{"id":32311,"date":"2026-09-10T09:00:00","date_gmt":"2026-09-10T07:00:00","guid":{"rendered":"https:\/\/cit.upc.edu\/?post_type=portfolio&p=32311"},"modified":"2026-09-04T10:05:26","modified_gmt":"2026-09-04T08:05:26","slug":"materials-compostos-reciclables-i-intelligents-per-a-estructures-mes-lleugeres-segures-i-eficients","status":"publish","type":"portfolio","link":"https:\/\/cit.upc.edu\/ca\/portfolio-item\/materials-compostos-reciclables-i-intelligents-per-a-estructures-mes-lleugeres-segures-i-eficients\/","title":{"rendered":"Materials compostos reciclables i intel\u00b7ligents per a estructures m\u00e9s lleugeres, segures i eficients"},"content":{"rendered":"\n

08\/09\/2026<\/p>
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El grup de recerca Control, Data and Artificial Intelligence (CoDAlab) de la Universitat Polit\u00e8cnica de Catalunya – BarcelonaTech (UPC) participa en el projecte ATHENS, que desenvolupa materials compostos termopl\u00e0stics reciclables amb sistemes avan\u00e7ats de monitoritzaci\u00f3 en temps real per millorar la fabricaci\u00f3, la seguretat i la vida \u00fatil d\u2019estructures lleugeres.<\/h4>
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Els materials compostos tenen un paper cada vegada m\u00e9s rellevant en sectors com el transport urb\u00e0, els vehicles i els trens, on la reducci\u00f3 de pes, l\u2019efici\u00e8ncia estructural i la durabilitat s\u00f3n factors clau per millorar el rendiment i reduir costos. Tot i aix\u00f2, els compostos tradicionals presenten limitacions importants: sovint s\u00f3n dif\u00edcils de reciclar, poden requerir processos de fabricaci\u00f3 complexos i necessiten sistemes fiables per garantir la seva integritat al llarg del temps.<\/p>

ATHENS neix per donar resposta a aquest repte mitjan\u00e7ant el desenvolupament de materials termopl\u00e0stics reciclables i la integraci\u00f3 de tecnologies de monitoritzaci\u00f3 intel\u00b7ligent. L\u2019objectiu \u00e9s crear estructures m\u00e9s lleugeres i eficients, optimitzar-ne la fabricaci\u00f3 amb processos automatitzats i incorporar xarxes de sensors encastats que permetin controlar-ne la qualitat i diagnosticar-ne l\u2019estat durant la seva vida \u00fatil.<\/p>

La soluci\u00f3 proposada combina materials compostos reciclables, sensors integrats, sistemes ciberf\u00edsics, intel\u00b7lig\u00e8ncia artificial i plataformes de validaci\u00f3 Hardware-in-the-Loop (HiL, una t\u00e8cnica de validaci\u00f3 en qu\u00e8 es prova un sistema f\u00edsic real \u2014per exemple, un sensor, una unitat de control, un dispositiu electr\u00f2nic o un sistema de monitoritzaci\u00f3\u2014 connectant-lo a un entorn de simulaci\u00f3 que reprodueix condicions reals de funcionament). Aquest enfocament permet monitoritzar tant el proc\u00e9s de fabricaci\u00f3 com la salut estructural del producte final, aportant una visi\u00f3 completa del comportament de l\u2019estructura des del moment en qu\u00e8 es produeix fins al seu \u00fas real.<\/p>

Una de les principals novetats del projecte \u00e9s la capacitat de detectar, identificar i predir possibles danys o fallades abans que es produeixin situacions cr\u00edtiques. A partir de les dades recollides pels sensors, el sistema impulsat per IA pot generar alertes preventives i donar suport a decisions de manteniment m\u00e9s eficients. D\u2019aquesta manera, ATHENS pot contribuir a reduir els costos de manteniment i fabricaci\u00f3, allargar la vida \u00fatil dels components i millorar la seguretat i la fiabilitat de les estructures fabricades amb materials compostos.<\/p>

En aquest marc, la UPC \u00e9s responsable del subprojecte SNAPSHOT, centrat en el desenvolupament de la capa d\u2019intel\u00b7lig\u00e8ncia del sistema. La seva aportaci\u00f3 inclou la creaci\u00f3 d\u2019algorismes d\u2019intel\u00b7lig\u00e8ncia artificial i sistemes d\u2019adquisici\u00f3 de dades per al control de processos i la monitoritzaci\u00f3 de la salut estructural. SNAPSHOT tamb\u00e9 desenvolupa un sistema ciberf\u00edsic capa\u00e7 de recollir dades de forma eficient i segura a partir de diversos sensors integrats en les estructures, tant durant la fabricaci\u00f3 com en condicions d\u2019\u00fas.<\/p>

A m\u00e9s, el subprojecte incorpora t\u00e8cniques avan\u00e7ades d\u2019IA, com l\u2019aprenentatge autom\u00e0tic basat en la f\u00edsica, l\u2019aprenentatge per transfer\u00e8ncia i models d\u2019IA explicable, amb l\u2019objectiu de millorar la precisi\u00f3, l\u2019adaptabilitat i la interpretabilitat del sistema de monitoritzaci\u00f3. Tamb\u00e9 es desenvoluparan plataformes HiL per simular condicions de funcionament realistes i validar les tecnologies de monitoritzaci\u00f3 i els algorismes d\u2019IA abans de la seva implementaci\u00f3 en entorns reals.<\/p>

Pressupost i consorci<\/h4>

ATHENS t\u00e9 una durada de 36 mesos (setembre de 2025 – agost de 2028) i compta amb un pressupost de 100.000,00 \u20ac. El projecte est\u00e0 coordinat per la Universidad Polit\u00e9cnica de Madrid (UPM), responsable al seu torn de l\u2019altre subprojecte d\u2019ATHENS, anomenat STRESS, orientat als materials i la fabricaci\u00f3. <\/p>
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\"ATHENS

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Tecnologia<\/h5>

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