Machine Learning Assisted Mechanical Metamaterial Design for Additive Manufacturing

Metamaterials, widely studied for its counterintuitive property such as negative Poisson’s ratio, negative refraction, negative thermal expansion, and employed in various fields, are recognised to provide foundation for superior multiscale structural designs. However, current mechanical metamaterial design methods usually relay on performing sizing optimisations on predefined topology or implementing time-consuming inverse homogenisation methods. Machine Learning (ML), as a powerful self-learning tool, is considered to have the potential of discovering metamaterial topology and extending its property bounds. This work considers the use of Neural Networks (NNs), (De-Convolutional Neural Networks) DCNNs and Generative Adversarial Networks (GANs) to speed up the generation of new topologies for metamaterials. NNs and DCNNs are trained to inversely generate metamaterial designs based on the input target effective macroscale properties, whilst the generator in GANs is expected to output diverse metamaterial microstructures with random noise inputs. This work highlights the potential of data-driven approaches in Design for Additive Manufacturing (DfAM) as an alternative to the time-consuming, conventional methods.Mechanical Engineerin

Design framework for multifunctional additive manufacturing: coupled optimization strategy for structures with embedded functional systems

The driver for this research is the development of multi-material additive manufacturing processes that provide the potential for multi-functional parts to be manufactured in a single operation. In order to exploit the potential benefits of this emergent technology, new design, analysis and optimization methods are needed. This paper presents a method that enables in the optimization of a multifunctional part by coupling both the system and structural design aspects. This is achieved by incorporating the effects of a system, comprised of a number of connected functional components, on the structural response of a part within a structural topology optimization procedure. The potential of the proposed method is demonstrated by performing a coupled optimization on a cantilever plate with integrated components and circuitry. The results demonstrate that the method is capable of designing an optimized multifunctional part in which both the structural and system requirements are considered

Design framework for multifunctional additive manufacturing: placement and routing of three-dimensional printed circuit volumes

A framework for the design of additively manufactured (AM) multimaterial parts with embedded functional systems is presented (e.g., structure with electronic/electrical components and associated conductive paths). Two of the key strands of this proposed framework are placement and routing strategies, which consist of techniques to exploit the true-3D design freedoms of multifunctional AM (MFAM) to create 3D printed circuit volumes (PCVs). Example test cases are presented, which demonstrate the appropriateness and effectiveness of the proposed techniques. The aim of the proposed design framework is to enable exploitation of the rapidly developing capabilities of multimaterial AM

Discovery of next-generation battery electrodes using topology optimisation

Energy storage systems (ESSs) are essential components for the delivery of uninterrupted renewable energy of the future. A key stride towards the development of these systems revolves around the design of insertion-electrode batteries (IEBs). However, battery cell performance metrics of capacity and rate capability in these batteries are limited by inefficient ion and electron transport due to the complex transport channels the ions must navigate to reach storage sites – a fundamental limitation of slurry-cast (SC) type electrodes. We present a gradient-driven approach to derive optimal electrode architecture, constrained only by the underlying multiphysics system defining transport mechanisms in and across solid and liquid phases. The derived framework challenges the traditional manufacturing techniques for electrodes, inspiring novel strategies for deriving new high-performance electrodes.Mechanical Engineerin

An investigation into reinforced and functionally graded lattice structures

Lattice structures are regarded as excellent candidates for use in lightweight energy absorbing applications, such as crash protection. In this paper we investigate the crushing behaviour, mechanical properties and energy absorption of lattices made by an additive manufacturing (AM) process. Two types of lattice were examined; body-centred-cubic (BCC) and a reinforced variant called BCCz. The lattices were subject to compressive loads in two orthogonal directions, allowing an assessment of their mechanical anisotropy to be made. We also examined functionally graded versions of these lattices, which featured a density gradient along one direction. The graded structures exhibited distinct crushing behaviour, with a sequential collapse of cellular layers preceding full densification. For the BCCz lattice, the graded structures were able to absorb around 114% more energy per unit volume than their non-graded counterparts before full densification, 1371 +or- 9 kJ/m3 vs. 640 +or- 10 kJ/m3. This highlights the strong potential for functionally graded lattices to be used in energy absorbing applications. Finally, we determined several of the Gibson-Ashby coefficients relating the mechanical properties of lattice structures to their density; these are crucial in establishing the constitutive models required for effective lattice design. These results improve the current understanding of AM lattices, and will enable the design of sophisticated, functional, lightweight components in the future

