Polymer micro-lattice buffer structure free impact absorption

Abstract

Advances in miniaturized electronics, perception modules, and flight controllers have expanded the use of uncrewed aerial systems (UAS) to indoor applications such as warehouse management, inspection, and subterranean exploration. Yet, no safety standards currently address the risks of lightweight aerial vehicles operating near humans. This study investigates ultra-light micro-lattice structures as protective elements to enhance crashworthiness without significantly affecting flight endurance. Patch samples with Face-Centered Cubic (FCC), Diamond (D), Kelvin (K), and Gyroid (GY) patterns were fabricated at an effective density of 65 kg/m³ and tested under compression and impact loading. Results show that Diamond and Kelvin lattices distribute loads more efficiently, achieving specific energy absorption values above 1000 J/kg, while impact tests reveal that flexible patches dissipate energy more effectively and maintain integrity under dynamic loading compared to rigid designs.

Keywords

micro-lattices ultra-lightweight structures photosensitive polymers additive manufacturing impact specific energy absorption

Introduction

The rapid expansion of uncrewed aerial systems (UAS) has been fueled by advances in miniaturized electronics, perception modules, and flight controllers, enabling deployment in warehouses,¹ public facilities,² and subterranean environments.³ Operating in such cluttered and populated spaces,⁴ however, raises pressing concerns for both human safety and the protection of on-board payloads, yet no formal standards currently govern impact resilience in lightweight UAS. This gap underscores the need for structural solutions that enhance crashworthiness without compromising the stringent mass constraints of aerial vehicles.⁵

Polymer micro-lattice structures offer a promising route to lightweight impact protection.⁶ Defined by periodic cellular architectures, they provide mechanical performance that derives not only from the base material but also from cell geometry, relative density, and manufacturing technique.^7,8 However, fabricating such intricate structures is non-trivial: most conventional processes struggle to achieve the high resolution and support-free capabilities required for defect-free lattice production. Extensive reviews by Goh et al.⁹ have highlighted the transformative potential of additive manufacturing for Unmanned Aerial Vehicles (UAVs), emphasizing its unique ability to produce complex, lightweight airframes that traditional methods cannot achieve. However, fabricating intricate structures like micro-lattices remains non-trivial. Recent advances in additive manufacturing (AM) address this limitation, with Masked Stereolithography Apparatus (MSLA) emerging as a particularly viable option. MSLA enables the low-cost fabrication of complex geometries with layer resolutions of 20–50 µm, making polymer lattices more accessible for aerospace applications.^10,11

Previous researches, namely in shielding equipment,¹² protheses¹³ and aerospace¹⁴ have highlighted the effectiveness of metallic lattices in blast and impact scenarios, where their varied deformation modes yield specific energy absorption (SEA) up to five times higher than foams or honeycombs.¹⁵ By contrast, polymer micro-lattices remain comparatively less explored despite their potential advantages in low-density, multi-impact regimes. Moreover, the diversity of cell geometries—spanning strut-based lattices, triply periodic minimal surfaces (TPMS),¹⁶ and shells—introduces variability that complicates comparison and standardization.

Recent research has successfully demonstrated the integration of micro-lattices into micro-UAV airframes to reduce weight and increase flight endurance. For instance, Xiao et al.¹⁷ employed dual-phase strengthening strategies to create ultralight chassis. However, such advancements have typically relied on industrial-grade Projection Micro-Stereolithography ( $P μ SL$ ) systems, with equipment costs exceeding $$ 100, 000$ ,¹⁸ limiting widespread adoption. This study bridges the gap between high-performance concepts and accessibility. By utilizing consumer-grade Masked Stereolithography (MSLA) hardware ( $< $ 500$ ), we investigate whether low-cost fabrication can deliver reliable safety solutions. Unlike prior works focused primarily on structural stiffness,¹⁷ our research specifically targets impact resilience and crashworthiness, determining how accessible micro-lattice manufacturing can protect lightweight payloads in dynamic collision scenarios.

This study addresses these limitations by experimentally evaluating polymer micro-lattices for low-speed impact attenuation, with three main contributions: first, the design and testing of lattice patches under realistic experimental conditions using a custom-built impact bench; second, the introduction of polymer micro-lattices fabricated via MSLA at relative densities below 100 g/m^3; and third, the demonstration that such lightweight lattices maintain competitive energy absorption, offering a viable pathway for safer and more resilient indoor UAS platforms.

