Title: Experimental Characterization and Predictive Model Validation of Roller Compaction in Automated Fiber Placement

Authors: Roudy Wehbe, Alex Brasington, Kevin Zimovan, William Montgomery, Ian Ross, Joshua Halbritter, Ramy Harik

DOI: 10.33599/nasampe/s.22.0707

Abstract: As the world of composites manufacturing evolves and moves toward more advanced automation, Automated Fiber Placement (AFP) continues to prove itself as one of the best options for manufacturing large and complex composite structures. When using AFP, there are a myriad of inputs and factors that can impact the final quality of the structure. One of the key factors in impacting potential defects is having consistent and uniform compaction along a given toolpath. To achieve this, the behavior of the compaction roller needs to be characterized under AFP loading conditions. A series of static loading tests are carried out to determine the roller deflection, contact area, and compression when deformed as function of the input compaction force. The collected data is then integrated into a predictive algorithm to evaluate the pressure distribution along the toolpaths and throughout a complex ply surface. Information from the model can then be incorporated back into the process planning stage of AFP to minimize possible defects that are generated by improper compaction.

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Conference: SAMPE 2022

Publication Date: 2022/05/23

SKU: TP22-0000000707

Pages: 16

Price: $32.00