不确定性下的工艺优化以改善熔融丝制造中聚合物丝的结合质量
Process Optimization Under Uncertainty for Improving the Bond Quality of Polymer Filaments in Fused Filament Fabrication
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中文总结 AI 辅助
本文开发计算框架，结合传热与烧结颈模型，量化FFF工艺中结合质量的不确定性，构建GP代理模型优化参数，经实验验证可提升FFF产品丝材结合质量。
中文摘要 AI 辅助
本文开发了一种计算框架，用于优化工艺参数，以最大化熔融丝制造（Fused Filament Fabrication, FFF）中挤出聚合物丝之间的结合质量。该框架将提供丝材温度分布估计的瞬态传热分析与用于评估相邻丝材界面处结合质量的烧结颈生长模型相结合。预测FFF工艺中的变异性对于实现制造部件的主动质量控制至关重要；然而，用于预测变异性的模型会受到假设和近似的影响。本文系统地量化了由于各种不确定性来源（包括偶然不确定性和认知不确定性）导致的结合质量模型预测的不确定性，并将该不确定性以及模型偏差纳入工艺参数优化中。基于Sobol指数的方差敏感性分析用于量化不同不确定性来源对结合质量不确定性的相对贡献。构建了高斯过程（Gaussian Process, GP）代理模型，以在优化中计算并纳入模型偏差。开展物理实验以校准和验证物理模型，同时也用于验证最优解。结果表明，所提出的不确定性下工艺参数优化方案可使FFF产品相邻丝材之间获得高结合质量。
英文摘要
This paper develops a computational framework to optimize the process parameters such that the bond quality between extruded polymer filaments is maximized in fused filament fabrication (FFF). A transient heat transfer analysis providing an estimate of the temperature profile of the filaments is coupled with a sintering neck growth model to assess the bond quality that occurs at the interfaces between adjacent filaments. Predicting the variability in the FFF process is essential for achieving proactive quality control of the manufactured part; however, the models used to predict the variability are affected by assumptions and approximations. This paper systematically quantifies the uncertainty in the bond quality model prediction due to various sources of uncertainty, both aleatory and epistemic, and includes the uncertainty and the model discrepancy in the process parameter optimization. Variance-based sensitivity analysis based on Sobol indices is used to quantify the relative contributions of the different uncertainty sources to the uncertainty in the bond quality. A Gaussian process (GP) surrogate model is constructed to compute and include the model discrepancy within the optimization. Physical experiments are conducted for calibration and validation of the physics model and also for validation of the optimum solution. The results show that the proposed formulation for process parameter optimization under uncertainty results in high bond quality between adjoining filaments of the FFF product.
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）
机构由 AI 辅助整理，请以论文原文为准。
