Improving design and manufacturing processes in aerospace and defense requires understanding and accounting for uncertainties. For example, there will be uncertainty in the properties of the materials used and manufacturing process for any component. Even for a perfect process that produced identical components, each may be deployed in different conditions.
Determining optimal design configurations or manufacturing processes under such uncertainties is difficult and can require substantial time using physical experiments and physics-based simulations (e.g. CFD and FEA). Also, it is time consuming to sort through large amounts of manufacturing data in order to identify the most useful and relevant information.
The solution is to first train an AI or machine learning model using data from the design or manufacturing process collected by an intelligent sampling plan. Once trained, the model can rapidly make accurate predictions for all what-if scenarios. With the roadblock of computational cost removed, many otherwise infeasible analyses may be conducted to improve the design or process.
Join us for this webinar to learn how AI and machine learning models can be used to enhance aerospace and defense design and manufacturing applications.
