Khái niệm cốt lõi
Optimizing extrusion force control in 3D printing through Bayesian optimization and transfer learning.
Thống kê
"The results of Section IV-B.1 demonstrate how for the force references of 0.1 N, 0.2 N, 0.3 N, and 0.4 N the RMSE of manually tuned controllers was reduced by 13.7 %, 86.9 %, 83.5 %, and 94.5 % respectively by using standard BO."
"In only one case, TL produced a marginally worse controller."
"Using data from the 0.2 N reference, TL converges marginally faster and to a better controller."
Trích dẫn
"The main contributions of this work are: The development of a preliminary Force Controlled Printing framework for FFF; A continuous Bayesian Optimization method for controller parameter tuning during a print process; The use of Transfer Learning on Bayesian Optimization to accelerate convergence rates." - Xavier Guidetti et al.