Summary
Additive manufacturing holds important promises for sustainable engineering products and applications. Combining the capabilities of 3D printing in terms of bionic lightweight design with the lightweight nature of aluminium and its outstanding properties could lead to a revolution in the field of sustainable mobility. Despite the recent advances, 3D printing of aluminium alloys is a complex process that leads to many defects and a high scrap rate. This becomes a cost barrier to scale the technology especially in the sectors automotive and aerospace. AMA proposes to develop a machine learning based toolpath with process parameters that are optimal to achieve a component defect free and first-time right. In the context of the project, we will provide science-based life cycle analysis of the indirect impact of our technology in enabling competitive sustainable solutions to scale by reducing the cost barrier and also compare the parts before and after using our AI technology in cost, quality and environmental impact criteria. The technical objective will be achieved by designing a testbed demonstrator. For this, a specific Aluminium alloy (ALF357) and a representative geometry from the automotive industry will be selected. The AI process simulation algorithm will be trained with increasing geometry complexity. Once demonstrated the accuracy, the model will be used to achieve first-time right printing and avoid the traditional costly trial and errors. The success of the use case will provide 1000Kelvin with a unique demonstrator, showing the capability of our AI based APIs to solve some of the complex process challenges of the AM industry. This will help position our business as a leader technology provider and enhance our growth perspective to promote the role of women in leading tech start-ups and open the way for a more inclusive 3D printing ecosystem.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101072302 |
| Start date: | 01-06-2022 |
| End date: | 31-05-2023 |
| Total budget - Public funding: | - 75 000,00 Euro |
Cordis data
Original description
Additive manufacturing holds important promises for sustainable engineering products and applications. Combining the capabilities of 3D printing in terms of bionic lightweight design with the lightweight nature of aluminium and its outstanding properties could lead to a revolution in the field of sustainable mobility. Despite the recent advances, 3D printing of aluminium alloys is a complex process that leads to many defects and a high scrap rate. This becomes a cost barrier to scale the technology especially in the sectors automotive and aerospace. AMA proposes to develop a machine learning based toolpath with process parameters that are optimal to achieve a component defect free and first-time right. In the context of the project, we will provide science-based life cycle analysis of the indirect impact of our technology in enabling competitive sustainable solutions to scale by reducing the cost barrier and also compare the parts before and after using our AI technology in cost, quality and environmental impact criteria. The technical objective will be achieved by designing a testbed demonstrator. For this, a specific Aluminium alloy (ALF357) and a representative geometry from the automotive industry will be selected. The AI process simulation algorithm will be trained with increasing geometry complexity. Once demonstrated the accuracy, the model will be used to achieve first-time right printing and avoid the traditional costly trial and errors. The success of the use case will provide 1000Kelvin with a unique demonstrator, showing the capability of our AI based APIs to solve some of the complex process challenges of the AM industry. This will help position our business as a leader technology provider and enhance our growth perspective to promote the role of women in leading tech start-ups and open the way for a more inclusive 3D printing ecosystem.Status
SIGNEDCall topic
HORIZON-EIE-2021-SCALEUP-01-03Update Date
09-02-2023
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