Energy absorption behavior of micro-lattices

Before examining the impact mechanics and the performance of the micro-lattice patches, it is essential to outline the fundamental mechanisms of energy absorption in polymer micro-lattices. Guillon¹⁹ provides a comprehensive description of the phenomena at play, emphasizing that polymers exhibit viscoelastic behavior that depends strongly on loading rate. During impacts, when polymer chains lack sufficient time to rearrange, which increased effective stiffness and strength.²⁰ Excluding specific cases such as frictional effects or bistability, energy absorption is primarily governed by plastic deformation and typically unfolds in two phases. The first corresponds to the initiation of damage and the formation of a crushing front, often visible as a peak in the load–displacement response. This is followed by a stabilization phase, in which deformation proceeds through localized folding or buckling. In low-stiffness polymers, this “folding mode” dominates, and energy dissipation is reflected by oscillations in the force–displacement curve as successive folds develop. Crack propagation can be influenced through the introduction of features such as notches, which act as triggers for controlled failure.¹⁹ Depending on the rigidity of the base material, deformation may culminate either in brittle rupture or in a densification phase where the lattice compacts under load.²¹

To compare materials and architectures, Ramakrishna et al.²² introduced the metric of specific energy absorption (SEA), defined as the energy absorbed per unit mass:

SEA = \frac{E}{m} = \frac{F_{avg}}{A ρ}

(1)

where $E$ is the absorption energy, $m$ the specimen mass, $F_{avg}$ the average load, $A$ the cross-section, and $ρ$ the density.

Simulation approaches have sought to predict SEA, though finite element (FE) models often diverge from experimental outcomes in dynamic scenarios.²³ This discrepancy stems from structural instabilities²⁴ or manufacturing imperfections,²⁵ both of which strongly influence local failure modes. For example, Rathbun et al.²⁶ observed that dynamic SEA values systematically exceed quasi-static ones, underscoring the challenge of faithfully reproducing load cases in silico. Zhang et al.²⁷ nevertheless achieved close agreement between experiments and FE predictions (errors < 22%) by focusing on compression of BCC/FCC structures, showing that vertical struts significantly increase SEA compared to diagonals. Yet, realistic FE modeling becomes computationally prohibitive for large assemblies due to the fine mesh required,²⁸ so single-cell studies are often used to approximate bulk response. While FE captures global stiffness and peak stresses, it remains less reliable for local damage or folding patterns.²⁹

To complement SEA, Schaedler et al.³⁰ proposed the efficiency metric $η$ , which relates the actual absorbed energy to the theoretical maximum:

η = \frac{1}{σ_{p}} \int_{0}^{ϵ_{d}} σ (ϵ) d ϵ

(2)

where $σ_{p}$ is the maximum stress before densification, $σ (ϵ)$ is the stress–strain curve, and $ϵ_{d}$ the densification strain. This efficiency index contextualizes SEA relative to an ideal material capable of maintaining $σ_{p}$ until full densification. Schaedler et al. demonstrated its value by comparing quasi-static compression (1 mm/min) and intermediate-velocity impact tests (5 m/s), which we will revisit in Section when analyzing our experimental results.

Understanding these mechanisms is critical for tailoring micro-lattice architectures to the specific safety requirements of indoor aerial systems, where reliable impact attenuation must be achieved without compromising mass constraints.

Methods and materials

To contextualize the results of our test campaign, this section details the design of the patch samples, the manufacturing processes employed, and the protocols used for compression and impact testing.

Patch design

Each patch sample was designed as a square plate measuring 45 × 45 mm with a thickness of 15 mm, limiting the total mass to 10 g, including 2 g of micro-lattice material. The 15 mm thickness accommodates either one, two, or three stacked lattice layers of 5 mm each. Given the lattice volume ( $30.4 {cm}^{3}$ ) and the density of the solid material ( $\approx 1.23 g / {cm}^{3}$ ), this configuration corresponds to an effective density of approximately $65 {cm}^{3}$ , yielding a relative density of about 5.3%.

