Processed data for figures.

The expanding range of materials available for 3D printing is driving its widespread adoption in advanced fields. As 3D printing becomes increasingly prevalent in the manufacturing of industrial components, its advantages in accommodating complex geometries and reducing material waste are attracting significant attention. Acquiring and applying precise elastic properties of materials during structural design is crucial for ensuring part safety and consistency. However, non-destructive mechanical property assessment methods remain limited. In this paper, we propose an efficient surrogate model, built using a Bayesian model updating approach combined with a random forest algorithm, to achieve high-precision calibration of material elastic constants. In the experiment, samples were 3D printed using fused deposition modeling, and modal information was obtained using operational modal analysis with one end fixed to simulate cantilever beam boundary conditions. Parameter updating was then performed within a Bayesian Markov Chain Monte Carlo framework. The deviation between the updated calculated frequencies and the measured frequencies was significantly reduced, and the Modal Assurance Criterion value between the updated calculated mode shapes and the measured mode shapes was higher than 0.99, demonstrating the accuracy of the updated parameters. Compared to traditional destructive testing methods, the proposed method directly calibrates the structural elastic modulus at the component level without affecting the normal use of the component, providing a more practical approach for the analysis and research of material properties in 3D printing additive manufacturing. The related technology can be extended to other structural forms of 3D-printed products.

可用于3D打印的材料种类持续拓展，正推动其在高端领域的规模化应用。随着3D打印在工业零部件制造中的普及率日益提升，其在适配复杂几何结构与降低材料损耗方面的优势受到广泛关注。在结构设计阶段获取并应用材料精准的弹性性能参数，对保障零部件的安全性与性能一致性至关重要。然而，当前非破坏性力学性能评估方法仍较为有限。本文提出一种高效代理模型，该模型结合贝叶斯模型更新（Bayesian model updating）方法与随机森林（random forest）算法构建，可实现材料弹性常数的高精度校准。实验中，采用熔融沉积成型（fused deposition modeling, FDM）工艺打印试样，并通过工作模态分析获取模态信息，将试样一端固定以模拟悬臂梁边界条件。随后在贝叶斯马尔可夫链蒙特卡洛（Bayesian Markov Chain Monte Carlo, MCMC）框架内开展参数更新。更新后的计算频率与实测频率之间的偏差显著降低，且更新后的计算振型与实测振型之间的模态置信准则（Modal Assurance Criterion, MAC）值高于0.99，验证了更新后参数的准确性。相较于传统破坏性检测方法，本文所提方法可直接在零部件层级校准结构弹性模量，且不会影响零部件的正常使用，为3D打印增材制造领域的材料性能分析与研究提供了更具实用性的路径。相关技术还可拓展至3D打印产品的其他结构形式。