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

• Published in: Polymers Vol. 15; no. 22; p. 4370
• Main Authors: Ahmed, Bilal Anjum, Nadeem, Uzair, Hakeem, Abbas Saeed, Ul-Hamid, Anwar, Khan, Mohd Yusuf, Younas, Muhammad, Saeed, Hasan Aftab
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2023
• Subjects: 3-D printers; 3D printing; Accuracy; Additive manufacturing; Analysis; Annealing; Carbon fibers; Design of experiments; Fused deposition modeling; Graphene; Identification and classification; Lactic acid; Manufacturing; Mechanical properties; Optimization; Orthogonal arrays; Polylactic acid; Polymers; Process parameters; Redevelopment; Regression models; Taguchi methods; Tensile strength; Three dimensional printing; Ultimate tensile strength; Saudi Arabia
• ISSN: 2073-4360; 2073-4360
• DOI: 10.3390/polym15224370

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%.