Four lattice architectures were selected: Face-Centered Cubic (FCC), Diamond (D), Kelvin (K), and Gyroid (GY). The choice of patterns was guided by three criteria: (1) providing a basis for comparison with prior studies, (2) ensuring a wide typological diversity, and (3) enabling self-supporting geometries compatible with additive manufacturing.³¹ FCC lattices (Figure 1(a)) are widely studied and ensure stability during fabrication. The D pattern (Figure 1(b)), analogous to a three-dimensional honeycomb, provides geometric homogeneity and reduced weight. The K structure (Figure 1(c)) features large internal cavities and a combination of planar and inclined struts, providing rigidity under impact while introducing potential instabilities associated with longitudinal struts aligned with the loading axis.^24,32 Finally, the gyroid (Figure 1(d)), a triply periodic minimal surface (TPMS), is recognized for its capacity to dissipate energy under compression.³³

Figure 1.

Micro-lattices patterns selected: Face Centered Cubic—FCC (a), Gyroid—GY (b), Kelvin—K (c), and Diamond—D (d) and example of the three levels of cell size to fill the 15 × 15 × 15 mm³ volume with the FCC pattern. The number of micro-lattice stages varies, but the diameters of the struts are adjusted to maintain the same relative mass. The patch samples have a total volume of 45 × 45 × 15 mm³.

To examine the role of scale, three cell sizes were designed: 15, 7.5, and 5 mm. These correspond to one, two, or three lattice layers within the patch thickness (Figure 1). Geometries and mounting interfaces were designed in Autodesk Fusion 360, while nTopology software was used to generate lattice volumes through implicit modeling. Strut thickness was tuned to equalize sample mass across patterns. Table 1 reports the strut diameters and wall thicknesses for each pattern cell size combination.

Table 1.

Diameter of the struts or thickness of the shell of micro-lattices in millimeters to maintain all patches at a weight of 2 g.

Pattern	Cell size
Pattern	50	75	150
▪FCC	0.48	0.73	1.5
♦D	0.52	0.78	1.56
★K	0.48	0.72	1.46
•GY	0.275	0.57	0.82

Note. Cell color corresponds to next graphs cell size colors: 50: [HTML]{008080}, 75: [HTML]{0082C8}, and 150: [HTML]{F58230}.

Two resin materials were evaluated: a rigid resin (Build) and a flexible resin (Tenacious), both manufactured by SirayaTech^® from urethane acrylate. Their densities, measured via hydrostatic balance on fully dense cubic samples, were 1.237 ± 0.001 g/cm³ for the rigid resin and 1.214 ± 0.08 g/cm³ for the flexible resin. Manufacturer specifications report tensile strengths of 33 MPa at 8% elongation for the rigid resin and 5 MPa at 70% elongation for the flexible resin. Our ASTM D638 tensile tests yielded 22.7 MPa at 3% elongation with a Young Modulus of 1.47 GPa for the rigid resin and 7.9 MPa at 78% elongation with a Young Modulus of 0.26 GPa for the flexible resin (https://git.initrobots.ca/lcatar/micro-lattices-patches-manufacturing). Deviations from datasheet values likely arise from variations in UV curing and post-processing, including environmental condition variation.

For clarity, abbreviations FCC, D, K, and GY refer to the patterns; cell size levels are denoted 150, 75, and 50; and the resins are abbreviated R (rigid) and S (soft). Three replicates were produced for each configuration, yielding 144 samples in total. This study leverages a full factorial design to enable a systematic and phenomenological exploration of topology-, material-, and density-dependent crushing behaviors. Leveraging the layer-wise curing characteristics of the MSLA process, multiple configurations could be fabricated simultaneously on a single build plate without significant additional manufacturing cost, allowing for a dense sampling of the design space. This approach was particularly suited to capturing non-linear responses and distinct failure modes across lattice architectures, which were central to the objectives of this study.

Patch manufacturing

Micro-lattice patches were fabricated using masked stereolithography apparatus (MSLA), which provides a favorable resolution-to-cost ratio for additively manufactured microstructures.³⁴ In MSLA, an LCD screen modulates UV exposure, curing voxels defined by pixel resolution in the XY-plane and layer height in Z. This voxelization produces nearly isotropic mechanical properties, advantageous for lattices with multidirectional strut orientations. The open-material compatibility and low cost of MSLA systems make them particularly suitable for experimental campaigns.

Samples were printed on an ©Elegoo Saturn S, equipped with a 4K 9.1-inch LCD screen, providing 48 µm XY resolution and a minimum Z step of 10 µm. Chitubox software was used for slicing. To maximize print reliability, samples were designed to be support-free.

