Application of machine learning and parametric NURBS geometry to mode shape identification. Issue 2 (3rd March 2016)

• Record Type: Journal Article
• Authors: Porter, Robert; Hepworth, Ammon; Jensen, C. Greg
• Is Part Of: Computer-aided design and applications. Volume 13:Issue 2(2016)
• Page Start: 184
• Page End: 198
• Publication Date: 2016-03-03
• Subjects: Mode shape identification -- machine learning -- parametric geometry; Computer-aided design -- Congresses; Computer-aided design -- Periodicals; Engineering design -- Data processing -- Congresses; Engineering design -- Periodicals; 620.00420285
• DOI: 10.1080/16864360.2015.1084185
• Languages: English
• ISSNs: 1686-4360

Abstract

Abstract: 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 the 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 paper investigates the automation of the mode shape identification process through the use of parametric geometry and machine learning. This allows the designer to gain a more complete view of the parts' dynamic properties. It also allows for increased time savings over the current standard of visual inspection. GRAPHICAL ABSTRACT: