{"id":65026,"date":"2021-01-29T17:21:21","date_gmt":"2021-01-29T16:21:21","guid":{"rendered":"https:\/\/welding-alloys.zerobox.co.za\/uncategorized\/aprendizado-de-maquina-usado-para-alcancar-melhorias-drasticas-no-desempenho-de-consumiveis-de-soldas-de-revestimento-para-o-welding-alloys-group\/"},"modified":"2026-03-14T16:47:56","modified_gmt":"2026-03-14T15:47:56","slug":"machine-learning-used-to-achieve-drastic-improvement-in-performance-of-hardfacing-welding-consumables","status":"publish","type":"post","link":"https:\/\/welding-alloys.zerobox.co.za\/pt-br\/noticias\/machine-learning-used-to-achieve-drastic-improvement-in-performance-of-hardfacing-welding-consumables\/","title":{"rendered":"Aprendizado de m\u00e1quina utilizado para alcan\u00e7ar melhoria dr\u00e1stica no desempenho de consum\u00edveis de soldagem de revestimento duro para o Welding Alloys Group"},"content":{"rendered":"<div id='full_slider_1'  class='avia-fullwidth-slider main_color avia-shadow   avia-builder-el-0 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class=\"slideshow_align_caption\"><h2 class='avia-caption-title '  itemprop=\"name\" >O que est\u00e1 acontecendo na Welding Alloys<\/h2><\/div><\/div><\/div><\/div><\/div><img decoding=\"async\" fetchpriority=\"high\" class=\"wp-image-60698 avia-img-lazy-loading-not-60698\"  src=\"https:\/\/welding-alloys.zerobox.co.za\/wp-content\/uploads\/2022\/01\/news-banner.jpg\" width=\"1280\" height=\"315\" title='banner de not\u00edcias' alt='banner de not\u00edcias'  itemprop=\"thumbnailUrl\"  style='min-height:250px; min-width:1016px; ' srcset=\"https:\/\/welding-alloys.zerobox.co.za\/wp-content\/uploads\/2022\/01\/news-banner.jpg 1280w, https:\/\/welding-alloys.zerobox.co.za\/wp-content\/uploads\/2022\/01\/news-banner-600x148.jpg 600w, https:\/\/welding-alloys.zerobox.co.za\/wp-content\/uploads\/2022\/01\/news-banner-300x74.jpg 300w, https:\/\/welding-alloys.zerobox.co.za\/wp-content\/uploads\/2022\/01\/news-banner-500x123.jpg 500w\" sizes=\"(max-width: 1280px) 100vw, 1280px\" 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Autoclose: 1 --><div class='flex_column_table av-3ygkdgi-2a01ee6e1d8034ee44e7e299db467106 sc-av_one_full av-equal-height-column-flextable'>\n<style type=\"text\/css\" data-created_by=\"avia_inline_auto\" id=\"style-css-av-3ygkdgi-2a01ee6e1d8034ee44e7e299db467106\">\n#top .flex_column_table.av-equal-height-column-flextable.av-3ygkdgi-2a01ee6e1d8034ee44e7e299db467106{\nmargin-top:15px;\nmargin-bottom:0px;\n}\n<\/style>\n<div  class='flex_column av-3ygkdgi-2a01ee6e1d8034ee44e7e299db467106 av_one_full  avia-builder-el-7  el_after_av_one_full  el_before_av_one_full  first no_margin flex_column_table_cell av-equal-height-column av-align-top av-animated-generic left-to-right  column-top-margin'     ><section  class='av_textblock_section av-3nodr1e-f32f89b03a3fe42ed9d7bbed1f6440b0 '   itemscope=\"itemscope\" itemtype=\"https:\/\/schema.org\/BlogPosting\" itemprop=\"blogPost\" ><div class='avia_textblock'  itemprop=\"text\" ><p><strong>Resumo Executivo<\/strong><\/p>\n<p>O desgaste \u00e9 um dos problemas mais desafiadores enfrentados pela ind\u00fastria pesada. Por exemplo, somente na ind\u00fastria de minera\u00e7\u00e3o, aproximadamente 17% da energia consumida \u00e9 utilizada para combater falhas por desgaste, representando 2,7% das emiss\u00f5es globais de CO.  