Optimization and Comparision of Linear Regression Model of Process Parameters Using Anova and Taguchi Methods

Authors- Mr. K. Sree Ramachandra Murthy, Yallaboyina Siva Shankar Veera Prasad, Pepakayala Dinesh, Bhargav Manikanta Meenavali, Thirumalasetty Veera Venkata Satya Sai

Abstract-In the Material Extrusion (MEX) process, optimizing key parameters such as layer height, line width, and print speed is crucial for enhancing print efficiency, reducing material consumption, and minimizing production time. This study employs ANOVA (Analysis of Variance) and Taguchi Methods to develop and compare a linear regression model that predicts print time and material usage. Additionally, the predicted values are compared with Cura 5.9.0 software results to validate the model’s accuracy. The Taguchi method, a robust statistical optimization tool, is used to determine the most influential factors affecting the print time and material consumption by analyzing the signal-to-noise ratio. ANOVA is applied to assess the significance and interaction of each parameter, ensuring reliable optimization. Layer height impacts print resolution and time, line width influences extrusion accuracy and material flow, and print speed directly affects the total build time. A linear regression model is developed based on experimental data, predicting output responses under different parameter settings. The model’s results are then compared with Cura’s estimated print time and material consumption values, highlighting any deviations and potential areas for improvement. The findings indicate that Cura’s slicer predictions align closely with experimental results but exhibit minor discrepancies due to software-based assumptions in deposition dynamics.

DOI: /10.61463/ijset.vol.13.issue2.247