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ADDitively Manufactured OPTimized Structures by means of Machine Learning

Project description

DEENESFRITPL

Using machine learning to move additive manufacturing to new level of processing

Rapid expansion of the architectural, engineering and construction industry (AECI) worldwide is contributing to large waste generation, high energy consumption and serious environmental pollution. The European Green Deal impels the AECI to minimise the use of materials and waste. Additive manufacturing (AM) is a flexible and automated process that can produce complex geometry objects, reducing the production time. The EU-funded ADDOPTML project will develop a comprehensive machine learning-based design manufacturing process for civil structures. It will encourage synergies between multidisciplinary academic experts and SMEs from Belgium, Cyprus, Germany, Greece, Italy, Jordan, the Netherlands and Spain. It will rely on the advancement of AM technologies, including recycled consumables, addressing the shortage of hospital units during the COVID-19 pandemic.

Objective

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.

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Topic(s)

Call for proposal

H2020-MSCA-RISE-2020

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Funding Scheme

MSCA-RISE -

Coordinator

ETHNICON METSOVION POLYTECHNION
Net EU contribution
€ 1 113 200,00
Address
Heroon polytechniou 9 zographou campus
157 80 Athina
Greece

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Region
Αττική Aττική Κεντρικός Τομέας Αθηνών
Activity type
Higher or Secondary Education Establishments
Links
Other funding
€ 0,00

Participants (12)

Partners (1)

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EC signature date 16 December 2020
Last update: 18 September 2023

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Permalink: https://cordis.europa.eu/project/id/101007595

European Union, 2023