This study investigates the optimization of Fused Deposition Modeling (FDM) parameters for Polyethylene Terephthalate Glycol (PETG) a material widely utilized in clinical and biomedical applications due to its favorable combination of lightweight properties, durability, and versatility. By applying a Grey-Taguchi methodology, the research systematically addresses how manufacturing variables influence mechanical performance, specifically focusing on tensile and compressive strength. Through rigorous signal-to-noise (S/N) ratio evaluations, the investigation identified the optimal parameter settings for individual mechanical properties. For tensile strength, a peak performance of 48.264MPa was achieved using level 1 extrusion temperature, a 55% infill density, and a printing speed of 20mm/s. Conversely, maximizing compressive strength to 47.762MPa required level 1 extrusion temperature, a higher infill density of 60%, and an increased printing speed of 30mm/s. Analysis of Variance (ANOVA) further confirmed that extrusion temperature, infill density, and printing speed all exert a statistically significant influence on these mechanical outcomes. Because biomedical and clinical components often require a balance of multiple mechanical traits, the study utilized Grey Relational Analysis (GRA) to establish a multi-response setting configuration of 1-3-3. This configuration yielded a peak grey relational grade of 1, representing the ideal compromise and overall optimal condition for dual-performance requirements. To ensure reliability, experimental validation tests were conducted. The results showed error margins of 4.01% for tensile strength and 6.14% for compressive strength. Because both error values remain well within acceptable engineering thresholds, the findings successfully verify the efficacy and precision of this optimization framework for additive manufacturing applications.
| Published in | American Journal of Polymer Science and Technology (Volume 12, Issue 2) |
| DOI | 10.11648/j.ajpst.20261202.12 |
| Page(s) | 51-70 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2026. Published by Science Publishing Group |
Additive Manufacturing, ASTM, 3D Printing, FDM, PETG, Tensile Strength
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APA Style
Anebo, A. A., Abebe, T. H., Sinha, D. K. (2026). Simultaneous Optimization of Manufacturing Variables for 3D-Printed PETG Prototypes Leveraging Taguchi-Coupled Grey Relational Approach. American Journal of Polymer Science and Technology, 12(2), 51-70. https://doi.org/10.11648/j.ajpst.20261202.12
ACS Style
Anebo, A. A.; Abebe, T. H.; Sinha, D. K. Simultaneous Optimization of Manufacturing Variables for 3D-Printed PETG Prototypes Leveraging Taguchi-Coupled Grey Relational Approach. Am. J. Polym. Sci. Technol. 2026, 12(2), 51-70. doi: 10.11648/j.ajpst.20261202.12
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Download@article{10.11648/j.ajpst.20261202.12,
author = {Abera Ayza Anebo and Temesgen Hailegiorgis Abebe and Devendra Kumar Sinha},
title = {Simultaneous Optimization of Manufacturing Variables for 3D-Printed PETG Prototypes Leveraging
Taguchi-Coupled Grey Relational Approach},
journal = {American Journal of Polymer Science and Technology},
volume = {12},
number = {2},
pages = {51-70},
doi = {10.11648/j.ajpst.20261202.12},
url = {https://doi.org/10.11648/j.ajpst.20261202.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajpst.20261202.12},
abstract = {This study investigates the optimization of Fused Deposition Modeling (FDM) parameters for Polyethylene Terephthalate Glycol (PETG) a material widely utilized in clinical and biomedical applications due to its favorable combination of lightweight properties, durability, and versatility. By applying a Grey-Taguchi methodology, the research systematically addresses how manufacturing variables influence mechanical performance, specifically focusing on tensile and compressive strength. Through rigorous signal-to-noise (S/N) ratio evaluations, the investigation identified the optimal parameter settings for individual mechanical properties. For tensile strength, a peak performance of 48.264MPa was achieved using level 1 extrusion temperature, a 55% infill density, and a printing speed of 20mm/s. Conversely, maximizing compressive strength to 47.762MPa required level 1 extrusion temperature, a higher infill density of 60%, and an increased printing speed of 30mm/s. Analysis of Variance (ANOVA) further confirmed that extrusion temperature, infill density, and printing speed all exert a statistically significant influence on these mechanical outcomes. Because biomedical and clinical components often require a balance of multiple mechanical traits, the study utilized Grey Relational Analysis (GRA) to establish a multi-response setting configuration of 1-3-3. This configuration yielded a peak grey relational grade of 1, representing the ideal compromise and overall optimal condition for dual-performance requirements. To ensure reliability, experimental validation tests were conducted. The results showed error margins of 4.01% for tensile strength and 6.14% for compressive strength. Because both error values remain well within acceptable engineering thresholds, the findings successfully verify the efficacy and precision of this optimization framework for additive manufacturing applications.},
year = {2026}
}
TY - JOUR T1 - Simultaneous Optimization of Manufacturing Variables for 3D-Printed PETG Prototypes Leveraging Taguchi-Coupled Grey Relational Approach AU - Abera Ayza Anebo AU - Temesgen Hailegiorgis Abebe AU - Devendra Kumar Sinha Y1 - 2026/09/22 PY - 2026 N1 - https://doi.org/10.11648/j.ajpst.20261202.12 DO - 10.11648/j.ajpst.20261202.12 T2 - American Journal of Polymer Science and Technology JF - American Journal of Polymer Science and Technology JO - American Journal of Polymer Science and Technology SP - 51 EP - 70 PB - Science Publishing Group SN - 2575-5986 UR - https://doi.org/10.11648/j.ajpst.20261202.12 AB - This study investigates the optimization of Fused Deposition Modeling (FDM) parameters for Polyethylene Terephthalate Glycol (PETG) a material widely utilized in clinical and biomedical applications due to its favorable combination of lightweight properties, durability, and versatility. By applying a Grey-Taguchi methodology, the research systematically addresses how manufacturing variables influence mechanical performance, specifically focusing on tensile and compressive strength. Through rigorous signal-to-noise (S/N) ratio evaluations, the investigation identified the optimal parameter settings for individual mechanical properties. For tensile strength, a peak performance of 48.264MPa was achieved using level 1 extrusion temperature, a 55% infill density, and a printing speed of 20mm/s. Conversely, maximizing compressive strength to 47.762MPa required level 1 extrusion temperature, a higher infill density of 60%, and an increased printing speed of 30mm/s. Analysis of Variance (ANOVA) further confirmed that extrusion temperature, infill density, and printing speed all exert a statistically significant influence on these mechanical outcomes. Because biomedical and clinical components often require a balance of multiple mechanical traits, the study utilized Grey Relational Analysis (GRA) to establish a multi-response setting configuration of 1-3-3. This configuration yielded a peak grey relational grade of 1, representing the ideal compromise and overall optimal condition for dual-performance requirements. To ensure reliability, experimental validation tests were conducted. The results showed error margins of 4.01% for tensile strength and 6.14% for compressive strength. Because both error values remain well within acceptable engineering thresholds, the findings successfully verify the efficacy and precision of this optimization framework for additive manufacturing applications. VL - 12 IS - 2 ER -
Department of Mechanical Engineering (Manufacturing Engineering Specialization), Wolaita Sodo University, Wolaita Sodo, Ethiopia
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