Process Parameter Optimization for Enhanced Compressive Strength of FDM-Printed ABS and Glass-Fibre-Reinforced ABS Using RSM and Metaheuristic Algorithms
8 affiliations
- 1Centre for Advanced Manufacturing, National Centre for Motorsport Engineering, School of Engineering and Built Environment, University of Greater Manchester, Bolton BL3 5AB, UK
- 2Department of Computer Studies & Engineering, Symbiosis International University Dubai, Dubai Knowledge Park, Dubai P.O. Box 502930, United Arab Emirates
- 3Symbiosis International (Deemed University), Pune 412115, India
- 4Department of Mechanical Engineering, School of Engineering and Built Environment, University of Greater Manchester, Deane Road, Bolton BL3 5AB, UK
- 5Department of Mechanical Engineering, Sahyadri College of Engineering & Management, Mangalore, Affiliated to Visvesvaraya Technological University, Belagavi 575007, India
- 6School of Mechanical Engineering, REVA University, Bangaluru 560064, India
- 7Department of Mechanical Engineering, Dr. H. N. National College of Engineering, Bengaluru 560070, India
- 8Faculty of Textile Technologies and Design, Istanbul Technical University, Gümüssuyu, Istanbul 34437, Turkey
Abstract
This study systematically investigates the effect of three Fused Deposition Modelling (FDM) printing parameters, i.e., wall thickness, layer height, and infill density, on the compressive behaviour of FDM-printed acrylonitrile butadiene styrene (ABS) and glass-fibre-reinforced ABS (ABS-GF). A Response Surface Methodology (RSM)-based Central Composite Design (CCD) comprising twenty experimental runs was employed to execute the experiments and develop regression models. The statistical significance of the process variables was determined through Analysis of Variance (ANOVA). Additionally, three nature-inspired optimization techniques, namely, the Firefly Algorithm (FA), Big Bang–Big Crunch (BB-BC), and Grey Wolf Optimization (GWO), were applied to identify the optimal printing conditions for achieving maximum compression strength. Experimental results demonstrated that glass-fibre-reinforced ABS exhibited superior compressive strength, greater load-carrying capacity, and more favourable deformation characteristics compared to neat ABS. Among the investigated parameters, infill density was found to be highly influential, as it controlled the internal porosity and structural continuity of the printed parts. The quadratic regression models developed exhibited excellent prediction accuracy, with coefficients of determination exceeding 95% for ABS and 97% for ABS-GF. Optimization results showed that the GWO algorithm achieved the highest fitness value of 97.15% and 98.48% for ABS and ABS-GF, respectively. The proposed optimization approach provides an effective framework for optimizing the compressive behaviour of FDM-printed polymer composites, thereby offering valuable guidance for the design and manufacture of high-performance engineering components through additive manufacturing techniques.
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BibTeX
@article{quadros2026process,
title = {{Process Parameter Optimization for Enhanced Compressive Strength of FDM-Printed ABS and Glass-Fibre-Reinforced ABS Using RSM and Metaheuristic Algorithms}},
author = {Jaimon Dennis Quadros and Yakub Iqbal Mogul and Ganesan Subramanian and Ma Mohin and Suhas Poojary and Krishan Jones and Joseph Horne and Hanumantharaya Rangaswamy and Prashanth Thalambeti and Mohammad Shohag Alam},
journal = {Polymers},
year = {2026},
month = oct,
doi = {10.3390/polym18192398},
url = {https://europepmc.org/article/PMC/PMC13666929},
note = {CC BY},
}
RIS
TY - JOUR
TI - Process Parameter Optimization for Enhanced Compressive Strength of FDM-Printed ABS and Glass-Fibre-Reinforced ABS Using RSM and Metaheuristic Algorithms
AU - Jaimon Dennis Quadros
AU - Yakub Iqbal Mogul
AU - Ganesan Subramanian
AU - Ma Mohin
AU - Suhas Poojary
AU - Krishan Jones
AU - Joseph Horne
AU - Hanumantharaya Rangaswamy
AU - Prashanth Thalambeti
AU - Mohammad Shohag Alam
PY - 2026
DA - 2026/10/01
JO - Polymers
DO - 10.3390/polym18192398
UR - https://europepmc.org/article/PMC/PMC13666929
AB - This study systematically investigates the effect of three Fused Deposition Modelling (FDM) printing parameters, i.e., wall thickness, layer height, and infill density, on the compressive behaviour of FDM-printed acrylonitrile butadiene styrene (ABS) and glass-fibre-reinforced ABS (ABS-GF). A Response Surface Methodology (RSM)-based Central Composite Design (CCD) comprising twenty experimental runs was employed to execute the experiments and develop regression models. The statistical significance of the process variables was determined through Analysis of Variance (ANOVA). Additionally, three nature-inspired optimization techniques, namely, the Firefly Algorithm (FA), Big Bang–Big Crunch (BB-BC), and Grey Wolf Optimization (GWO), were applied to identify the optimal printing conditions for achieving maximum compression strength. Experimental results demonstrated that glass-fibre-reinforced ABS exhibited superior compressive strength, greater load-carrying capacity, and more favourable deformation characteristics compared to neat ABS. Among the investigated parameters, infill density was found to be highly influential, as it controlled the internal porosity and structural continuity of the printed parts. The quadratic regression models developed exhibited excellent prediction accuracy, with coefficients of determination exceeding 95% for ABS and 97% for ABS-GF. Optimization results showed that the GWO algorithm achieved the highest fitness value of 97.15% and 98.48% for ABS and ABS-GF, respectively. The proposed optimization approach provides an effective framework for optimizing the compressive behaviour of FDM-printed polymer composites, thereby offering valuable guidance for the design and manufacture of high-performance engineering components through additive manufacturing techniques.
SN - 2073-4360
ER -