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A methodology, which consists of design, optimization and evaluation of periodic lattice-based cellular structures fabricated by additive manufacturing, is presented. A user-friendly design framework for lattice cellular structures is developed by using a size optimization algorithm. A 3D modeling process for the lattice-based cellular structures is introduced for non-linear finite element analysis and production. The approach is demonstrated on compression block with periodic lattice-based unit cells. First, based on loading condition, most appropriate lattice layout is selected. Then, for the selected lattice layout, the lattice components are modeled as simple beam and size of the beam cross sections is optimized using in-house optimization approach for both yield and local buckling criteria. The 3D model for the optimized lattice structure is built and non-linear finite element study is conducted to predict the performance. Physical parts are 3D printed and tested to compare with the simulations. Material properties for the 3D printed parts are determined for the finite element study using reverse engineering of actual measured data.

Stretchable strain sensors with large strain range, high sensitivity, and excellent reliability are of great interest to applications in soft robotics, wearable devices, and structure-monitoring systems. Unlike conventional template lithography-based approaches, 3D-printing can be used to fabricate complex devices in a simple and cost-effective manner. In this paper, we report 3Dprinted stretchable strain sensors that embed a flexible conductive composite material in a hyperelastic substrate. Three commercially available conductive filaments are explored, among which the ETPU from Rubber3D Printing, Sweden, shows the highest sensitivity (gauge factor of 20), with a working strain range of 0%-12.5%. The ETPU strain sensor exhibits an interesting behavior where the conductivity increases with the strain. In addition, the resistance change of the ETPU sensor in a doubly-clamped configuration in response to a wind stimulus is characterized, and the sensor shows sensitivity to wind velocity beyond 3.5 m s −1 . The experimentally identified material parameters are used in finite-element modeling and simulation to investigate the behavior of the 3D-printed stretchable strain sensor when subjected to wind loading. In particular, the model-predicted sensor output at different wind speeds, obtained with the computed sensor strain and the experimentally characterized strain-resistance relationship, achieves good match with the experimental data.

Purpose Methods to optimize lattice structure design, such as ground structure optimization, have been shown to be useful when generating efficient design concepts with complex truss-like cellular structures. Unfortunately, designs suggested by lattice structure optimization methods are often infeasible because the obtained cross-sectional parameter values cannot be fabricated by additive manufacturing (AM) processes, and it is often very difficult to transform a design proposal into one that can be additively designed. This paper aims to propose an improved, two-phase lattice structure optimization framework that considers manufacturing constraints for the AM process. Design/methodology/approach The proposed framework uses a conventional ground structure optimization method in the first phase. In the second phase, the results from the ground structure optimization are modified according to the pre-determined manufacturing constraints using a second optimization procedure. To decrease the computational cost of the optimization process, an efficient gradient-based optimization algorithm, namely, the method of feasible directions (MFDs), is integrated into this framework. The developed framework is applied to three different design examples. The efficacy of the framework is compared to that of existing lattice structure optimization methods. Findings The proposed optimization framework provided designs more efficiently and with better performance than the existing optimization methods. Practical implications The proposed framework can be used effectively for optimizing complex lattice-based structures. Originality/value An improved optimization framework that efficiently considers the AM constraints was reported for the design of lattice-based structures.

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