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Printing Parameter Optimization of Additive Manufactured PLA Using Taguchi Design of Experiment

Three-dimensional printing (3DP), known as additive layer manufacturing (ALM), is a manufacturing process in which a three-dimensional structure is constructed by successive addition of deposited layers. Fused Deposition Modeling (FDM) has evolved as the most frequently utilized ALM process because...

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Detalles Bibliográficos
Autores principales: Ahmed, Bilal Anjum, Nadeem, Uzair, Hakeem, Abbas Saeed, Ul-Hamid, Anwar, Khan, Mohd Yusuf, Younas, Muhammad, Saeed, Hasan Aftab
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10675521/
https://www.ncbi.nlm.nih.gov/pubmed/38006094
http://dx.doi.org/10.3390/polym15224370
Descripción
Sumario:Three-dimensional printing (3DP), known as additive layer manufacturing (ALM), is a manufacturing process in which a three-dimensional structure is constructed by successive addition of deposited layers. Fused Deposition Modeling (FDM) has evolved as the most frequently utilized ALM process because of its cost-effectiveness and ease of operation. Nevertheless, layer adhesion, delamination, and quality of the finished product remain issues associated with the FDM process parameters. These issues need to be addressed in order to satisfy the requirements commonly imposed by the conventional manufacturing industry. This work is focused on the optimization of the FDM process and post-process parameters for Polylactic acid (PLA) samples in an effort to maximize their tensile strength. Infill density and pattern type, layer height, and print temperature are the process parameters, while annealing temperature is the post-process parameter considered for the investigation. Analysis based on the Taguchi L18 orthogonal array shows that the gyroid infill pattern and annealing cycle at 90 °C results in a maximum ultimate tensile strength (UTM) of 37.15 MPa. Furthermore, the regression model developed for the five variables under study was able to predict the UTS with an accuracy of more than 96%.