Se esses n\u00fameros forem multiplicados para todos os setores (constru\u00e7\u00e3o, petr\u00f3leo e g\u00e1s, etc.), a escala do problema torna-se evidente. Assim, evitar ou minimizar o desgaste \u00e9 uma prioridade m\u00e1xima na ind\u00fastria. <\/p>\n<p>Um dos m\u00e9todos mais comuns para combater o desgaste \u00e9 soldar consum\u00edveis altamente ligados (materiais de <a href=\"https:\/\/welding-alloys.zerobox.co.za\/pt-br\/application\/consumiveis-para-soldagem-de-revestimento-duro\/\">revestimento<\/a>) nas superf\u00edcies dos componentes. Esses consum\u00edveis devem atender a requisitos rigorosos de seguran\u00e7a, custo, impacto ambiental e desempenho. O desempenho ao desgaste \u00e9 determinado por uma intera\u00e7\u00e3o complexa de propriedades, portanto, otimizar a rela\u00e7\u00e3o custo\/benef\u00edcio para materiais de revestimento \u00e9 uma opera\u00e7\u00e3o altamente complexa.  <\/p>\n<p>Este estudo de caso descreve a jornada realizada pelo Welding Alloys Group (WAG) e pela Intellegens na aplica\u00e7\u00e3o de aprendizado de m\u00e1quina a esse problema, que resultou no desenvolvimento de um material de revestimento aprimorado com vantagens dr\u00e1sticas de custo\/benef\u00edcio, n\u00e3o apenas do ponto de vista de desempenho, mas tamb\u00e9m ambiental.<\/p>\n<p><strong>Desafio<\/strong><br \/>\nO desgaste \u00e9 um fen\u00f4meno muito complexo. A concep\u00e7\u00e3o comum de que alta dureza garante altas propriedades de resist\u00eancia ao desgaste \u00e9 enganosa. A resist\u00eancia ao desgaste ideal \u00e9 definida por uma intera\u00e7\u00e3o complexa de propriedades qu\u00edmicas e mec\u00e2nicas de cada material envolvido na aplica\u00e7\u00e3o. Essas propriedades incluem composi\u00e7\u00e3o, dureza, tenacidade, m\u00f3dulo de Young, tamanho de gr\u00e3o, composi\u00e7\u00e3o de fase, etc. Par\u00e2metros externos como temperatura, press\u00e3o e umidade tamb\u00e9m desempenham pap\u00e9is importantes.    <\/p>\n<p>Al\u00e9m disso, varia\u00e7\u00f5es aleat\u00f3rias e\/ou sistem\u00e1ticas em arames e procedimentos de soldagem de revestimento resultam em produtos acabados com grandes discrep\u00e2ncias no desempenho. Os custos ambientais devido ao uso excessivo de cromo (Cr) altamente poluente tamb\u00e9m s\u00e3o uma grande preocupa\u00e7\u00e3o, assim como as regulamenta\u00e7\u00f5es ambientais cada vez mais rigorosas que impulsionam consum\u00edveis de soldagem mais enxutos. <\/p>\n<p>O objetivo deste projeto foi pegar um consum\u00edvel de soldagem \u00e0 base de ferro fundido com alto teor de Cr e otimizar a rela\u00e7\u00e3o custo\/benef\u00edcio em fun\u00e7\u00e3o da composi\u00e7\u00e3o qu\u00edmica, com base em m\u00e9todos padr\u00e3o de resist\u00eancia \u00e0 abras\u00e3o como m\u00e9trica de desempenho, utilizando o kit de ferramentas de aprendizado de m\u00e1quina Alchemite\u2122.<\/p>\n<p><strong>Solu\u00e7\u00e3o<\/strong><\/p>\n<p>O Alchemite\u2122 Engine \u00e9 a ferramenta exclusiva de aprendizado profundo da Intellegens, que utiliza o poder das redes neurais profundas para construir modelos abrangentes em m\u00faltiplas composi\u00e7\u00f5es e propriedades de materiais a partir de dados experimentais esparsos. O modelo para materiais de revestimento foi constru\u00eddo com dados de composi\u00e7\u00e3o e propriedades f\u00edsicas da literatura, bem como dados hist\u00f3ricos do WAG. A perda de peso foi utilizada como medida de resist\u00eancia ao desgaste.  <\/p>\n<p><strong>Metodologia<\/strong><\/p>\n<p>Etapa 1: Constru\u00e7\u00e3o do modelo e projeto preliminar do material<strong><br \/>\n<\/strong>O Alchemite\u2122 definiu os par\u00e2metros mais influentes no desempenho e sugeriu a primeira rodada de composi\u00e7\u00f5es te\u00f3ricas. Os engenheiros do WAG confirmaram e aprovaram a fabrica\u00e7\u00e3o. <strong><br \/>\n<\/strong>Etapa 2: Valida\u00e7\u00e3o<strong><br \/>\n<\/strong>As formula\u00e7\u00f5es foram fabricadas, testadas e validadas em rela\u00e7\u00e3o aos valores previstos, mostrando concord\u00e2ncia aceit\u00e1vel e ficando dentro da incerteza calculada.<strong><br \/>\n<\/strong>Etapa 3: Refinamento<strong><br \/>\n<\/strong>Itera\u00e7\u00f5es adicionais foram realizadas permitindo que o modelo fosse refinado.<br \/>\nEtapa 4: Conclus\u00e3o da modelagem e trabalho de laborat\u00f3rio<br \/>\nUma nova formula\u00e7\u00e3o foi definida. Os testes de laborat\u00f3rio mostraram desempenho compar\u00e1vel aos produtos existentes, mas com redu\u00e7\u00e3o de elementos de liga de at\u00e9 50% e redu\u00e7\u00e3o de pre\u00e7o entre 10% e 15%. <\/p>\n<p class=\"text-left\">O Alchemite\u2122 prop\u00f4s um consum\u00edvel de revestimento aprimorado com vantagens dr\u00e1sticas de custo\/benef\u00edcio do ponto de vista de desempenho e ambiental<\/p>\n<p><strong>Principais resultados<\/strong><\/p>\n<ul>\n<li>As previs\u00f5es do Alchemite foram validadas experimentalmente. As composi\u00e7\u00f5es selecionadas para valida\u00e7\u00e3o diferiram consideravelmente dos materiais existentes. Os dados sobre esses novos materiais melhoraram significativamente o modelo.  <\/li>\n<li>O Alchemite\u2122 conseguiu propor uma alternativa aprimorada, mais econ\u00f4mica e mais ecol\u00f3gica ao material de revestimento atualmente utilizado.<\/li>\n<li>O WAG levou este material adiante e est\u00e1 agora realizando testes de desempenho em escala industrial.<\/li>\n<\/ul>\n<h6 class=\"text-center\">Jean-Marie Bonnel (Diretor T\u00e9cnico) e Mario Cordero (Gerente de Inova\u00e7\u00e3o e P&amp;D), l\u00edderes do projeto no WAG, concordam que: <strong>&#8220;A combina\u00e7\u00e3o de amplo conhecimento t\u00e9cnico e dados experimentais fornecidos pelo WAG, e os algoritmos exclusivos de aprendizado profundo fornecidos pela Intellegens, resultou em um material aprimorado com vantagens dr\u00e1sticas de custo\/benef\u00edcio.&#8221;<\/strong><\/h6>\n<p><strong>Oportunidades futuras<\/strong><br \/>\nEste resultado representa um passo significativo para o Welding Alloys Group. Demonstrou uma abordagem bem-sucedida para o uso de m\u00e9todos computacionais de ponta para a melhoria de consum\u00edveis de soldagem existentes e o projeto de novos. Esta abordagem est\u00e1 sendo continuada para outras linhas de consum\u00edveis de soldagem.  <\/p>\n<p><strong>Sobre o Welding Alloys Group<\/strong><br \/>\nO Welding Alloys Group \u00e9 o fornecedor de refer\u00eancia para consum\u00edveis de soldagem avan\u00e7ados, equipamentos automatizados para prote\u00e7\u00e3o contra desgaste e solu\u00e7\u00f5es de desgaste projetadas. O Welding Alloys Group est\u00e1 comprometido em formar relacionamentos pr\u00f3ximos com os clientes e estabeleceu in\u00fameras parcerias no setor como fornecedor de solu\u00e7\u00f5es totais &#8211; desde consum\u00edveis e m\u00e1quinas at\u00e9 solu\u00e7\u00f5es de engenharia integradas. <\/p>\n<p><strong>Sobre a Intellegens<\/strong><br \/>\nA Intellegens desenvolveu um motor de intelig\u00eancia artificial exclusivo, o Alchemite\u2122, para