Structural supercapacitors (SSCs) are a class of multifunctional composites that simultaneously provide loadcarrying and energy storage capacities. This paper presents a physics-based continuum multiscale electrochemical model aimed at understanding the influence of design decisions on the electrochemical performance of mechanically robust supercapacitors (SCs). The approach was exemplified by considering carbon aerogelmodified carbon fibre-reinforced multifunctional electrodes in a room-temperature ionic liquid (RTIL) electrolyte. The new pseudo 4D (P4D) multiscale model integrates a 3D macroscopic dynamic model to solve the charge transport while coupling a 1D microscopic equilibrium electric double layer (EDL) model to determine the local voltage-dependent double layer capacitance of the porous structural electrode. The numerical model was evaluated to simulate the conventional electrochemical device characterisation methods: cyclic voltammetry, galvanostatic charging and discharging, and electrochemical impedance spectroscopy. The model achieved prediction errors below 10 % for specific energy and capacitance, and around 20 % for specific power. Additionally, an asymmetrical electrochemical behaviour was predicted on the symmetric cell, exhibiting an unbalanced potential window and capacitance at the negative and positive electrodes. Overall, the proposed P4D multiscale electrochemical model provides a powerful tool for analysing, developing, and optimising SSCs and more general SCs with porous electrodes.

Effective battery design is complex, requiring the resolution of multiple conflicting demands. Even where optimal electrode thickness, porosity, and architecture have been determined for isolated electrodes, designing optimal electrode parameters in full-cell configurations remains challenging. The performances of the positive and negative electrodes are linked due to the need to balance their capacities and complicated by differing rate-limiting mechanisms and ion transport asymmetries, under charge and discharge. This work develops a rational strategy for full-cell electrode design. First, validated, physics-based continuum models are used to determine Pareto fronts identifying best target negative and positive electrode thickness and porosity, under charge and discharge. The calculated ion concentrations provide a mechanistic understanding of the charge/discharge asymmetry. These Pareto fronts are then plotted in the areal capacity-volumetric energy density plane to identify the most effective electrode combinations. The approach is illustrated for a lithium-ion cell configuration using a graphite negative electrode and a LiNi0.6Mn0.2Co0.2O2 positive electrode (Gr/NMC622) but can be generalised to other electrode pairs and battery chemistries.

Lattice structures are being increasingly explored due to advances in additive manufacturing (AM). However, their adoption in impact applications is hindered by an insufficient understanding of their dynamic behaviour, particularly for composite and functionally graded (FG) lattices. This study presents the first systematic investigation that concurrently evaluates multiple design parameters, including cell topology, grading strategy, fibre reinforcement, and loading conditions, on the dynamic response of AM lattices. Lattice specimens made from nylon and carbon fibre-reinforced nylon were tested under intermediate strain rates ranging from 22 to 79 s-1 using drop-weight impact. The results indicate that both stretching-dominated and bending-dominated architectures retained their characteristic failure modes across the tested strain rates, with the latter exhibiting greater strain rate sensitivity. FG lattices outperform their ungraded counterparts in energy absorption by at least 15.2 %, with fibre reinforcement providing an additional enhancement of up to 37.5 %. The experimental results align well with the Cowper-Symonds model and highlight the combined influence of material and lattice design on rate-dependent performance. The findings and insights gathered pave the way for designing high-performance AM lattices for energy absorption applications.

This paper presents a novel actuator concept for reconfigurable drones, combining 3D-printed structures with embedded smart materials, namely, shape memory alloys (SMA). By leveraging the flexibility and the rigidity of two distinct polymers and the morphing capabilities of SMA, the actuator enables lightweight, adaptive drone mechanisms. Several hinge designs were designed to assess deformation range, actuation speed, and durability. This research highlights the potential of integrating smart materials with additive manufacturing to develop morphing aerospace systems capable of real-time adaptation across diverse operational scenarios.