Patch geometry required a rigid mounting interface for testing. Direct adhesion to the print bed was problematic: excessive contact area risked damaging fine struts during removal, while insufficient contact caused detachment during curing. An optimized sole plate was therefore designed (Figure 2), featuring furrows that reduce adhesion, angled transitions for structural support, and a reinforced platform ensuring rigidity under load while allowing manual detachment without damaging the lattice. The sole plate also served as a universal mounting interface across the compression and impact benches.

Figure 2.

Patch design: (a) Optimized sole plate design, (b) Furrows section with minimized surface in contact with the print bed, (c) a cross-section of the furrows grooves, and (d) the detail of the successive part layers.

The complete step-by-step manufacturing protocol can be found here (https://git.initrobots.ca/lcatar/micro-lattices-patches-manufacturing).

Patch compression tests

Compression tests were performed on an MTS Systems Alliance RF/200 machine with a 1000 N load cell (sensitivity 2.51 mV/V). Displacement speeds were set at 5 mm/min for rigid samples and 10 mm/min for flexible ones, corresponding to strain rates of $5.6 \times 10^{- 3}$ s⁻¹ and $1.1 \times 10^{- 2}$ s⁻¹, respectively. Samples were aligned on custom supports matching the sole plate geometry (Figure 3). Compression data provided insight into deformation mechanisms prior to dynamic impact testing.

Figure 3.

Compression test device with diamond (D) sample and view of a patch (FCC_50 sample).

Custom impact bench

Standardized impact tests such as Charpy, Izod, or drop-weight towers^12,35–37 are not directly representative of UAS collisions, as they neglect rebound and dynamic interactions. A custom impact bench was therefore developed (Figure 4), consisting of a linear catapult propelled by an electric motor. The design accommodates both substructures and full micro-UAS, enabling low-velocity impacts under realistic operating conditions.

Figure 4.

Overview of the custom impact test bench. The first main rail (green) is used to propel the second rail (red). The sample trolley (yellow) holds the sample (blue). Just before impact, it is released and translates freely along the red rail.

During testing, a propulsion trolley was accelerated along the primary rail and, immediately before impact, released a sample-holding trolley onto a secondary rail. This allowed the patch to rebound freely after contact. All tests were conducted at 2 m/s, representative of indoor inspection UAS where velocity is minimized for control. The sample trolley was loaded to simulate an effective system inertia of 1 kg.

The bench incorporated high-frequency instrumentation to capture impact dynamics: a PCB Piezotronics©352C34 accelerometer, three PCB Piezotronics©208C03 load cells arranged in a triangular configuration, a LeddarOne optical distance sensor for pre-impact velocity, and a P3 America© LMCR13 potentiometer for high-precision displacement. A Chronos 2.0 high-speed camera was used for qualitative analysis.

Data from the accelerometer, load cells, and potentiometer were acquired at 6250 Hz using a Siemens© SCADAS system, while the LeddarOne sensor streamed data at 100 Hz via a Python script on a Linux laptop. Synchronization was achieved by sending a 5 V trigger from the laptop to the SCADAS system prior to each launch.

Together, these design, fabrication, and testing protocols establish a robust framework for evaluating the energy absorption capacity of polymer micro-lattices. The combination of quasi-static compression and dynamic impact experiments enables direct comparison across lattice geometries, cell size levels, and material types, while the custom test bench ensures that the results are representative of realistic low-velocity UAS collision scenarios. The following section presents the outcomes of this evaluation, highlighting the relationships between geometry, material behavior, and impact performance.

Results and discussion

Based on the compression and impact test campaign, we computed and analyzed several performance metrics to characterize the behavior of the micro-lattices under static and dynamic loading.

Compression tests

A representative time-lapse of the compression experiments is provided online here. The response of the lattices is influenced by three main factors: geometry, cell size, and base material (Figure 5). These factors often interact, for instance, strut buckling depends simultaneously on cell geometry and size. In the FCC architecture, sequential yielding stages are observed, with the number of force peaks directly related to the number of lattice rows (Figure 5(a), FCC_75).

Figure 5.