treinar redes neurais a partir de dados esparsos e ruidosos, t\u00edpicos de dados do mundo real. O Alchemite\u2122 \u00e9 a plataforma de aprendizado profundo para otimiza\u00e7\u00e3o de materiais e processos para: <\/p>\n<ul>\n<li>Otimizar formula\u00e7\u00f5es para desempenho, custo ou impacto ambiental<\/li>\n<li>Orientar experimentos, reduzindo em at\u00e9 80% o n\u00famero de testes necess\u00e1rios<\/li>\n<li>Validar e identificar valores at\u00edpicos e dados interessantes<\/li>\n<\/ul>\n<p>O Alchemite\u2122 pode ser licenciado como produto SaaS para cientistas, engenheiros e t\u00e9cnicos, ou para uso aut\u00f4nomo por equipes de an\u00e1lise de dados do cliente.<\/p>\n<p>Refer\u00eancias<br \/>\n[1] Global energy consumption due to friction and wear in the mining industry. Tribology<br \/>\nInternational. Volume 115, novembro de 2017, p\u00e1ginas 116-139<\/p>\n<\/div><\/section><\/div><\/div><!--close column table wrapper. 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Por exemplo, somente na ind\u00fastria de minera\u00e7\u00e3o, aproximadamente 17% da energia consumida \u00e9 utilizada para combater falhas por desgaste, representando 2,7% das emiss\u00f5es globais de CO. <\/p>\n","protected":false},"author":1,"featured_media":68322,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","inline_featured_image":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[24468],"tags":[],"technical-articles-filter":[],"class_list":["post-65026","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-noticias"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.7 (Yoast SEO v27.7) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Aprendizado de m\u00e1quina utilizado para alcan\u00e7ar melhoria dr\u00e1stica no desempenho de consum\u00edveis de soldagem de revestimento duro para o Welding Alloys Group | Welding Alloys<\/title>\n<meta name=\"description\" content=\"O desgaste \u00e9 um dos problemas mais desafiadores enfrentados pela ind\u00fastria pesada. Por exemplo, somente na ind\u00fastria de minera\u00e7\u00e3o, aproximadamente 17% da energia consumida \u00e9 utilizada para combater falhas por desgaste, representando 2,7% das emiss\u00f5es globais de CO.\" \/>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Aprendizado de m\u00e1quina utilizado para alcan\u00e7ar melhoria dr\u00e1stica no desempenho de consum\u00edveis de soldagem de revestimento duro para o Welding Alloys Group\" \/>\n<meta property=\"og:description\" content=\"O desgaste \u00e9 um dos problemas mais desafiadores enfrentados pela ind\u00fastria pesada. Por exemplo, somente na ind\u00fastria de minera\u00e7\u00e3o, aproximadamente 17% da energia consumida \u00e9 utilizada para combater falhas por desgaste, representando 2,7% das emiss\u00f5es globais de CO.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/welding-alloys.zerobox.co.za\/pt-br\/noticias\/machine-learning-used-to-achieve-drastic-improvement-in-performance-of-hardfacing-welding-consumables\/\" \/>\n<meta property=\"og:site_name\" content=\"Welding Alloys\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/weldingalloysgroup\" \/>\n<meta property=\"article:published_time\" content=\"2021-01-29T16:21:21+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-14T15:47:56+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/welding-alloys.zerobox.co.za\/wp-content\/uploads\/2022\/02\/WAG-CaseStudy.