Multi-Objective Optimization Under Uncertainty of Part Quality in Fused Filament Fabrication
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering
Volume 8, Issue 1
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. This article is available in the ASME Digital Collection at https://10.1115/1.4053181.org/10.1115/1.4048867.
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Copyright © 2022 by ASME.
History
Received: Jan 23, 2021
Revision received: Nov 30, 2021
Published online: Jan 6, 2022
Published in print: Mar 1, 2022
Authors
Funding Information
National Institute of Standards and Technology10.13039/100000161: 70 NANB14H036
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