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This study investigates the programmable strain sensing capability, auxetic behaviour, and failure modes of 3D-printed, self-monitoring auxetic lattices fabricated from in-house engineered polyetheretherketone (PEEK) reinforced with multi-walled carbon nanotubes (MWCNTs). A skeletally-parametrized geometric modelling framework, combining Voronoi tessellation with 2D wallpaper symmetries, is used to systematically explore a vast range of non-predetermined topologies beyond traditional lattice designs. A representative set of these architectures is realized via fused filament fabrication, and multiscale characterization—including macroscale tensile testing and microstructural analysis—demonstrates tuneable multifunctional performance as a function of MWCNT content and unit cell topology. Real-time resistance measurements track deformation, damage initiation, and progression, with the sensitivity factor increasing from below 1 in the elastic regime (strain sensitivity) to as high as 80 for PEEK/MWCNT at 6 wt.% under inelastic deformation (damage sensitivity). Implicit architecture-topology tailoring further allows fine-tuning of mechanical properties, achieving stiffness values ranging from 9 MPa to 63 MPa and negative Poisson’s ratios between –0.63 and –0.17 using ~3 wt.% MWCNT at a relative density of 25%. Furthermore, a novel piezoresistive finite element model, implemented in Abaqus via a user-defined subroutine, accurately captures the electromechanical response up to the onset of ligament failure, offering predictive capability. These results demonstrate how architecture-topology tuning can be leveraged to customise strain sensitivity and failure modes, enabling the development of multifunctional piezoresistive lattice composites for applications such as smart orthopaedic implants, aerospace skins, and impact-tolerant systems. 

Abstract One of the major challenges in 3D printing is its lack of scalability both in size and speed, which directly impacts its economic feasibility for large-scale industrial applications. Cooperative 3D printing (C3DP) is an emerging paradigm that aims to address these issues by employing multiple mobile printers that work in parallel. However, a crucial step in enabling C3DP is the development of a collision-free communication framework between the printers during the manufacturing process. Many C3DP systems found in the literature develop solutions for collision-free printing that are specific to the setup being used, thus not allowing the solution to be transferred to other similar systems. In this paper, we formulate a general framework that generates four distinct collision-free communication strategies to enable arm-arm coordination for C3DP using robotic manipulators. We considered collisions both between the arms with themselves and between the arms and the part being printed. The strategies are general in that they are agnostic to the number of printers, their kinematics, and their spatial configurations in the manufacturing environment. We conducted a study of the four strategies using a two-printer scenario and then physically validated them with four test cases of varying geometries. The results show that the strategies successfully produce printed parts while being collision-free. The makespan reduction using our strategies when compared to a single printer varied from 20% to 42% depending on the strategy used. Finally, we discuss the limitations of the framework, as well as future research directions.