ItemArticle

Hybrid response surface methodologyparticle swarm optimization framework for predictive modeling and tensile strength optimization of PLA bio-composites

Title
Hybrid response surface methodologyparticle swarm optimization framework for predictive modeling and tensile strength optimization of PLA bio-composites
Creator
Alagarsamy, Sivasamy; Seshadhri, Venugopal; Packkirisamy, Vignesh; Muruganandham, Rajagopal; Chanakyan, Chandrasekaran
Description
Developing mechanically robust biodegradable composites is critical for next-generation orthopedic support devices. Although polylactic acid (PLA) is widely used in additive manufacturing, incorporating fillers can lead to reduced tensile performance when interfacial bonding with the matrix is inadequate. This study aims to enhance the mechanical performance of 3D-printed PLA reinforced with 2wt% rice husk-derived silica (SiO2) through optimized post-annealing. A hybrid statisticalcomputational framework combining Response Surface Methodology (RSM) and Particle Swarm Optimization (PSO) was implemented to identify optimal print speed, annealing temperature, and annealing time. PSO predicted the optimal conditions as 50mm/s, 90.90C, and 60min, respectively, corresponding to a projected ultimate tensile strength (UTS) of 54.65MPa. Confirmation experiments validated the prediction, yielding a mean UTS of 54.49MPa with an error below 1%. Scanning electron microscopy revealed improved interlayer fusion and enhanced ductility in the optimized samples relative to unoptimized ones. Overall, the integration of RSM and PSO effectively refined post-annealing conditions without modifying material composition, demonstrating a viable strategy for strengthening PLA-based biocomposites. The proposed framework provides a practical route for tailoring mechanical properties in biomedical additive manufacturing, particularly for load-bearing orthopedic applications. The Author(s) 2026
Source
Journal of Thermoplastic Composite Materials;
Date
01-01-2026
Publisher
SAGE Publications Ltd
Subject
Fused deposition modeling (FDM); particle swarm optimization (PSO); post-annealing; response surface methodology (RSM); rice husk derived SiO2
Coverage
Alagarsamy S., Department of Mechanical Engineering, Mahath Amma Institute of Engineering & Tech, Pudukkottai, India; Seshadhri V., Department of Mechanical Engineering, Excel Engineering College, Komarapalayam, India; Packkirisamy V., Centre for Sustainable Materials and Surface Metamorphosis, Chennai Institute of Technology, Kundrathur, India; Muruganandham R., School of Business and Management, Christ University, Karnataka, Bangalore, India; Chanakyan C., Department of Mechanical Engineering, Global Academy of Technology, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 8927057; CODEN: JTMAE
Format
online
Language
English
Type
Article

Citation

Alagarsamy, Sivasamy; Seshadhri, Venugopal; Packkirisamy, Vignesh; Muruganandham, Rajagopal; Chanakyan, Chandrasekaran, “Hybrid response surface methodologyparticle swarm optimization framework for predictive modeling and tensile strength optimization of PLA bio-composites,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 17, 2026, https://archives.christuniversity.in/items/show/23118.

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