Web Stiffening of Additive Manufactured Polylactide (PLA) Railroad Tracks Using Fiberglass Wrapping
DOI:
https://doi.org/10.37256/dmt.3220233190Keywords:
3D printing, fiber reinforced polymer (FRP) composite fiberglass wrapping, MPMs, bending flexure tests, experimental investigationsAbstract
Three-dimensional (3D) printed railroad tracks can properly address the rendering of a variety of topographic profiles of naturally occurring landscapes and reduce construction costs. Load tests of PLA railroad tracks found that web failure is a dominant failure mode. This paper evaluates the web stiffening strategies of additively fabricated PLA railroad tracks for rail-guided, micro-people movers (MPMs). To strengthen the track web, web-widening with additional PLA material and stiffening using composite reinforcement using glass fiber reinforced polymer (GFRP) wrap have been investigated. The composite wrapping technique was conducted by bonding fiberglass cloth to both sides of the web using a polymeric resin. Flexural load tests were conducted on the specimens with different infill volume percentages. A linear trend was observed on the peak bending capacities of specimens with PLA infill fiber contents ranging between 20% to 100%. Different failure modes were observed for the different percentage specimens during the tests. When compared to their unreinforced counterparts, the 60% infill specimen had the largest increase in strength about 1,500 N in bending with the addition of GFRP wrap. The tangent stiffnesses of all samples were calculated which showed the 100% infill specimen had the highest increase of approximately 50%. The wrapping reinforcement was found to significantly modify the failure mechanisms of the 3D-printed tracks with different infill volume percentages. The strengthening of the 3D-printed tracks using GFRP wrapping is also shown to be similar to the widening of web sections of the additively printed railroad tracks.
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Copyright (c) 2023 Navanit Sri Shanmugam, Vidya Subhash Chavan, David Boyajian, Shen-En Chen, Nicole L. Braxtan, R. Janardhanam
This work is licensed under a Creative Commons Attribution 4.0 International License.