Multiscale Experiments and Predictive Modelling for Inverse Design and Failure Mitigation in Additively Manufactured Lattices
Mattia Utzeri, Marco Sasso, Vikram S. Deshpande, S. Kumar

TL;DR
This paper investigates how additive manufacturing process parameters influence the energy absorption and failure modes of 3D-printed lattice materials, developing predictive models to optimize design and mitigate failures.
Contribution
It introduces a multiscale experimental and modeling framework that captures process-induced variations and damage mechanisms in FFF-printed lattices, enabling inverse design and performance enhancement.
Findings
Micro-CT imaging reveals density-dependent damage modes.
FE models accurately predict crushing response at different densities.
Inter-bead damage is identified as the main energy absorption limiter.
Abstract
Additive manufacturing (AM) enables the development of high-performance architected cellular materials, emphasizing the growing importance of establishing programmable and predictable energy absorption capabilities. This study evaluates the impact of a precisely tuned fused filament fabrication (FFF) AM process on the energy absorption and failure characteristics of thermoplastic lattice materials through multiscale experiments and predictive modelling. Lattices with four distinct unit cell topologies and three varying relative densities are manufactured, and their in-plane mechanical response under quasi-static compression is measured. Macroscale testing and micro-CT imaging reveal relative density-dependent damage mechanisms and failure modes, prompting the development of a robust predictive modelling framework to capture process-induced performance variation and damage. For lower…
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Taxonomy
TopicsManufacturing Process and Optimization · Additive Manufacturing and 3D Printing Technologies · Additive Manufacturing Materials and Processes
