Descripción del proyecto
El aprendizaje automático puede llevar la fabricación por adición a un nuevo nivel de procesamiento
La rápida expansión mundial de la industria de la arquitectura, la ingeniería y la construcción (AECI, por sus siglas en inglés) está generando un gran volumen de residuos, un elevado consumo energético y una grave contaminación medioambiental. El Pacto Verde Europeo insta a la AECI a minimizar los materiales utilizados y los residuos generados. La fabricación por adición (FA) es un proceso automatizado y flexible puede producir objetos geométricos complejos, reduciendo el tiempo de producción. El proyecto financiado con fondos europeos ADDOPTML desarrollará un exhaustivo proceso de fabricación de diseños basados en el aprendizaje automático para estructuras civiles. Fomentará sinergias entre expertos académicos de múltiples disciplinas y pymes de Bélgica, Chipre, Alemania, Grecia, Italia, Jordania, Países Bajos y España. Se basará en el impulso de tecnologías de FA, incluidos consumibles reciclados, para afrontar la falta de unidades hospitalarias durante la pandemia de la COVID-19.
Objetivo
Additive Manufacturing (AM) has attracted the interest of industry due to its potential for flexible and automated production of complex geometry objects, combined with minimizing the time required to develop new products. According to recent analysis, the market for AM products is projected to grow annually by 18%. Although AM technologies are an integral part of digitized industrial production and of the 4th industrial revolution, Architectural, Engineering & Construction Industry (AECI) is reluctant to adopt them in design and construction. While AECI has been a pillar industry since 80's, its scale and impact on the global economy expands continuously. This development is also associated with important drawbacks, including large contribution to waste production, huge energy consumption and severe environmental pollution. The recent Green New Deal for Europe sets as primary objective the reform of heavy industry, including AECI, which accounts for 40% of the energy consumed on the continent. The main tools for achieving this objective are to minimize both materials used and amount of AECI waste. The principal aim of ADDOPTML network is to create and validate a holistic machine learning aided, optimum design-manufacturing process of civil structures by developing strong synergies among a multi-disciplinary team of academic experts and SMEs from Belgium, Cyprus, Germany, Greece, Italy, Jordan, Netherlands and Spain. This will be achieved taking advantage of ongoing progress in AM technologies including recycled consumables, thus contributing to the European challenge to become the world's first climate-neutral bloc by 2050. As a primary application, an integrated structural design framework based on AM optimized structural elements for transitional structures will be developed, to address the shortage of hospital units faced by many countries as coronavirus pandemic continues to sweep the World and to develop novel human shelter structure for post-disaster housing.
Ámbito científico
- social sciencespolitical sciencespolitical transitionsrevolutions
- medical and health scienceshealth sciencesinfectious diseasesRNA virusescoronaviruses
- natural sciencesearth and related environmental sciencesenvironmental sciencespollution
- engineering and technologymechanical engineeringmanufacturing engineeringadditive manufacturing
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Palabras clave
Programa(s)
Régimen de financiación
MSCA-RISE - Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE)Coordinador
157 80 ATHINA
Grecia