Machine Learning)) permet de compl\u00e9ter le mod\u00e8le formel \u00e9tabli ci-dessus.<\/li>\n\n\n\n<li>Impl\u00e9menter l\u2019int\u00e9gration du logiciel en se basant sur des solutions IoT.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Partenaires et financement<\/h2>\n\n\n\n<p><a href=\"https:\/\/heig-vd.ch\/\" target=\"_blank\" rel=\"noreferrer noopener\">HEPIA<\/a><\/p>\n\n\n\n<p><a href=\"https:\/\/www.tornos.com\" target=\"_blank\" rel=\"noreferrer noopener\">Tornos<\/a><\/p>\n\n\n\n<p>Financ\u00e9 par la <a href=\"https:\/\/www.hes-so.ch\/\" target=\"_blank\" rel=\"noreferrer noopener\">HES-SO<\/a> (programme th\u00e9matique Industrie 4.0)<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">R\u00e9sultats<\/h2>\n\n\n\n<p>Le projet a permis d\u2019\u00e9tablir une m\u00e9thodologie de mod\u00e9lisation de la d\u00e9viation thermique en se basant sur des donn\u00e9es d\u2019exp\u00e9riences. Des outils et des framework ont \u00e9t\u00e9 d\u00e9velopp\u00e9s pour mettre en \u0153uvre cette m\u00e9thodologie. Le sch\u00e9ma ci-apr\u00e8s illustre la m\u00e9thodologie et les outils associ\u00e9s pour la mod\u00e9lisation et la pr\u00e9diction de la d\u00e9viation thermique.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"945\" height=\"556\" src=\"https:\/\/www.he-arc.ch\/wp-content\/uploads\/2023\/01\/he-arc-ingenierie-thermomac1.png\" alt=\"\" class=\"wp-image-41469\" srcset=\"https:\/\/www.he-arc.ch\/wp-content\/uploads\/2023\/01\/he-arc-ingenierie-thermomac1.png 945w, https:\/\/www.he-arc.ch\/wp-content\/uploads\/2023\/01\/he-arc-ingenierie-thermomac1-300x177.png 300w, https:\/\/www.he-arc.ch\/wp-content\/uploads\/2023\/01\/he-arc-ingenierie-thermomac1-768x452.png 768w\" sizes=\"(max-width: 945px) 100vw, 945px\" \/><figcaption class=\"wp-element-caption\"><em>Architecture de la solution TherMoMac<\/em><\/figcaption><\/figure>\n\n\n\n<p>Les mod\u00e8les d\u2019apprentissage automatique utilis\u00e9s pour la pr\u00e9diction de l\u2019erreur thermique fournissent des performances tr\u00e8s encourageants&nbsp; sur une d\u00e9colleteuse Tornos SwisNano4 (voir figure ci-dessous).<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" width=\"945\" height=\"406\" src=\"https:\/\/www.he-arc.ch\/wp-content\/uploads\/2023\/01\/he-arc-ingenierie-thermomac2.png\" alt=\"\" class=\"wp-image-41471\" srcset=\"https:\/\/www.he-arc.ch\/wp-content\/uploads\/2023\/01\/he-arc-ingenierie-thermomac2.png 945w, https:\/\/www.he-arc.ch\/wp-content\/uploads\/2023\/01\/he-arc-ingenierie-thermomac2-300x129.png 300w, https:\/\/www.he-arc.ch\/wp-content\/uploads\/2023\/01\/he-arc-ingenierie-thermomac2-768x330.png 768w\" sizes=\"(max-width: 945px) 100vw, 945px\" \/><figcaption class=\"wp-element-caption\">Pr\u00e9diction de la d\u00e9viation thermique (en orange) en utilisant le Machine Learning versus courbe de la mesure r\u00e9elle (en bleu). En rouge en bas, a) la d\u00e9viation thermique maximale pr\u00e9dite et mesur\u00e9e; b) le coefficient de corr\u00e9lation entre pr\u00e9diction et mesure et c) le Mean Absolut Error (MAE)<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">Valorisation<\/h2>\n\n\n\n<ul>\n<li>Aicha Rizzotti, Nabil Ouerhani, Patrick Haas, <em>TherMoMac \u2013 Data-Driven Thermal Behavior Modelling of Machine-Tools<\/em>,&nbsp; F&amp;E-KONFERENZ ZU INDUSTRIE 4.0, ETH Zurich, January 