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Optimizing the flexural properties of additively manufactured PETG: a multi-objective approach

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Abstract

Additive manufacturing (AM) techniques, such as fused filament fabrication (FFF), play nowadays an important role for processing polymeric materials, owing to their considerable advantages such as its cost-effectiveness and wide range of usable materials. In particular, techniques such as FFF involve multiple parameters that critically influence printing quality, including the choice of filament material. Although, existing literature extensively investigates how these parameters affect print quality and overall process efficiency, the relative influence of specimen weight apart from process parameters is rarely discussed. In this study, we focus on the flexural properties of PETG processed via FFF. Using an orthogonal Taguchi design of experiment, we analyzed the influence of four control factors: infill density, infill pattern, layer height, and printing speed. Flexural properties were evaluated based on the flexural modulus of elasticity, flexural yield strength, and flexural absorbed energy, both in absolute terms and normalized to weight. Following the Taguchi analysis, grey relational analysis (GRA) was used to identify the optimal set of parameters for both absolute and reduced values. This study yields valuable insights into each parameter impact, the efficient fabrication capabilities, while it also provides guidelines for future research. By employing a combination of Taguchi DOE and GRA, the obtained flexural properties of the printed parts were significantly improved and optimized based on different criteria, taking into account the weight of specimens and printing time, and finally, it was deduced that the consideration of reduced values can reveal promising alternative strategies for obtaining optimized parts.

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Correspondence to Panagiotis Karmiris-Obratański.

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Karkalos, N.E., Karmiris-Obratański, P., Papazoglou, E.L. et al. Optimizing the flexural properties of additively manufactured PETG: a multi-objective approach. Int J Adv Manuf Technol 131, 4307–4326 (2024). https://doi.org/10.1007/s00170-024-13301-8

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