Optimization and Regression Modeling of Fused Deposition Modeling for PLA Material for Vehicle Interior Applications

<div class="section abstract"><div class="htmlview paragraph">Additive Manufacturing (AM), particularly Fused Deposition Modeling (FDM), has revolutionized the manufacturing sector by enabling the production of complex geometries using various materials. Polylactic Acid (PLA) is a biodegradable thermoplastic often used in additive manufacturing (AM) because to its eco-friendliness, cost-effectiveness, and processing simplicity. This research seeks to enhance the parameters of Fused Deposition Modeling (FDM) for PLA material with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The researchers conducted experimental trials to investigate the influence of key FDM parameters, including layer thickness, infill density, printing speed, and nozzle temperature, on essential outcomes such as dimensional accuracy, surface quality, and mechanical qualities. The design of experiments (DOE) technique facilitated a systematic investigation of parameters. The TOPSIS method, a decision-making tool based on several criteria, was used to assess the trial data and identify the optimal parameter values. TOPSIS offers a thorough approach for improving parameters in FDM by considering both proximity to the ideal solution and distance from the negative ideal solution. The findings revealed the effectiveness of the TOPSIS technique in identifying the optimal parameter combinations for enhancing the printing quality and efficiency of PLA components. The proposed optimization framework provides significant insights into the optimization and control of processes, hence promoting the broader use of FDM technology across many sectors. This work improves the understanding of Fused Deposition Modeling (FDM) for Polylactic Acid (PLA) and offers effective methods for improving FDM settings. Manufacturers may enhance printing productivity, quality, and sustainability via the use of the TOPSIS methodology. This will subsequently facilitate the broader use of additive manufacturing technologies across many applications.</div></div>