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3D Printing Parameter Optimization Using Taguchi Approach to Examine Acrylonitrile Styrene Acrylate (ASA) Mechanical Properties

Polymer composites with different reinforcements have many applications. By adjusting process settings and adding fibers and fillers, composite properties can be improved. Additive manufacturing is popular in the polymer industry because it can manufacture intricately designed parts with fewer defec...

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Detalles Bibliográficos
Autores principales: Hameed, Abdul Zubar, Aravind Raj, Sakthivel, Kandasamy, Jayakrishna, Shahzad, Muhammad Atif, Baghdadi, Majed Abubakr
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9416275/
https://www.ncbi.nlm.nih.gov/pubmed/36015513
http://dx.doi.org/10.3390/polym14163256
Descripción
Sumario:Polymer composites with different reinforcements have many applications. By adjusting process settings and adding fibers and fillers, composite properties can be improved. Additive manufacturing is popular in the polymer industry because it can manufacture intricately designed parts with fewer defects and greater strength with less material consumption. Composites use thermoplastics and thermosetting polymers. Thermoset plastics cannot be reused or recycled; therefore, they are disposed in landfills, creating pollution and environmental harm. In this work, thermoplastic ASA (Acrylonitrile Styrene Acrylate) polymer filament is used for FDM 3D printing. The specimens are made by varying five process parameters that affected the materials’ mechanical properties. The tensile, flexural and impact specimens are made using MINITAB software and ASTM requirements. The L18 orthogonal array experimental design, specimens and results were optimized. Infill density and layer height were most influential. Maximum tensile strength of 51.86 MPa, flexural strength of 82.56 MPa and impact strength of 0.180 J/mm(2) were obtained by following the software-suggested input factors and compared with the predicted values. Final error percentage was obtained between the predicted and the experimental results and it was found to be under 3%, which is acceptable.