2020<\/li>\n\n\n\n<li>Nabil Ouerhani, Projet <em>TherMoMac<\/em>, Journ\u00e9e de recherche IA, 2018 (Fribourg)<\/li>\n\n\n\n<li>N. Ouerhani, A. Rizzotti, Bernard Loehr, D. Santos De Pinho, Ph. Schindelholz, <em>Cyber-Physical System for Data-Driven Modelling and Prediction of Thermal Deviation in Turning Machine-Tools<\/em>, 4th International Conference on Industrial Automation, Robotics and Control Engineering (IARCE 2020), 2020<\/li>\n\n\n\n<li>D. Santos De Pinho, P. Neuenschwander, N. Ouerhani, <em>Process Parameters Optimization for Energy Efficiency in Swiss-Type Machining<\/em>, International Conference on Industrial Automation, Robotics and Control Engineering (IARCE 2019), Amsterdam, 2019<\/li>\n\n\n\n<li>A. Gay Des Combes, P. Muller, P. Neuenschwander, N. Pazos, N. Ouerhani, <em>WirelessHART-based Sensor Network for Multichannel Measurement of machine-tool Energy Consumption in Production Environments<\/em>, International Conference on Industrial Automation, Robotics and Control Engineering (IARCE 2019), Amsterdam, 2019<\/li>\n\n\n\n<li>N. Ouerhani, B. Loehr,A. Rizzotti, A. Limat, D. Santos De Pinho, Ph. Schindelholz, <em>Tool Position Measurement Methods for Data-Driven Thermal Error Compensation in&nbsp; High Precision Turning Machine-Tools,<\/em> International Conference on Industrial Automation, Robotics and Control Engineering (IARCE 2019), 2019<\/li>\n\n\n\n<li>Nabil Ouerhani, Bernard Loehr, A\u00efcha Rizzotti-Kadouri, Dylan Santo De Pinho, Adrien Limat and Philippe Schindelholz, <em>Data-Driven Thermal Deviation Prediction in Turning Machine-Tool &#8211; A Comparative Analysis of Machine Learning Algorithms<\/em>, International Conference on Industry 4.0 and Smart Manufacturing (ISM) 2021<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Cheffe de projet<\/h2>\n\n\n\n<div id=\"contact-card-block_619646874bb40\" class=\"contact-card is-shadowed mb-2-5 is-not-contained\">\n  <div class=\"contact-card__inner\">\n                <div class=\"contact-entry mb-1\">\n        Dr Nabil Ouerhani\n      <\/div>\n                      <div class=\"contact-entry mb-1\">\n        <a href=\"tel:+41329302208\"  class=\"simple-link contact-entry mb-1\">+41 32 930 22 08<\/a>\n      <\/div>\n                      <div class=\"contact-entry \">\n        <a href=\"mailto:nabil.ouerhani@he-arc.ch\"  class=\"simple-link contact-entry mb-1\">Nabil.Ouerhani@he-arc.ch<\/a>\n      <\/div>\n            <\/div>\n<\/div>\n","protected":false},"author":6,"featured_media":41469,"template":"","he-arc_domain":[3],"he-arc_project_skill":[67],"he-arc_project_year":[41,42,43],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.13 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>TherMoMac - Haute-Ecole Arc<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.he-arc.ch\/en\/projets-recherche\/thermomac\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"TherMoMac - Haute-Ecole Arc\" \/>\n<meta property=\"og:description\" content=\"Contexte et enjeux Le but principal de ce projet est de r\u00e9duire fortement la phase de pr\u00e9chauffe des machines-outils en \u00ab \u00e9quipant \u00bb ces machines avec un syst\u00e8me cyber-physique capable de pr\u00e9dire en temps r\u00e9el l\u2019erreur thermique et d\u2019estimer la compensation thermique correspondante. 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