Prediction of Optimized Design Under Dynamic Loads Using Kriging Metamodel 2022-28-0385

Stamped components play an important role in supporting various sub-systems within a typical engine and transmission assembly. In some cases, the stamped components will not initially meet the design criteria, and material may need to be added to strengthen it. However, in other cases the component may be overdesigned, and there will be opportunities to reduce mass while still meeting all design criteria. In this latter case, multiple CAE simulations are often performed to enhance the component design by varying design parameters such as thickness, bend radius, material, etc., The conventional process will assess changes in one parameter at a time, while holding other parameters constant. Though this helps in meeting the design criteria, it is often very difficult to produce the best optimized design within the limited time span with this approach.

With the aid of Altair-HyperMorph techniques, multiple design parameters can be varied simultaneously. Design of Experiments (DOE) analyses are performed using Altair-HyperStudy to extract simulated results corresponding to the pre-defined design parameters. These DOE results are analyzed thoroughly using various statistical tools to optimize the design. Also, the DOE results can be used in Machine Learning (ML) methodologies which would help in predicting the optimized results without performing the corresponding iteration.

This paper describes the usage of ML process to avoid repetitive CAE simulations and to optimize the stamped components using DOE data. This helps in getting the best optimized design within the available simulation cycle time.