Load-Displacement of softer (a) and most rigid (b) configurations. Both sides present similar behaviors: peaks of strength associated with loading plateaus. These peaks are particularly noticeable with the FCC pattern, directly correlated to the number of layers. Measurements’ uncertainties of the three samples’ repetition are represented by the colored shaded area around each curve. We deliberately did not represent the final consolidation part of the compression test to better highlight the initial variations. Therefore, the deformation stops at 10 mm and load above 100 N.

Smaller cell sizes extend the stress plateau, as does increasing the number of struts aligned with the loading axis. D and K lattices, which incorporate more vertical struts than FCC, display this effect more clearly. By contrast, the GY pattern—with its continuous surfaces but reduced contact area—shows greater variability in force response.

Material differences are pronounced: the flexible resin produces maximum forces five to ten times lower than the rigid resin due to its lower stiffness. Larger cells (cell size 150) also appear stiffer despite comprising fewer struts.

To quantify the stability of the crushing force -an essential characteristic for constant-acceleration impact absorption—we introduce a custom metric referred to as the Stiffness Score. Unlike elastic stiffness, which characterizes the initial linear response, this metric evaluates the flatness of the force plateau over the crushing regime of the force-displacement curve.

The instantaneous derivative of the force $F$ with respect to displacement $x$ was computed at each recorded point $i$ as

{F_{i}^{'} = \frac{dF}{dx} |}_{i}

(3)

A data point was considered part of a stable crushing plateau if the absolute slope satisfied $F_{i}^{'} < 10 N / mm$ The Stiffness Score is then defined as the fraction of the compression stroke meeting this condition

Stiffness Score = \frac{1}{N} \sum_{i = 1}^{N} 1_{(| F_{i}^{'} | < 10)} \times 100,

(4)

where $N$ is the total number of data points, and $1$ denotes the indicator function. A score of 100% corresponds to an ideal perfectly plastic response with a constant crushing force (Figure 6).

Figure 6.

Stiffness score for all tested combinations, showing a wide range of performances. On the right side of the graph: micro-lattices made of flexible materials obtain the highest score.

Flexible materials and D lattices consistently achieved the highest scores, indicating superior ability to distribute load smoothly. K structures ranked next, while GY were the least effective, likely due to their reduced load-bearing surface. FCC showed the greatest variability, alternating between favorable plateaus and sharp buckling peaks.

Impact tests

Impact behavior is illustrated in Figure 7, with videos available online (https://git.initrobots.ca/lcatar/micro-lattices-patches-manufacturing). Flexible and rigid lattices exhibit distinct responses. In quasi-static tests, both materials show comparable peak sequences associated with successive cell layers, though amplitudes are lower for flexible resin. Under impact, the flexible lattice again displays two peaks, but a rebound occurs before full densification of the second layer. Maximum displacement reaches 7 mm, consistent with complete buckling of the first layer.

Figure 7.

Force with respect to displacement for the FCC micro-lattice with a cell size of 7.5 mm. The curves show results for the soft material (left: (a and c)) and the rigid material (right: (b and d)), both for the impacts (top: (a and b) and the compression (bottom: (c and d)). Under the curve, in red, the absorbed energy before the rupture of the micro-lattice is represented. Measurements’ uncertainties of the three samples’ repetition are represented by the colored shaded area around each curve. Different focus areas (FA) are highlighted in orange to support the analysis of (b).

Rigid lattices fail catastrophically: high-speed imagery (Figure 8) reveals progressive stages of fracture. The first peak corresponds to initial buckling (FA n°1), followed by lower-amplitude peaks from bending of residual struts (FA n°2), then higher peaks near 300 N associated with rupture of the second cell layer (FA n°3). Final failure coincides with impact against the mounting trolley (FA n°4). Unlike flexible specimens, rigid lattices shatter, losing their ability to dissipate energy.

Figure 8.

Time-lapse of the impact of a rigid (R) and flexible (S) sample. The outcome of the impact is significantly different, with the rigid micro-lattice exploding and the flexible micro-lattice deforming without breaking. The first force peak corresponds to FA n°1 and the second force peak to FA n°3 in Figure 7.

Peak forces are consistently higher in impact than compression due to polymer stiffening under high strain rates. However, the time history differs sharply: flexible specimens sustain deformation for $> 0.7 s$ , while rigid lattices fail within 0.2 s, reflecting abrupt deceleration.

Beyond energy absorption, the maximum impact load or dynamic yield strength, $σ_{y}$ is a critical safety parameter, as it governs the peak deceleration transmitted to the payload at initial contact. Figure 9 compares the $σ_{y}$ values extracted from the first impact force peak for all tested configurations.

