Optimization and Regression Modeling of Fused Deposition Modeling for PLA Material for Vehicle interior applications

Fused Deposition Modeling (FDM) is a prominent method of additive manufacturing known for its capacity to create intricate structures using thermoplastic materials. Automobile components consisting of PLA are easily recovered from end-of-life vehicles (ELV) at a cheap cost, potentially yielding better purity recycled lactic acid monomers. PLA is used by automobile manufacturers (including Ford and Mazda) to make floor mats, tires, and consoles. Polylactic Acid (PLA) is a commonly utilized biodegradable thermoplastic recognized for its environmentally friendly nature, cost-effectiveness, and straightforward processing. This study aims to optimize the FDM parameters for PLA material and construct regression models to accurately predict printing performance. The study involved conducting experimental trials to investigate the impact of important FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical outcomes, including dimensional accuracy, surface quality, and mechanical properties. The methodology of design of experiments (DOE) enabled a systematic exploration of parameters. Regression models were created using statistical methods to establish connections between process parameters and performance indicators. The results demonstrated the effectiveness of the regression models in accurately forecasting the printing performance for PLA material. The models provide valuable insights regarding the optimal parameter configurations to maximize printing efficiency, quality, and mechanical strength. This study enhances the comprehension of Fused Deposition Modeling (FDM) for Polylactic Acid (PLA) material and provides useful techniques for optimizing the manufacturing process. Manufacturers can improve printing productivity and quality by utilizing regression models. This will help promote the wider use of FDM technology in industries that need biodegradable and sustainable components.