Project description
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, Bulgaria, Cyprus, Germany, Greece, Italy, Jordan and Spain. This will be achieved by 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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- medical and health sciences health sciences public health epidemiology pandemics
- social sciences political sciences political transitions revolutions
- medical and health sciences health sciences infectious diseases RNA viruses coronaviruses
- natural sciences earth and related environmental sciences environmental sciences pollution
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.3. - Stimulating innovation by means of cross-fertilisation of knowledge
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-RISE - Marie Skłodowska-Curie Research and Innovation Staff Exchange (RISE)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-RISE-2020
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
157 72 ATHINA
Greece
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.