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Accelerating the transition to innovative and sustainable manufacturing by an AI-based software simulation that achieves first-time right printing of lightweight and complex of aluminium alloy parts.

CORDIS · observation · Publication date unknown

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 succ

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recordType

award

status

CLOSED

region

EU

value

75000

unit

EUR