Project description
Fatigue resistance for sustainable production
Additive manufacturing (AM) promises a sustainable future by reducing the environmental impact of industrial products. However, its widespread adoption is limited by poor fatigue resistance in AM materials due to inherent surface and internal defects. Current post-processing treatments can improve fatigue resistance but are costly, time-consuming, and sometimes impractical. Supported by the Marie Skłodowska-Curie Actions programme, the FLAME project seeks to overcome these challenges by developing a hybrid framework that integrates physics-based models with machine learning. This approach aims to enhance fatigue life prediction accuracy from 75 % to 90 %, reducing the need for extensive post-processing and cutting production time to under a minute. FLAME’s innovations are expected to advance AM technology.
Objective
Additive manufacturing (AM) has been proven as a likely potential alternative to conventional manufacturing methods as it is able to reduce the global environmental impact of many industrial products. One of the main obstacles that prevent AM from expanding its application towards fulfilling the requisites of sustainability and green manufacturing, is poor fatigue resistance of AM material caused by multiple intrinsic surface and internal defects.
Currently, different post-processing treatments are being used to tackle this challenge by enhancing surface regularity and reducing bulk defects. Despite the enhancement of fatigue resistance after the aforementioned treatments, these are additional steps that result in longer production times and higher costs. Moreover, due to the complexity of geometry and size, sometimes AM components cannot be easily post-processed by regular treatments.
Optimizing the AM processes aiming at maximized fatigue life is a plausible solution that significantly minimizes the need for post-processing. However, the main barrier here is the lack of accurate, fast, and economic prediction tools to correlate fatigue life directly to AM process parameters. FLAME will address the aforementioned needs and pave the way towards efficient fatigue life assessment of AM via developing a hybrid framework through the association of physics-based models and machine learning approaches. It will improve the state-of-the-art fatigue life prediction accuracy from 75% to 90%, making them more economical by eliminating experiments and fast with an implementation time of less than 1 minute.
FLAME, through its aforementioned performance, will effectively maximize the fatigue life at the design stage which results in a reduction of post-processing demand and a decrease in production costs. Moreover, it boosts the advancement of AM technology to replace the conventional methods paving the path to sustainable manufacturing and minimizing environmental impacts.
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: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
You need to log in or register to use this function
We are sorry... an unexpected error occurred during execution.
You need to be authenticated. Your session might have expired.
Thank you for your feedback. You will soon receive an email to confirm the submission. If you have selected to be notified about the reporting status, you will also be contacted when the reporting status will change.
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.
-
HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
See all projects funded under this programme
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.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
See all projects funded under this funding scheme
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) HORIZON-MSCA-2023-PF-01
See all projects funded under this callCoordinator
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.
20133 Milano
Italy
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.