Insights into the mechanical properties of several triply periodic minimal surface lattice structures made by polymer additive manufacturing

Three-dimensional lattices have applications across a range of fields including structural lightweighting, impact absorption and biomedicine. In this work, lattices based on triply periodic minimal surfaces were produced by polymer additive manufacturing and examined with a combination of experimental and computational methods. This investigation elucidates their deformation mechanisms and provides numerical parameters crucial in establishing relationships between their geometries and mechanical performance. Three types of lattice were examined, with one, known as the primitive lattice, being found to have a relative elastic modulus over twice as large as those of the other two. The deformation process of the primitive lattice was also considerably different from those of the other two, exhibiting strut stretching and buckling, while the gyroid and diamond lattices deformed in a bending dominated manner. Finite element predictions of the stress distributions in the lattices under compressive loading agreed with experimental observations. These results can be used to create better informed lattice designs for a range of mechanical and biomedical applications

Experimental and Numerical Investigations on Dynamic Mechanical Properties of TPMS Structures

Triply Periodic Minimal Surface (TPMS) lattice structures have been of increasing interest due to their light weighting, enhanced mechanical properties, and energy absorption characteristics for automotive and biomedical applications. With the advent of additive manufacturing and geometric modeling software, TPMS lattices with complex geometries can be realized. In this work, TPMS lattice structures were fabricated with PLA using fused filament fabrication (FFF) and their dynamic properties are characterized through drop tower experiments. Although lightweight TPMS lattices are beneficial for their impact absorption capability, most of the existing works are limited to quasi-static compression, and dynamic impact tests are rarely performed. The current study investigates the stress-strain and energy absorption characteristics of TPMS lattices through drop tower testing and numerical modeling. Finite element modeling for TPMS lattices is carried out to validate the experimental responses. The mechanical properties, deformation, and failure mechanisms of TPMS lattices under dynamic impact are summarized for potential future applications.Mechanical Engineerin

An investigation into reinforced and functionally graded lattice structures

Lattice structures are regarded as excellent candidates for use in lightweight energy absorbing applications, such as crash protection. In this paper we investigate the crushing behaviour, mechanical properties and energy absorption of lattices made by an additive manufacturing (AM) process. Two types of lattice were examined; body-centred-cubic (BCC) and a reinforced variant called BCCz. The lattices were subject to compressive loads in two orthogonal directions, allowing an assessment of their mechanical anisotropy to be made. We also examined functionally graded versions of these lattices, which featured a density gradient along one direction. The graded structures exhibited distinct crushing behaviour, with a sequential collapse of cellular layers preceding full densification. For the BCCz lattice, the graded structures were able to absorb around 114% more energy per unit volume than their non-graded counterparts before full densification, 1371 +or- 9 kJ/m3 vs. 640 +or- 10 kJ/m3. This highlights the strong potential for functionally graded lattices to be used in energy absorbing applications. Finally, we determined several of the Gibson-Ashby coefficients relating the mechanical properties of lattice structures to their density; these are crucial in establishing the constitutive models required for effective lattice design. These results improve the current understanding of AM lattices, and will enable the design of sophisticated, functional, lightweight components in the future

Effect of Manufacture-Induced Interfaces on the Tensile Properties of 3D Printed Polyamide and Short Carbon Fibre-Reinforced Polyamide Composites

This study aims to elucidate the structure–property–process relationship of 3D printed polyamide and short carbon fibre-reinforced polyamide composites. The macroscopic properties (tensile modulus) of the 3D printed samples are quantitatively correlated to the printing process-induced intrinsic microstructure with multiple interfaces. The samples were printed with different layer thicknesses (0.1, 0.125 and 0.2 mm) to obtain the varied number of interface densities (number of interfaces per unit sample thickness). The result shows that the printed short carbon fibre-reinforced polyamide composites had inferior partially bonded interfaces compared to the printed polyamide, and consequently exhibited interface-dependent elastic performance. The tensile modulus of 3 mm thick composites decreased up to 18% as a function of interface density, whilst the other influencing aspects including porosity, crystallinity and fibre volume fraction (9%) were the same. Injection moulding was also employed to fabricate samples without induced interfaces, and their tensile properties were used as a benchmark. Predictions based on the shear-lag model were in close agreement (<5%) with the experimental data for the injection-moulded composites, whereas the tensile modulus of the printed composites was up to 38% lower than the predicted modulus due to the partial bonded interfaces