Process-fracture mapping of a DLP-printed photopolymer using Bayesian active learning and surrogate-based sensitivity analysis

Ethan Blackwell; Yogesh C. Chandrashekar; Guoqiang Li; Kshitiz Upadhyay

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arXiv · 2608.11594

Process-fracture mapping of a DLP-printed photopolymer using Bayesian active learning and surrogate-based sensitivity analysis

Ethan Blackwell·Yogesh C. Chandrashekar·Guoqiang Li·Kshitiz Upadhyay

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Abstract

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.

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Ethan Blackwell, Yogesh C. Chandrashekar, Guoqiang Li, Kshitiz Upadhyay. 2026-08-12. Process-fracture mapping of a DLP-printed photopolymer using Bayesian active learning and surrogate-based sensitivity analysis. https://arxiv.org/abs/2608.11594

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