Figure 9.

Comparison of Dynamic Yield Stress ( $σ_{y}$ ) derived from impact tests ( $v = 2 m / s$ ). The flexible resin (hatched bars) demonstrates superior dynamic strength for fine structures (Cell Size 50) due to strain-rate stiffening, whereas the rigid resin (solid bars) suffers from premature brittle fracture, outperforming the flexible material only at larger cell sizes (150).

In contrast to quasi-static conditions, where rigid materials typically exhibit higher strength, the impact results show that flexible lattices (S) often achieve higher dynamic yield stresses than their rigid counterparts (R), particularly for finer architectures (Cell Size 50). This behavior is primarily attributed to the strain-rate sensitivity of the polymers. Under impact loading at 2 m/s, the flexible resin undergoes viscoelastic stiffening, enabling it to sustain higher transient loads, whereas the rigid resin is prone to dynamic embrittlement.

For the finest geometries (Cell Size 50), the thin rigid struts shatter experience rapid brittle fracture upon impact due to local stress concentrations, as reported in studies on ultrastiff architected materials.³⁸ This premature failure prevents the lattice from reaching its theoretical load-bearing capacity. In contrast, the flexible lattices accommodate the impact through progressive deformation, allowing higher peak forces to be reached prior to folding. The superior strength of the rigid material is only recovered for coarser architectures (Cell Size 150), where thicker struts can withstand the initial impact shock without immediate fracture.

Specific energy absorption and efficiency

SEA was computed using equation (1) for all configurations. Consistent with Ramakrishna et al.,²² loading rate had a decisive influence: patterns that performed best in compression reversed in ranking under impact. Flexible lattices, initially weaker under quasi-static loading (< 100 N, Figure 5(a)), excelled in dynamic tests by bending without fracture, whereas rigid lattices that carried high static forces (200–500 N) shattered on impact, preventing energy dissipation (Figure 10).

Figure 10.

SEA of micro-lattices samples in quasi-static (blue shaded area) and impact (brown shaded area) tests. Rigid and soft materials show an inverted tendency between the two test protocols due to the change in loading speed.

Efficiency $η$ (equation (2)) was plotted against density (Figure 11). All samples were fabricated at equal mass (2 g), corresponding to 65 kg/m³. Despite this ultralight density—below the 100 kg/m³ threshold—measured efficiencies reached 35%, substantially higher than the ≤ 10% variation reported for foams, honeycombs, and lattices by Schaedler et al.³⁰ Compared to state-of-the-art polymer and metallic lattices, our MSLA-fabricated specimens achieved comparable or superior SEA at significantly lower density, highlighting the potential of low-cost LCD printing for scalable production.

Figure 11.

Energy absorption efficiency of our micro-lattices in comparison with existing works. We address the lowest density with a significant distribution of efficiency.

These results are particularly relevant for micro-UAS below 250 g, where strict weight constraints coincide with regulatory advantages.³⁹

Association of multiple micro-lattices

Practical applications will often require combining patches with different mechanical responses. The contrasting behaviors of rigid and flexible lattices suggest that hybrid assemblies could extend impact resilience while maintaining low weight. For effective pairing, two conditions must be satisfied: (1) the soft lattice must absorb force before the rigid lattice fails, and (2) its absorbed energy must exceed the energy threshold for breaking the rigid lattice.

From the impact data, we derived critical velocities for rupture of each rigid configuration (Table 2). This “speed map” quantifies safe operating limits when rigid lattices are integrated with flexible counterparts. By cross-referencing absorbed energies, compatibility maps (Figure 12) were generated, identifying viable soft–rigid combinations. These maps demonstrate that strategic layering of different lattices can provide tailored crashworthiness for lightweight UAS.

Table 2.

Energy and velocity required to break rigid micro-lattices.

Sample	Energy (J)	Max speed (m/s)
GY_50_R	none	none
FCC_50_R	0.0084	0.1300
K_50_R	0.0298	0.2441
GY_75_R	0.0392	0.279
D_50_R	0.0941	0.4339
K_75_R	0.1041	0.4563
D_75_R	0.1980	0.6295
FCC_75_R	0.2553	0.7146
D_150_R	0.2741	0.7404
K_150_R	0.2798	0.7481
GY_150_R	0.3095	0.7868
FCC_150_R	0.8351	1.2924

Figure 12.