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"400\" \/>\n\t<meta property=\"og:image:height\" content=\"200\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"thoughtcorp\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Escrito por\" \/>\n\t<meta name=\"twitter:data1\" content=\"thoughtcorp\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. tempo de leitura\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutos\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/welding-alloys.zerobox.co.za\\\/pt-br\\\/noticias\\\/machine-learning-used-to-achieve-drastic-improvement-in-performance-of-hardfacing-welding-consumables\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/welding-alloys.zerobox.co.za\\\/pt-br\\\/noticias\\\/machine-learning-used-to-achieve-drastic-improvement-in-performance-of-hardfacing-welding-consumables\\\/\"},\"author\":{\"name\":\"thoughtcorp\",\"@id\":\"https:\\\/\\\/welding-alloys.zerobox.co.za\\\/pt-br\\\/#\\\/schema\\\/person\\\/d8f86a7cb9a08e431c9ca315ae84574e\"},\"headline\":\"Aprendizado de m\u00e1quina utilizado para alcan\u00e7ar melhoria dr\u00e1stica no desempenho de consum\u00edveis de soldagem de revestimento duro para o Welding Alloys Group\",\"datePublished\":\"2021-01-29T16:21:21+00:00\",\"dateModified\":\"2026-03-14T15:47:56+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/welding-alloys.zerobox.co.za\\\/pt-br\\\/noticias\\\/machine-learning-used-to-achieve-drastic-improvement-in-performance-of-hardfacing-welding-consumables\\\/\"},\"wordCount\":2960,\"publisher\":{\"@id\":\"https:\\\/\\\/welding-alloys.zerobox.co.za\\\/pt-br\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/welding-alloys.zerobox.co.za\\\/pt-br\\\/noticias\\\/machine-learning-used-to-achieve-drastic-improvement-in-performance-of-hardfacing-welding-consumables\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/welding-alloys.zerobox.co.za\\\/wp-content\\\/uploads\\\/2022\\\/02\\\/WAG-CaseStudy.jpg\",\"articleSection\":[\"Not\u00edcias\"],\"inLanguage\":\"pt-BR\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/welding-alloys.zerobox.co.za\\\/pt-br\\\/noticias\\\/machine-learning-used-to-achieve-drastic-improvement-in-performance-of-hardfacing-welding-consumables\\\/\",\"url\":\"https:\\\/\\\/welding-alloys.zerobox.co.za\\\/pt-br\\\/noticias\\\/machine-learning-used-to-achieve-drastic-improvement-in-performance-of-hardfacing-welding-consumables\\\/\",\"name\":\"Aprendizado de m\u00e1quina utilizado para alcan\u00e7ar melhoria dr\u00e1stica no desempenho de consum\u00edveis de soldagem de revestimento duro para o Welding Alloys Group | Welding Alloys\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/welding-alloys.zerobox.co.za\\\/pt-br\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/welding-alloys.zerobox.co.za\\\/pt-br\\\/noticias\\\/machine-learning-used-to-achieve-drastic-improvement-in-performance-of-hardfacing-welding-consumables\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/welding-alloys.zerobox.co.za\\\/pt-br\\\/noticias\\\/machine-learning-used-to-achieve-drastic-improvement-in-performance-of-hardfacing-welding-consumables\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/welding-alloys.zerobox.co.za\\\/wp-content\\\/uploads\\\/2022\\\/02\\\/WAG-CaseStudy.jpg\",\"datePublished\":\"2021-01-29T16:21:21+00:00\",\"dateModified\":\"2026-03-14T15:47:56+00:00\",\"description\":\"O desgaste \u00e9 um dos problemas mais desafiadores enfrentados pela ind\u00fastria pesada. 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