The dynamic characteristics of a part are highly dependent on geometric and material properties of the part. The identification and tracking of vibrational mode shapes within an iterative design process becomes difficult and time consuming due to the frequently changing part definition. Currently, visual inspection of analysis results is used as the means to identify the shape of each vibrational mode determined by the modal analysis. This thesis investigates the automation of the mode shape identification process through the use of parametric geometry and the Modal Assurance Criterion. Displacement results from finite element modal analysis are used to create parametric geometry templates which can be compared one to another irrespective of part geometry or finite element mesh density. Automation of the mode shape identification process using parametric geometry and the Modal Assurance Criterion allows for the mode shapes from a baseline design to be matched to modified part designs, giving the designer a more complete view of the part's dynamic properties. It also enables the identification process to be completed much more quickly than by visual inspection.

In any design, the dynamic characteristics of a part are dependent on its geometric and material properties. Identifying vibrational mode shapes within an iterative design process becomes difficult and time consuming due to frequently changing part definition. Although research has been done to improve the process, visual inspection of analysis results is still the current means of identifying each vibrational mode determined by a modal analysis. This research investigates the automation of the mode shape identification process through the use of parametric geometry and machine learning.In the developed method, displacement results from finite element modal analysis are used to create parametric geometry which allows the matching of mode shapes without regards to changing part geometry or mesh coarseness. By automating the mode shape identification process with the use of parametric geometry and machine learning, the designer can gain a more complete view of the part's dynamic properties. It also allows for increased time savings over the current standard of visual inspection