Title: HYBRIDIZED MODEL FOR TEMPERATURE PREDICTION IN AUTOMATED FIBER PLACEMENT

Authors: Matthew J. Godbold, Ben J. Francis, Alex R. Brasington, Ramy Harik

DOI: 10.33599/nasampe/s.23.0016

Abstract: Automated Fiber Placement (AFP) is an advanced manufacturing technique for industrial-scale composite structures. When using AFP, there are a multitude of inputs and factors that can impact the final quality of the structure. To ensure interlaminar strength and optimal interface between the incoming tows and the substrate, the AFP head utilizes a heating element to induce resin tackiness. Attempts at modeling heat within AFP currently utilize physics and multiphysics-based modeling (PBM) strategies. However, PBM often falls short due to the interactions of countless parameters and prevalence of unknown variables, namely material related. This paper will present a hybridized temperature model for AFP, where data-driven modeling (DDM) predicts the applied temperature informing PBM methods for evaluation of temperature distribution and evolution. Data will be collected through experimentation for development of the models and a virtual case-study will be performed to assess the validity of the DDM and the full hybridized physics-data model. To demonstrate the benefits of such an integration, a case-study is executed on a complex curvature tool.

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Conference: SAMPE 2023

Publication Date: 2023/04/17

SKU: TP23-0000000016

Pages: 24

Price: $48.00