Recent advancements in Multi-Material Laser Powder Bed Fusion (MM-LPBF) offer the unique ability to additively manufacture highly complex structures by selectively depositing multiple material systems within a single layer in the conventional laser powder bed process. MM-LPBF combines the fine resolution of typical LPBF processes with the ability for high resolution spatial control of multiple material placements. The alternating active phase (AAP) algorithm is applied to design a multi-material Messerschmitt-Bo & uml;lkow-Blohm (MBB) beam, minimizing compliance while also considering manufacturability. Topologically optimized solutions were fabricated through MM-LPBF (316 L stainless steel and CuCrZr) and evaluated through flexural testing with digital image correlation (DIC) and mechanical performance was compared to finite element analysis (FEA). Microstructure characterization of the bi-material interface revealed localized MM-LPBF process-specific defect formation. Mechanical testing revealed progressive stages of failure initiating in the bulk CuCrZr regions and propagating to interfacial regions. DIC analysis indicated that stiffness of the multi-metal MBB structure was within 5.3% of the predicted stiffness from FEA. Findings from this study demonstrate that highly complex multimaterial topologically optimized (MM-TO) designs, which were otherwise not plausible to manufacture through either traditional or AM methods, are now feasible at high resolution. The applied method can be extended to other applications which would benefit from multi-objective criterion such as multi-material heat exchangers, biomedical devices, and energy storage devices. Finally, this research highlights the need for further MM-LPBF process development to reduce bulk porosity and interfacial defects, as its processing physics differ significantly from single material LPBF and other metal AM processes.

Multi-material additive manufacturing (MMAM) unlocks unprecedented opportunities in engineering, afforded by material and geometric complexity. However, leveraging its full potential necessitates advanced computational design tools tailored to specific application needs and fabrication techniques capable of translating digital designs into physical products with minimal discrepancy. This review provides a comprehensive evaluation of computational algorithms available for multi-material design, encompassing numerical methods like topology optimisation as well as data-driven inverse design strategies. MMAM techniques are evaluated for their multi-material design capability and manufacturing constraints. Key challenges in MMAM design are identified, including multi-material interface, advanced material response modelling, manufacturability, failure constraints, robustness, multifunctionality, and integrated design-manufacturing-validation workflow. Recommendations are made for incorporating these considerations into the optimisation framework. Future research directions are also suggested to pave the way for innovative multi-material applications.

Multifunctional structural power composites (SPCs) provide a lightweighting solution for conventional electrochemical energy storage, while offering additional mechanical capability. This work studied the anisotropic electrical response of woven carbon fibre (WCF) reinforced structural supercapacitor electrodes, i.e., plain weave, spread tow, and carbon aerogel (CAG) modified spread tow fabrics, under cyclic compaction. Experimental results show that 1 MPa compaction increased in-plane conductivity by over 60% and out-of-plane conductivity by at least five-fold for all fabrics tested. Numerical studies revealed that the intra-yarn fibre volume fraction is a critical factor for both in-plane and out-of-plane electrical performance. The predicted in-plane conductivity of woven fabrics presents a linear relationship with the intra-yarn fibre volume fraction, following a modified rule of the mixtures (ROMs). For the out-of-plane conduction, a larger number of percolating paths formed with more fibre-to-fibre contacts and fibre clusters under a higher fibre volume fraction, thus promoting the out-of-plane conductivity. Additionally, CAG modification formed a conductive CAG skin over the fabric surface, which largely reduced the in-plane electrical anisotropy of WCFs. In principle, reducing the intra-yarn free volume of WCF-reinforced electrodes provides a route towards significantly improving the electrical performance of SPCs and serves as a guidance for subsequent encapsulation and multifunctional design.

Total or partial hip replacement procedures have become one of the most frequently performed surgical procedures worldwide. However, a significant and increasing percentage of these patients return for revision surgeries due to stress shielding phenomena and the following implications such as aseptic implant loosening. Various strategies are under investigation to reduce the mechanical properties gap between the implant and the femur tissues. In this study, different development aspects have been merged to improve the design of hip implants and minimise the stress shielding. A low elastic modulus β-Ti TNTZO alloy has been used as the implant material. An actively optimised lattice structure has been embedded into the implant to tune its mechanical properties according to the bone properties of the patient. The study showed that using TNTZO reduced the average stress shielding of the implant by 31 % compared to commercial CoCr implants. Moreover, with two-step TNTZO lattice optimisation, the stress shielding was fully eliminated in all the femur sections, which accelerates the healing process of the bone and extends its long-term life. The study presented a scope for further optimisation to address other objectives, such as implant stiffness and eliminating stress concentration zones.

