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 stu...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.展开更多
基金funding by the Deutsche Forschungsgemeinschaft under Germany's Excellence Strategy within the Cluster of Excellence PhoenixD(EXC 2122,Project ID 390833453).
文摘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.
