Multi-Objective Optimization Under Uncertainty of Part Quality in Fused Filament Fabrication

2026-08-18

AI Summary

本文通过贝叶斯神经网络模型预测和鲁棒性设计优化，解决熔融沉积成型过程中几何精度与丝材粘合质量的多目标优化问题。

Abstract

This work presents a data-driven methodology for multi-objective optimization under uncertainty of process parameters in the fused filament fabrication (FFF) process. The proposed approach optimizes the process parameters with the objectives of minimizing the geometric inaccuracy and maximizing the filament bond quality of the manufactured part. First, experiments are conducted to collect data pertaining to the part quality. Then, Bayesian neural network (BNN) models are constructed to predict the geometric inaccuracy and bond quality as functions of the process parameters. The BNN model captures the model uncertainty caused by the lack of knowledge about model parameters (neuron weights) and the input variability due to the intrinsic randomness in the input parameters. Using the stochastic predictions from these models, different robustness-based design optimization formulations are investigated, wherein process parameters such as nozzle temperature, nozzle speed, and layer thickness are optimized under uncertainty for different multi-objective scenarios. Epistemic uncertainty in the prediction model and the aleatory uncertainty in the input is considered in the optimization. Finally, Pareto surfaces are constructed to estimate the tradeoffs between the objectives. Both the BNN models and the effectiveness of the proposed optimization methodology are validated using the actual manufacturing of the parts.

Problem

• Multi-Objective Optimization
• Uncertainty
• Fused Filament Fabrication
• Geometric Inaccuracy
• Filament Bond Quality

Innovation

• Bayesian Neural Network
• Uncertainty Quantification
• Robust Design Optimization
• Multi-Objective Optimization
• Fused Filament Fabrication