The effects of toolpath and glass fiber reinforcement on bond strength and dimensional accuracy in material extrusion of a hot melt adhesive
Source abstract
Abstract
In this work, a polyamide hot melt adhesive (Technomelt PA 6910) and its glass fiber-filled composite (Loctite 3D 6910) were evaluated as feedstocks for desktop-scale thermally-driven material extrusion additive manufacturing (AM). Technomelt PA 6910 is a semicrystalline polymer with a sub-ambient glass transition temperature, intermediate melting temperature, and low recrystallization temperature. This paper aims to study the effect of glass fibers and toolpath on mechanical properties, warpage, and dimensional accuracy of prints. Glass fibers improved the yield strength of 0° raster angle bars, but reduced the strength of 90° raster angle samples. The reduced weld strength was more significant in samples cut from single road width boxes than multilayer (≈3.2 mm thick) specimens due to fast cooling of thin parts. Glass fibers prevented warpage and excessive spread of initial layers of boxes. Toolpath affected tensile properties for Technomelt PA 6910, in which longer toolpaths resulted in higher warpage and decreased tensile strength of parts due to longer layer times, but did not affect tensile properties for Loctite 3D 6910. While Technomelt PA 6910 exhibits isotropic tensile properties, the addition of glass fibers resulted in anisotropic properties of Loctite 3D 6910 bars, which was more significant in single road (≈0.4 mm thick) parts. Multilayer Loctite 3D 6910 structures are stronger, which makes this material more appropriate for larger scale applications in which there is a high thermal mass and slumping is a significant printing challenge.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Additive Manufacturing and 3D Printing Technologies · Additive Manufacturing Materials and Processes · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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