熔融沉积成型工艺中零件质量不确定性下的多目标优化
Multi-Objective Optimization Under Uncertainty of Part Quality in Fused Filament Fabrication
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中文总结 AI 辅助
针对熔融沉积成型工艺中零件质量的不确定性，研究人员采用贝叶斯神经网络模型，优化工艺参数以最小化几何不精度并最大化丝材结合质量，验证了方法的有效性。
中文摘要 AI 辅助
本研究提出一种数据驱动方法，用于解决熔融沉积成型（FFF）工艺中工艺参数不确定性下的多目标优化问题。该方法以最小化制造零件的几何不精度、最大化丝材结合质量为目标，优化工艺参数。首先开展实验收集与零件质量相关的数据；随后构建贝叶斯神经网络（BNN）模型，将几何不精度与结合质量预测为工艺参数的函数，该模型可捕获因模型参数（神经元权重）知识不足导致的认知不确定性，以及因输入参数固有随机性引发的输入变异性。基于这些模型的随机预测，研究不同基于鲁棒性的设计优化公式，针对不同多目标场景，在不确定性下优化喷嘴温度、喷嘴速度、层厚等工艺参数，优化过程同时考虑预测模型的认知不确定性与输入的偶然不确定性。最终构建帕累托曲面以评估目标间的权衡关系，通过实际零件制造验证了BNN模型及所提优化方法的有效性。
英文摘要
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.
发表机构
- Vanderbilt University（范德堡大学）
- National Institute of Standards and Technology (NIST)（美国国家标准与技术研究院）
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