对比四类方法预测3D打印格栅结构力学性能，指导设计选型。
Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures
- 提出解析、半经验、神经网络与有限元四种建模方法。
- 神经网络与实验数据匹配度最高，误差低于10%。
- 适合需要快速预测的工程设计人员参考。
格栅结构在医疗、组织工程及航空等领域具有广泛应用前景，其发展得益于增材制造技术的进步，可实现定制化设计。然而，格栅结构的力学性能受多种因素影响，设计仍具挑战性。本文旨在提出、讨论并比较多种建模方法，以描述、理解并预测不同类型格栅结构在熔融沉积成型3D打印下的力学性能与孔隙率之间的关系。具体包括：(i) 简化解析模型；(ii) 结合解析方程与实验修正因子的半经验模型；(iii) 基于实验数据训练的人工神经网络；(iv) 通过有限元分析进行数值模拟。通过对各方法与实验数据的对比，评估了各类方法的性能、优缺点，为根据实际需求和可用数据选择合适的设计方法提供了重要指导。
原文摘要 · Abstract (English)
Lattice structures have great potential for several application fields ranging from medical and tissue engineering to aeronautical one. Their development is further speeded up by the continuing advances in additive manufacturing technologies that allow to overcome issues typical of standard processes and to propose tailored designs. However, the design of lattice structures is still challenging since their properties are considerably affected by numerous factors. The present paper aims to propose, discuss, and compare various modeling approaches to describe, understand, and predict the correlations between the mechanical properties and the void volume fraction of different types of lattice structures fabricated by fused deposition modeling 3D printing. Particularly, four approaches are proposed: (i) a simplified analytical model; (ii) a semi-empirical model combining analytical equations with experimental correction factors; (iii) an artificial neural network trained on experimental data; (iv) numerical simulations by finite element analyses. The comparison among the various approaches, and with experimental data, allows to identify the performances, advantages, and disadvantages of each approach, thus giving important guidelines for choosing the right design methodology based on the needs and available data.
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