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The Sunload Failure Prediction of Interior Components

IP.com Disclosure Number: IPCOM000241026D
Publication Date: 2015-Mar-20
Document File: 2 page(s) / 61K

Publishing Venue

The IP.com Prior Art Database

Abstract

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The Sunload Failure Prediction of Interior Components

Background/Traditional Approach

Interiors have always been a major facet of modern vehicles but they have increasingly assumed a vital role in determining the success or failure of a vehicle in the market. A successful interior system not only needs to meet all functional requirements, but also has to have distinguished quality and craftsmanship as represented by its aesthetically appealing surfaces, harmonious gap configurations, tight margins and fit-and-finishes. Furthermore, such characteristics have to be maintained under a wide range of loading conditions. These loading conditions can be broadly categorized into three types: (1). Process loads from vehicle manufacturing. They include residual stresses from injection molding process and assembly loads from fasteners, clips, springs, bumpers, and weldings; (2). Mechanical loads from customer usage and those transferred from road during the lifetime of the vehicle; and (3). Thermal loads due to sun exposure and ambient climate changes. All these loads can lead to permanent deformation and result in dimensional failure such as surface distortion, warpage, squeak and rattles, and loss of functionality.

The current CAE simulation of sunload is using either the linear thermal expansion or non- linear creep analysis without considering the effect of residual stress induced into the plastic parts during the injection molding process. The thermal loads are applied as uniform temperatures on the interior parts, which is usually not true.

New Approach

The developed new CAE simulation tool for Sunload Failure Prediction of Interior Components encompasses innovative CAE methods at both microstructural and macroscopic levels, and guided by a multi-physics CAE process with seamless integration as shown in Diagram 1.

 Long-fiber entanglement model for the injection molding process. This semi-analytical model provides efficient pr...