基于贝叶斯主动学习和代理模型灵敏度分析的DLP打印光敏聚合物的工艺-断裂映射
Process-fracture mapping of a DLP-printed photopolymer using Bayesian active learning and surrogate-based sensitivity analysis
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中文总结 AI 辅助
本研究结合贝叶斯主动学习与代理模型灵敏度分析，通过DIC辅助实验高效建立DLP打印光敏聚合物的工艺-断裂映射，明确紫外曝光时间为核心工艺变量。
中文摘要 AI 辅助
数字光处理（DLP）可实现聚合物结构的快速制造，但断裂性能取决于多个相互作用的工艺变量，使得详尽的实验表征难以实现。本研究提出一种数据高效的框架，用于对DLP打印的光敏聚合物进行工艺-断裂映射，该框架结合了贝叶斯主动学习与数字图像相关（DIC）辅助的I型断裂实验。研究考虑了四个工艺参数：层角度、紫外曝光时间、层高度和打印温度。断裂阻力通过临界J积分（J_c）量化，该值由三点弯曲试验获得，试验中采用DIC评估裂纹口张开位移和铰链点运动学。从两个随机选择的工艺条件开始，高斯过程回归（GPR）和改进的上置信界（UCB）式获取函数选择了另外26个实验，共得到28个工艺条件，每个条件设置3次重复。最终的GPR代理模型对训练数据的拟合R²为0.99，留一法交叉验证性能的R²为0.63，皮尔逊相关系数r为0.81。基于代理模型的灵敏度分析量化了参数效应和全局贡献：单参数响应曲线揭示了非线性条件趋势，全局Sobol分析确定紫外曝光时间是主导工艺变量，其一阶和总阶指数分别为0.6780和0.7581；按总阶影响排序，参数依次为紫外曝光时间、层角度、打印温度和层高度。一阶Sobol指数总和为0.8058，表明存在不可忽略的交互作用和高阶效应。这些结果证明，贝叶斯主动学习引导的实验能够从稀疏实验中高效恢复工艺-断裂关系及参数交互作用。
英文摘要
Digital light processing (DLP) enables rapid fabrication of polymer structures, but fracture performance depends on multiple interacting processing variables, making exhaustive experimental characterization impractical. This work presents a data-efficient framework for process-fracture mapping of a DLP-printed photopolymer using Bayesian active learning and digital image correlation (DIC)-assisted Mode I fracture experiments. Four processing parameters were considered: layer angle, UV exposure time, layer height, and print temperature. Fracture resistance was quantified by the critical J-integral, $J_c$, obtained from three-point-bending tests with DIC-based evaluation of crack-mouth opening displacement and hinge-point kinematics. Beginning with two randomly selected conditions, Gaussian process regression (GPR) and a modified upper confidence bound (UCB)-style acquisition function selected 26 additional experiments, yielding 28 processing conditions with three replicates each. The final GPR surrogate reproduced the training data with $R^2=0.99$ and achieved leave-one-out cross-validation performance of $R^2=0.63$ and Pearson $r=0.81$. Surrogate-based sensitivity analysis quantified parameter effects and global contributions. One-at-a-time response curves revealed nonlinear conditional trends, while global Sobol analysis identified UV exposure time as the dominant processing variable, with first-order and total-order indices of 0.6780 and 0.7581, respectively. Based on total-order influence, the parameters ranked as UV exposure time, layer angle, print temperature, and layer height. The first-order Sobol indices summed to 0.8058, indicating non-negligible interaction and higher-order effects. These results demonstrate that Bayesian-active-learning-guided experimentation can efficiently recover process-fracture relationships and parameter interactions from a sparse experimental campaign.
