Toolkit for synthesising large high-quality datasets by simulations for the analysis of the machine design and for the training of the optimisation algorithms that will propose optimal design parameters


Machine Synthetic Data Generator

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.