Development and Comparison of Model-Based and Data-Driven Approaches for the Prediction of the Mechanical Properties of Lattice Structures

TL;DR

This paper compares four modeling approaches—analytical, semi-empirical, neural network, and finite element analysis—for predicting the mechanical properties of 3D-printed lattice structures, providing insights into their accuracy and applicability.

Contribution

It introduces and evaluates four different modeling methods for predicting lattice structure properties, highlighting their strengths and limitations.

Findings

Neural network models achieved high accuracy with experimental data.

Finite element analysis provided detailed insights but was computationally intensive.

Simplified analytical models offered quick estimates with lower precision.

Abstract

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.