Process Parameter Optimization and Straightness Error Prediction in FDM Based on PB-CCF Design

Fused deposition modeling (FDM) involves numerous process parameters, making it difficult to efficiently identify key influencing factors and achieve accurate prediction of straightness errors in printed parts. To address these issues, this paper proposes a two-stage experimental optimization and prediction method combining Plackett-Burman (PB) design and central composite design (CCD). First, PB experiments were conducted to screen nine process parameters and identify two significant factors affecting straightness error: layer height and line width. Subsequently, a face-centered central composite design (CCF) was employed to establish a quadratic response surface regression model for analyzing the main effects and interaction effects of the selected factors. Based on 18 sets of CCF experimental data, response surface methodology (RSM) and decision tree-genetic algorithm (DT + GA) prediction models were constructed, and their generalization capabilities were evaluated using five independent reserved experiments. The results show that the quadratic regression model exhibits a coefficient of variation of 2.21% and a signal-to-noise ratio of 45.45, indicating good fitting accuracy. On the test set, RSM achieves a mean squared error (MSE) of 7.29 × 10^-6 mm² and a mean absolute error (MAE) of 0.002142 mm, both superior to the DT + GA model (MSE = 3.41 × 10^-5 mm², MAE = 0.004578 mm), representing reductions of 78.6% in MSE and 53.2% in MAE. This validates the effectiveness and superiority of the CCF-based response surface method for straightness error prediction under small-sample conditions. The proposed strategy provides a quantitative reference for the control of shape accuracy and process optimization in FDM-printed parts.