Impact of Process Parameters on the Tensile Properties of DLP Additively Manufactured ELAST-BLK 10 UV-Curable Elastomer

Abstract

Abstract Digital light processing (DLP) is an emerging vatphotopolymerization-based 3D-printing technology where full layers of photosensitive resin are irradiated and cured with projected ultraviolet (UV) light to create a three-dimensional part layer-by-layer. Recent breakthroughs in polymer chemistry have led to a growing number of UV-curable elastomeric photoresins developed exclusively for vat photopolymerization additive manufacturing (AM). Coupled with the practical manufacturing advantages of DLP AM (e.g., industry-leading print speeds and sub-micron-level print resolution), these novel elastomeric photoresins are compelling candidates for emerging applications requiring extreme flexibility, stretchability, conformability, and mechanically-tunable stiffness (e.g., soft robotic actuators and stretchable electronics). To advance the role of DLP AM in these novel and promising technological spaces, a fundamental understanding of the impact of DLP manufacturing process parameters on mechanical properties is requisite. This paper highlights our recent efforts to explore the process-property relationship for ELAST-BLK 10, a new commercially-available UV-curable elastomer for DLP AM. A full factorial design of experiments is used to investigate the effect of build orientation and layer thickness on the quasi-static tensile properties (i.e., small-strain elastic modulus, ultimate tensile strength, and elongation at fracture) of ELAST-BLK 10. Statistical results, based on a general linear model via ANOVA methods, indicate that specimens with a flat build orientation exhibit the highest elastic modulus, ultimate tensile strength, and elongation at fracture, likely due to a larger surface area that enhances crosslink density during the curing process. Several popular hyperelastic constitutive models (e.g., Mooney-Rivlin, Yeoh, and Gent) are calibrated to our quasi-static tensile data to facilitate component-level predictive analyses (e.g., finite-element modeling) of soft robotic actuators and other emerging soft-matter applications.