A Novel Autoencoder Variant for Predicting 3D Printing Parameters From Geometric and Consumption Constraints

In recent years, the field of 3D printing has heavily relied on expert knowledge and complex trial-and-error procedures to determine appropriate printing parameters that meet desired consumption specifications. This study introduces a novel method for predicting 10 printing parameters based on 7 geometric features and 3 target consumption constraints (time, length, weight). Rather than using a traditional autoencoder model, we implement a variant that combines a reverse model with a forward-pretrained model. The forward model, pre-trained using XGBoost, predicts the 3 target consumption parameters from the 7 geometric features and 10 printing parameters. The reverse model then generates the 10 printing parameters from the 7 geometric features and the desired 3 consumption constraints. Through staged training and optimized loss function adjustments, our model achieves an R² of 0.9567, demonstrating its precise predictive capabilities and potential to optimize the 3D printing process while reducing reliance on expert intervention.