Optimization and Regression Modeling of Additive Manufacturing (Fused Deposition Modeling) of PETG Material for Automobile applications

Additive Manufacturing (AM) techniques, specifically Fused Deposition Modeling (FDM), have transformed the manufacturing industry by allowing the creation of intricate shapes using different materials. Polyethylene Terephthalate Glycol (PETG) is a thermoplastic that is commonly used in additive manufacturing (AM) because of its advantageous mechanical properties, resistance to chemicals, and ease of processing. PETG is used especially for producing automotive components that require impact resistance, dimensional stability, and good surface finish. It can be used for interior trim parts, protective covers, and exterior components The primary objective of this study is to enhance the FDM process parameters for PETG material and construct regression models that can accurately forecast important performance metrics. An investigation was carried out through experimental trials to examine the impact of FDM process parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on important factors including dimensional accuracy, surface finish, and mechanical strength. The methodology of Design of Experiments (DOE) was utilized to systematically investigate the parameter space and determine the most favorable settings. Regression models were created using statistical methods to establish connections between process parameters and performance indicators. These models offer a predictive tool for optimizing FDM parameters and attaining desired results in PETG additive manufacturing. The results emphasize the ideal parameter combinations for improving the quality and efficiency of PETG FDM printing. Moreover, the regression models effectively forecast performance metrics, enabling the optimization and control of processes. This study enhances the understanding of PETG additive manufacturing procedures and offers practical techniques for optimizing FDM parameters. Manufacturers can optimize the production of PETG components by utilizing regression modeling techniques. This will result in enhanced quality and consistency, ultimately facilitating the wider implementation of AM technology across different industries.