Toolkit to assist designers to optimally define the key design parameters in multiphysical systems, enhancing machine performance as required for each scenario, through AI.

Machine Design Optimiser

The AI-based tool for the design phase of mechanisms and dynamic machines encompasses various modules to address the complexities of optimizing dynamic systems. These modules aim to support the design process by creating models that establish relationships between machine performance indicators and design parameters. These models can be either data-driven or based on machine physics, allowing for flexibility in the design process.

The AI-assistant within the tool enables users to modify design parameters based on objective functions and criteria. Manufacturing and operation constraints, as well as boundary conditions, are defined to ensure the parameters fall within applicable ranges. Target criteria are established and weighted according to their importance, enabling the optimization of CAD parameters.

Additionally, the tool takes into account the evolution of machines over their lifecycle, considering advanced effects such as clearance, flexibility, thermal influences, and fatigue crack evolution. The impact of design parameters on these effects is analyzed to enhance the understanding of machine behavior.

Parametric models play a crucial role in the development of the AI module, incorporating optimization criteria for machine components. These models provide a framework for incorporating design parameters and optimization goals into the AI-assisted design process, facilitating efficient and effective design optimization for mechanisms and dynamic machines.