Polymer composites are commonly exposed to moisture and undergo reductions in mechanical properties. It is challenging to describe the moisture absorption dynamics of 3D printed parts due to manufacture-induced microstructures. This work investigates the moisture absorption of printed short carbon fibre reinforced polyamide (SFRP) with varied microstructures and its impact on mechanical properties. The printed SFRP have inferior microstructures and diffusivity increases with the number of interlayer interfaces by up to 119%, which is 258% higher than that of compression moulded composite. The yield stress and tensile modulus of SFRP decrease by up to 59% and 79%, respectively. This deterioration is irreversible and more significant than injection moulded samples as the microstructure is permanently degraded by moisture. Additionally, the shear moduli of printed polyamide and SFRP decrease by up to 63% and 74%, respectively. The results are crucial for prediction, evaluation, and maintenance of 3D printed applications in humid conditions.

Lattice structures with multiple unit cell types diversify the property space by offering more design freedom, encouraging adaptation of metamaterials in engineering applications. It is essential to ensure structural connectivity and smooth transition among different cell types to avoid pre-mature failure. In this work, we propose a framework based on latent space operations to generate smoothly morphing and fully connected transition cells, addressing the current research gap in realising lattice designs of dissimilar unit cells. Latent embedding - a lowdimensional representation of the original microstructure - is obtained through a variational autoencoder. Different types of triply periodic minimal surface (TPMS) lattice were chosen as the targets to demonstrate the capability of the algorithm in handling complex 3D geometries within a physically restricted transition region. Both qualitative and quantitative evaluations are provided to illustrate the connectivity and geometric similarity of the generated transition. Benchmark comparisons against both analytical and existing machine learning (ML) based solutions indicate the superior efficacy and generality of the proposed framework.

This paper presents an eXtended physics-informed neural networks (XPINN)-based framework for predicting the temperature history during a multi-layer Directed Energy Deposition (DED) process. The proposed XPINN-based framework, advancing from its PINN-based counterpart, demonstrates significant accuracy improvement, around 50% reduction in RMSE and maximum absolute error, and extended capability of temperature history prediction with domain decomposition for more complex configurations such as interpass time, void, and interruption of scan that are prevalent in real-life DED designs. It is validated via a series of 2D benchmark tests against numerical simulations with an increasing degree of complexity. The effect of different domain decompositions is compared and discussed. Strategies that improve the training outcome are also proposed and analysed. With the enhanced capability of working on more complex configurations while retaining the characteristic availability of derivative information, the proposed framework brings process-ware design optimisation based on scientific machine learning (SciML) techniques one step closer to the application to real-life additive manufacturing applications.

Commercial lithium-ion battery (LIB) electrodes traditionally comprise a homogeneous layer of stochastically mixed constituent materials. However, a significant barrier to cell performance is attributed to the architecture of the electrode; the trade-off between useful capacity and rate capability limits the cell performance during fast charging or discharging. This study develops a continuum model to emulate the behaviour of these electrodes. It presents optimal electrode thickness and active material (AM) volume fraction values that maximise cell performance for slurry-cast electrodes. Finally, the study demonstrates that by patterning the electrode architecture, volumetric energy density can be significantly improved, subject to manufacturing constraints, and provides quantitative design guidelines for future study.

Batteries with high energy densities become essential with the increased uptake of electric vehicles. Battery housing, a protective casing encapsulating the battery, must fulfil competing engineering requirements of high stiffness and effective thermal management whilst being lightweight. In this study, a graded lattice design framework is developed based on topology optimisation to effectively tackle the multidisciplinary objectives associated with battery housing. It leverages the triply periodic minimal surfaces lattices, aiming for high mechanical stiffness and efficient heat dissipation considering heat conduction and convection. The effectiveness of the proposed framework was demonstrated through the battery housing design, showcasing its ability to address multidisciplinary objectives as evidenced by the analysis of the Pareto front. This study identifies the potential of lattices in lightweight applications incorporating multiphysics and offers an efficient lattice design framework readily extended to other engineering challenges.