Result of possible associations between rigid and soft micro-lattices. Most combinations with rigid material and cell size 50 or soft material and cell size 150 are eliminated by the two association criteria.

This compatibility analysis further reveals that fewer than 30% of the possible associations satisfy the dual criteria for hybrid use. Among these, configurations with intermediate cell size (75) achieve the most favorable trade-off, offering high SEA while maintaining stiffness levels that smooth the load transfer without transmitting excessive forces likely to fracture the rigid lattice. This balance suggests that carefully tuned lattice pairing can enhance both absorption efficiency and structural reliability in ultralight aerial platforms.

Conclusion

This study examined the role of polymer micro-lattices in improving crashworthiness for lightweight aerial systems, focusing on the interplay of geometry, cell size, and material. By fabricating samples at ultralight densities below 100 g/m³ and subjecting them to both quasi-static and dynamic loading, we demonstrated that energy absorption efficiency comparable to state-of-the-art designs can be achieved at a fraction of the weight. A key outcome was the marked inversion of specific energy absorption between compression and impact tests, underscoring the critical influence of loading speed on deformation mechanisms and failure modes.

It is important to note that the studied samples comprise a limited number of unit cells (one to three layers) to match the thin profile required for UAS buffers. Consequently, the reported mechanical behavior includes boundary effects imposed by the mounting plates and does not fully represent the bulk properties of thicker, quasi-infinite lattice structures. The following results should therefore be interpreted as the performance of a finite structural component designed for integration in lightweight aerial systems, rather than an intrinsic material property.

The results also revealed the potential of combining rigid and flexible lattices to exploit their complementary strengths. Compatibility maps derived from impact data showed that only a subset of configurations are viable, with intermediate cell size offering the most favorable trade-off between stiffness and absorption efficiency. These findings provide a design framework for tailoring lattice associations to improve the resilience of aerial vehicles operating under diverse collision scenarios.

The custom-built free-rebound impact bench further enabled the evaluation of lattice behavior under conditions that closely reflect low-velocity UAS impacts. While differences from standardized tests remain to be quantified, the system demonstrated reliable repeatability and offers a flexible platform for ongoing evaluation. Future work will benchmark its performance against conventional drop towers and extend the analysis to hybrid lattice panels integrated into full-scale aerial vehicles.

Moreover, the applicability of the developed impact bench extends beyond the characterization of material samples to full-scale aerial systems. Recent work by Mili et al.⁴³ leveraged this experimental setup to assess the crashworthiness of complete commercial and custom drones. They demonstrated that the generated impact data can drive ’Safety Governors’ that actively limit flight velocity based on force thresholds. This highlights a holistic approach to safety where active control strategies act as a critical complement to the passive energy absorption capabilities provided by the micro-lattices.

It would also be relevant to complement the present experimental study with numerical investigations that leverage the datasets generated here to calibrate finite element models, particularly with respect to the rate-dependent fracture mechanisms observed in the tested resins. The measured force-displacement responses and failure modes provide suitable benchmarks for validating constitutive models under dynamic loading. In this context, microstructure-informed modeling approaches such as Voronoi-based reconstruction methods successfully applied to polymethacrylimide foams by Cen et al.⁴⁴ could be adapted to improve the prediction of yield surfaces and damage evolution in architected lattices.

Additionally, the dynamic embrittlement observed in rigid lattices suggests avenues for material-level improvements. For instance, matrix reinforcement strategies aimed at enhancing toughness and interfacial bonding-such as the incorporation of hyperbranched epoxy-grafted carbon nanotubes proposed by Peng et al.⁴⁵ could be explored in future studies to mitigate brittle failure under impact loading.

Overall, the work advances both the experimental methodology and the material–structural design space for ultralight energy-absorbing systems. By demonstrating how micro-lattices can deliver effective protection at extremely low densities, it opens new pathways for the development of safer, more durable aerial platforms within stringent mass constraints.

Footnotes

Acknowledgements

All authors contributed to the conception and design of the study. Louis Catar performed material preparation, data collection, and analysis. The first draft of the manuscript was written by Louis Catar and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

ORCID iD

Louis Catar

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors acknowledge the financial support of the NSERC CREATE UTILI program (), and FRQNT Team grant (#283381).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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