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

Area of Science:

• Materials Science and Engineering
• Manufacturing Technology
• Polymer Science

Background:

• Additive Manufacturing (AM), particularly Fused Deposition Modelling (FDM), is gaining traction for complex part fabrication.
• Polyethylene Terephthalate Glycol (PETG) is a versatile material for FDM applications across various industries.
• Optimizing FDM process parameters is crucial for achieving desired component quality and efficiency.

Purpose of the Study:

• To develop a predictive regression model for FDM process enhancement.
• To control and optimize Polyethylene Terephthalate Glycol (PETG) processing parameters.
• To systematically analyze the impact of critical FDM parameters on printing time, dimensional deviation, and surface finish.

Main Methods:

• Design of Experiments (DOE) was employed to gather empirical data.
• Regression modeling was utilized to establish relationships between process variables and performance metrics.
• Statistical analysis was performed to validate the predictive model's accuracy.

Main Results:

• A highly accurate predictive model was successfully developed.
• Optimal parameter settings were identified for PETG components.
• The model demonstrated the ability to enhance PETG component efficiency, surface quality, and dimensional accuracy.

Conclusions:

• The developed regression model provides a practical guide for FDM process optimization and quality control.
• This research offers valuable insights for the mass adoption of PETG-based FDM in sectors like automotive, aerospace, and biomedical.
• The study bridges experimental investigation with predictive modeling for deeper understanding of FDM dynamics.