AIDEAS
Industrial Equipment Design Suite

The primary objective of this tool is to facilitate the integration of the AI-assisted optimization modules developed in AIMDO and AIMDG with standard CAD/CAM/CAE systems, thus enabling their effective implementation. This entails the creation of APIs and user interfaces (UIs) that allow seamless integration of the AI-assisted optimization modules with CAx systems such as SolidWorks, ensuring their readiness for operational environments. The APIs and UIs will not only incorporate the specific functionalities of the optimization modules but also account for the requirements of other standard CAx solutions, such as SolidWorks. Additionally, comprehensive testing and performance optimization will be conducted as part of this task.

The primary objective of this tool is to synthesize data for training the optimization modules in AIMDO. Consequently, AI solutions will be made accessible for shorter time series and lower volume productions, with a reduced need for resources to train the relevant AI algorithms. The data will be primarily generated through artificial means, utilizing digital twins and simulations. The first step involves determining suitable samples from the parameter spaces. Subsequently, the automation of the simulation processes must be established, ensuring that the necessary computer resources are available for executing the automated simulations. Additionally, a monitoring system will be developed to oversee the simulation data space, tailored to the specific optimization modules. Real-world and historical data from pilot customers will also be incorporated to enhance the training data tensor. This inclusion is particularly important to guarantee unbiased training data, enabling the optimization modules to draw from a balanced data pool.

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.

The development of the Machine Passport for storing and sharing manufacturing data throughout the product life phases is a crucial aspect of ensuring efficient data integration, sharing, and exchange in the industry. The MP aims to establish data exchange protocols, standards, and interfaces that facilitate seamless communication between various computer-aided systems and manufacturing stages.