Improving automotive design and manufacturing processes 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, the performance of each will vary depending on uncertainties associated with its use. For example, the fatigue life of a component could vary based on the vehicle model it is installed in and road conditions.
Determining optimal design configurations or manufacturing processes under such uncertainties is difficult and can require substantial time using test data, experiments, and physics-based simulations (e.g. CFD and FEA). Also, it is time consuming to sort through large amounts of manufacturing data 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 automotive design and manufacturing applications.
