{"id":57270,"date":"2026-03-23T10:08:55","date_gmt":"2026-03-23T09:08:55","guid":{"rendered":"https:\/\/www.nae.fr\/2026\/03\/23\/ai-optimized-design-of-hybrid-functionally-graded-metal-matrix-composites-for-additive-manufacturing-and-multifunctional-engineering-applications\/"},"modified":"2026-03-23T10:08:55","modified_gmt":"2026-03-23T09:08:55","slug":"ai-optimized-design-of-hybrid-functionally-graded-metal-matrix-composites-for-additive-manufacturing-and-multifunctional-engineering-applications","status":"publish","type":"post","link":"https:\/\/www.nae.fr\/en\/2026\/03\/23\/ai-optimized-design-of-hybrid-functionally-graded-metal-matrix-composites-for-additive-manufacturing-and-multifunctional-engineering-applications\/","title":{"rendered":"AI-optimized design of hybrid functionally graded metal matrix composites for additive manufacturing and multifunctional engineering applications"},"content":{"rendered":"
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\n\nThe optimization of functionally graded metal matrix composites (FGMMCs) has long been hindered by the complex interplay between reinforcement distribution, process constraints, and multifunctional performance objectives. Current data-driven and empirical frameworks like ANN-GA hybrids, CNN-DQN models and XGBoost regressors cannot guarantee physical validity, as well as generalizability, and, as a result, provide non-manufacturable or unstable predictions. To overcome these shortcomings, this study presents a Physics-Informed Neural Operator (PINO)-assisted inverse design workflow of the Al6061 hybrid composites that combines Principal Component Analysis (PCA) to reduce dimensionality of the gradient fields and Bayesian Optimization (BO) to optimize the performance of the material-synthesizing system.\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div><\/blockquote>\n
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\n\nPour en savoir plus : AI-optimized design of hybrid functionally graded metal matrix composites for additive manufacturing and multifunctional engineering applications<\/a>\n\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>","protected":false},"excerpt":{"rendered":"

The optimization of functionally graded metal matrix composites (FGMMCs) has long been hindered by the complex interplay between reinforcement distribution, process constraints, and multifunctional performance objectives. Current data-driven and empirical frameworks like ANN-GA hybrids, CNN-DQN models and XGBoost regressors cannot guarantee physical validity, as well as generalizability, and, as a result, provide non-manufacturable or unstable […]<\/p>\n","protected":false},"author":1,"featured_media":56547,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[34,16],"tags":[35,43,31,32],"class_list":["post-57270","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-innovation-et-technologique","category-rti","tag-actualites","tag-allegement-et-performances-des-structures","tag-composites","tag-fabrication-additive"],"acf":[],"yoast_head":"\nAI-optimized design of hybrid functionally graded metal matrix composites for additive manufacturing and multifunctional engineering applications - NAE<\/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.nae.fr\/en\/2026\/03\/23\/ai-optimized-design-of-hybrid-functionally-graded-metal-matrix-composites-for-additive-manufacturing-and-multifunctional-engineering-applications\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI-optimized design of hybrid functionally graded metal matrix composites for additive manufacturing and multifunctional engineering applications - NAE\" \/>\n<meta property=\"og:description\" content=\"The optimization of functionally graded metal matrix composites (FGMMCs) has long been hindered by the complex interplay between reinforcement distribution, process constraints, and multifunctional performance objectives. Current data-driven and empirical frameworks like ANN-GA hybrids, CNN-DQN models and XGBoost regressors cannot guarantee physical validity, as well as generalizability, and, as a result, provide non-manufacturable or unstable […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.nae.fr\/en\/2026\/03\/23\/ai-optimized-design-of-hybrid-functionally-graded-metal-matrix-composites-for-additive-manufacturing-and-multifunctional-engineering-applications\/\" \/>\n<meta property=\"og:site_name\" content=\"NAE\" \/>\n<meta property=\"article:published_time\" content=\"2026-03-23T09:08:55+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.nae.fr\/wp-content\/uploads\/2026\/06\/Logo-Springer-link.png\" \/>\n\t<meta property=\"og:image:width\" content=\"316\" \/>\n\t<meta property=\"og:image:height\" content=\"316\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"adminwa\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"adminwa\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/www.nae.fr\\\/2026\\\/03\\\/23\\\/ai-optimized-design-of-hybrid-functionally-graded-metal-matrix-composites-for-additive-manufacturing-and-multifunctional-engineering-applications\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/www.nae.fr\\\/2026\\\/03\\\/23\\\/ai-optimized-design-of-hybrid-functionally-graded-metal-matrix-composites-for-additive-manufacturing-and-multifunctional-engineering-applications\\\/\"},\"author\":{\"name\":\"adminwa\",\"@id\":\"https:\\\/\\\/www.nae.fr\\\/#\\\/schema\\\/person\\\/3d658e930f01449b7195ce4a78fcfc1e\"},\"headline\":\"AI-optimized design of hybrid functionally graded metal matrix composites for additive manufacturing and multifunctional engineering applications\",\"datePublished\":\"2026-03-23T09:08:55+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/www.nae.fr\\\/2026\\\/03\\\/23\\\/ai-optimized-design-of-hybrid-functionally-graded-metal-matrix-composites-for-additive-manufacturing-and-multifunctional-engineering-applications\\\/\"},\"wordCount\":138,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/www.nae.fr\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/www.nae.fr\\\/2026\\\/03\\\/23\\\/ai-optimized-design-of-hybrid-functionally-graded-metal-matrix-composites-for-additive-manufacturing-and-multifunctional-engineering-applications\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/www.nae.fr\\\/wp-content\\\/uploads\\\/2026\\\/06\\\/Logo-Springer-link.png\",\"keywords\":[\"Actualit\u00e9s\",\"All\u00e8gement et performances des structures\",\"Composites\",\"Fabrication Additive\"],\"articleSection\":[\"Innovation et technologique\",\"RTI\"],\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/www.nae.fr\\\/2026\\\/03\\\/23\\\/ai-optimized-design-of-hybrid-functionally-graded-metal-matrix-composites-for-additive-manufacturing-and-multifunctional-engineering-applications\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/www.nae.fr\\\/2026\\\/03\\\/23\\\/ai-optimized-design-of-hybrid-functionally-graded-metal-matrix-composites-for-additive-manufacturing-and-multifunctional-engineering-applications\\\/\",\"url\":\"https:\\\/\\\/www.nae.fr\\\/2026\\\/03\\\/23\\\/ai-optimized-design-of-hybrid-functionally-graded-metal-matrix-composites-for-additive-manufacturing-and-multifunctional-engineering-applications\\\/\",\"name\":\"AI-optimized design of hybrid functionally graded metal matrix composites for additive manufacturing and multifunctional engineering applications - 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