Thu, Dec 8, 2022 3:00 AM - 4:00 AM CST
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.