Performance Optimization of Biopolymer in Material Extrusion Process Using Support Vector Regression and Grey Wolf Algorithm

R Ozah and D Sarma, JOURNAL OF MATERIALS ENGINEERING AND PERFORMANCE, 34, 24600-24610 (2025).

DOI: 10.1007/s11665-025-11112-w

Additive manufacturing, commonly referred to as 3D printing, is a layer- by-layer technique of building products from a digital model. Fused Deposition Modeling (FDM) is a prominent additive manufacturing technology. PLA is a commonly utilized FDM element among other polymers owing to its rapid disintegration. In FDM, layer height, printing speed, extrusion temperature, and layer thickness represent a few of the process variables that have the most effects on the response parameters. Tensile strength has been identified as a critical response variable for any process-oriented evaluation of its mechanical performance and material reliability. In this work, an assessment of the tensile strength of the additively manufactured samples is performed to maximize the process response. Response surface methodology and Support Vector Regression (SVR) were utilized to develop the predictive model. The influence of process parameters is studied using the surface plots and contour plots. The current research work implemented a Grey Wolf Optimizer with the SVR-based model in modern manufacturing to enhance tensile strength, addressing the growing demand for precision and efficiency. The social hierarchy and hunting behaviors of grey wolves served as inspiration for GWO, which was used to identify the ideal process parameters that would yield the highest tensile strength. A maximum tensile strength of 37.95 MPa was attained at an extrusion temperature of 200 degrees C, a layer thickness of 0.10 mm, and a printing speed of 55.93 mm/s. Further, microstructural studies are conducted to analyze the material behavior and product quality.

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