Multi-response optimization of PETG FDM parameters using taguchi–grey relational analysis and perdition by regression modeling

Additive Manufacturing (AM) techniques, especially Fused Deposition Modelling (FDM), have generated much interest recently for their capabilities for manufacturing complex geometries using a variety of materials. In this work, a regression model has been developed for the FDM process performance enhancement and to control PETG processing. This study systematically analysed the effect of critical FDM parameters on key performance criteria such as printing time, dimensional deviation, and surface finish, including nozzle temperature, printing speed, and infill density. Experiments were carried out following a defined design of experiments to gather data which were then used to develop regression models for the prediction of printing results. A statistical treatment was done on the relationships among process variables with its impact on performance metrics. The predictive model developed showed a high level of accuracy, thus allowing for the identification of optimal levels for parameter settings conducive to PETG component efficiency, surface quality, and dimensional accuracy. Thus, the study acts as a practical guide for manufacturers willing to upgrade their additive manufacturing processes relating to process optimization, quality control, and production planning. By tying experimental inquiry with predictive modeling, this work delves deep into the dynamics of the FDM process and provides valuable insights for the mass use of PETG-based FDM in automotive, aerospace, biomedical, and other industry sectors.