Thu, Feb 16, 2023 9:00 AM - 10:00 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.