This paper presents a physics-informed neural network (PINN)-based solution framework that predicts the thermal history during a multi-layer Directed Energy Deposition (DED) process. The meshless nature and the readily available derivative information of PINN solution opens up new opportunities for modelling the thermally induced distortion in metal Additive Manufacturing (AM). The proposed framework incorporates simple yet effective strategies that enable PINN to overcome the usual shortfall of neural networks (NNs) in dealing with discontinuities. It is a critical step for applying PINN to the multi-layer problem which intrinsically contains discontinuities due to the layer-by-layer nature of DED and other metal AM processes. The accuracy of the proposed framework is validated via a benchmark test against ANSYS simulation. Leveraging the possibility of initialisation with prior knowledge, PINN is also demonstrating potential computational time-savings, especially for larger parts. Furthermore, remarks on strategies to improve ease of training and prediction accuracy by PINN for the particular use case in DED temperature history prediction have been made. The proposed framework sets the foundation for the subsequent exploration of applying scientific machine learning (SciML) techniques to real-life engineering applications.

It has been widely recognised that making Topology Optimisation (TO) structures support-free is a key step to improving their manufacturability. However, current numerical methods for support-free TO are typically extremely time-consuming. This work demonstrates the potential of using conditional, deeply convolutional Generative Adversarial Networks as a post-processing step to produce the support-free variant of a given TO design that has a high stiffness-to-weight ratio similar to the output from the lengthy, iterative numerical method. It is accompanied by an investigation of the post-processing strategy for outputs generated with a machine-learning-based method.

Lattice structures, which benefit from the high manufacturing flexibility of Additive Manufacturing (AM) techniques, have been applied in diverse scenarios, especially within aerospace. To further exploit the potential of lattice structures, Machine Learning (ML) is employed to assist with the design of functionally-graded lattices in this paper. A ML-based inverse lattice generator is trained to output lattice unit cells from the input of target mechanical properties. Using this inverse generator, a novel lattice structure generation method is proposed with the result based on low-resolution Topology Optimization (TO). The proposed strategy has proven to be superior to conventional density-based lattice generation methods and have high performance close to the computational-expensive high-resolution TO results. The strategy can serve as a feasible design tool when facing large-scale real-world structural design problems.

We use three-dimensional printing to manufacture lattices with uniform and graded relative density, made from a composite parent material comprising a nylon matrix reinforced by short carbon fibers. The elastic-plastic compressive response of these solids is measured up to their densification regime. Data from experiments on the lattices with uniform relative density are used to deduce the dependence of their elastic-plastic homogenized constitutive response on their relative density, in the range 0.2-0.8. These data are used to calibrate finite element (FE) simulations of the compressive response of functionally graded lattices (FGLs), which are found in good agreement with the corresponding measurements, capturing the salient features of the measured stress versus strain responses. This exercise is repeated for two lattice topologies (body-centered cubic and Schwarz-P). The phenomenological constitutive models produced in this study can be used in topology optimization to maximize the performance of 3D-printed FGLs components in terms of stiffness, strength, or energy absorption.

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.

Abstract Additive manufacturing (AM) provides exceptional design flexibility, enabling the manufacture of parts with shapes and functions not viable with traditional manufacturing processes. The two paradigms aiming to leverage computational methods to design AM parts imbuing the design-for-additive-manufacturing (DFAM) principles are design optimization (DO) and simulation-driven design (SDD). In line with the adoption of AM processes by industry and extensive research efforts in the research community, this article focuses on powder-bed fusion for metal AM and material extrusion for polymer AM. It includes detailed sections on SDD and DO as well as three case studies on the adoption of SDD, DO, and artificial-intelligence-based DFAM in real-life engineering applications, highlighting the benefits of these methods for the wider adoption of AM in the manufacturing industry.

Conventional wind turbine blade manufacture relies on large, expensive moulds. Instead, using additive manufacturing to print the internal structure of blades, upon which it would be possible to lay composite plies, could significantly reduce manufacturing costs and, as one could “3D print” topologically optimal designs, improve structural efficiency. In general, topology optimisation integrates well with additive manufacturing. There are, however, two main challenges associated with the adoption of topology optimisation in wind blade design, i.e. accounting for: (i) the aeroelastic response of blades; and (ii) the variety of different materials that would be employed, in the composite laminates as well as the printed structure. To address these challenges, the present paper proposes a new multi-step design and optimisation framework relying on the combination of three software. First, a conventional aero-servo-elastic model is used to evaluate blade loads and displacements. Next, a topology optimisation software is used to optimise the blade laminates and core structure. Third, a lattice generator is used to convert the topological optimised “grey” design into an equivalent cellular design that can be printed using additive manufacturing. The full methodology of this design framework and an initial proof-of-concept topology optimisation solution are presented in this paper.