Additive manufacturing (AM) has and continues to experience considerable market and technological growth with many forecasting a tripling in market value over the next decade. One of the primary drivers for this growth is the increased freedom afforded to the design of both the external form and internal structure of fabricated parts. This freedom presents greater opportunities in optimising a parts mechanical properties, (such as strength, stiffness and mass), which in turn leads to enhanced performance whilst potentially reducing material use and hence, environmental impact. Realising this potential will further increase the viability of AM for a greater range of engineering contexts. Correspondingly, the contribution of this paper lies in the creation and validation of a method for the topological optimisation of the infill structure of fused deposition modelled (FDM) components. The proposed method uses results attained from finite element analysis (FEA) to influence the design of the internal structure (i.e. infill) by locally varying the composition of the infill based upon the associated stress values. This paper presents and discusses the proposed method, and demonstrates the generalisability of the method through its ability to handle complex geometries and loading conditions, and manufacturing process constraints. In addition, the paper validates the method through testing of FDM beams comprised of FEA influenced and standard honeycomb infill designs undergoing four different loading scenarios. The validation reveals that a three and a half times increase in strength can be achieved where the stress profiles are well defined within the structure. In addition, the FEA-influenced beams exhibited more consistent failure mode profiles, which maybe desirable for designing parts with specific failure mode characteristics.

This review explores the intersection of Artificial Intelligence (AI) and Generative Design (GD) in engineering within the mechanical, industrial, civil, and architectural domains. Driven by advances in AI and computational resources, this intersection has grown rapidly, yielding over 14,000 publications since 2016. To map the research landscape, this review employed semantic search and Natural Language Processing, parsing 14,355 publications to ultimately select the 88 most relevant studies through clustering and topic modelling. These studies were categorised according to AI and GD techniques, application domains, benefits, and limitations, providing insights into research trends and practical implications. The results reveal a significant growth in the integration of advanced generative AI methods, notably Generative Adversarial Networks for direct design generation, alongside the continued use of genetic algorithms and surrogate models (e.g., Convolutional Neural Networks and Multilayer Perceptrons) to manage computational complexity. Structural and aerodynamic applications were the most common, with benefits including improvements in computational efficiency and design diversity. However, barriers remain, including data generation costs, model accuracy, and interpretability. Research opportunities include the development of generalisable foundation surrogate models, the integration of emerging generative methods such as diffusion models and large language models, and the explicit consideration of manufacturability constraints within generative processes.

3D modelling fulfils a critical role in research, allowing for complex cell behaviour and interactions to be studied in physiomimetic conditions. With tissue banks becoming established for a number of cancers, researchers now have access to primary patient cells, providing the perfect building blocks to recreate and interrogate intricate cellular systems in the laboratory. The ducts of the human breast are composed of an inner layer of luminal cells supported by an outer layer of myoepithelial cells. In early-stage ductal carcinoma in situ, cancerous luminal cells are confined to the ductal space by an intact myoepithelial layer. Understanding the relationship between myoepithelial and luminal cells in the development of cancer is critical for the development of new therapies and prognostic markers. This requires the generation of new models that allows for the manipulation of these two cell types in a physiological setting. Using access to the Breast Cancer Now Tissue Bank, we isolated pure populations of myoepithelial and luminal cells from human reduction mammoplasty specimens and placed them into 2D culture. These cells were infected with lentiviral particles encoding either fluorescent proteins, to facilitate cell tracking, or an inducible human epidermal growth factor receptor 2 (HER2) expression construct. Myoepithelial and luminal cells were then recombined in collagen gels, and the resulting cellular structures were analysed by confocal microscopy. Myoepithelial and luminal cells isolated from reduction mammoplasty specimens can be grown separately in 2D culture and retain their differentiated state. When recombined in collagen gels, these cells reform into physiologically reflective bilayer structures. Inducible expression of HER2 in the luminal compartment, once the bilayer has formed, leads to robust luminal filling, recapitulating ductal carcinoma in situ, and can be blocked with anti-HER2 therapies. This model allows for the interaction between myoepithelial and luminal cells to be investigated in an in-vitro environment and paves the way to study early events in breast cancer development with the potential to act as a powerful drug discovery platform.

Engineering Design Communication (EDC) is fundamental to almost all Engineering Design activities as it provides the ability for knowledge and information to be shared between engineers. It is part of ‘what we do’. This communication contains a great deal of rationale relating to the evolution of Product Development and is essential for understanding ‘why the product is the way it is’. The need to support EDC is becoming more important due to the fact that Product Development is becoming more distributed, multi-disciplinary and involving greater re-use of past designs. With the advent of Social Media (SM), it is argued that there is the technical capability to provide more effective support for EDC within a computer-mediated environment. In order to explore this potential, this paper defines the requirements for the effective support of EDC through an extensive review of the literature. It then discusses the suitability of a SM approach and then presents the theoretical foundations of a SM framework to support EDC

Dealing with component interactions and dependencies remains a core and fundamental aspect of engineering, where conflicts and constraints are solved on an almost daily basis. Failure to consider these interactions and dependencies can lead to costly overruns, failure to meet requirements, and lengthy redesigns. Thus, the management and monitoring of these dependencies remains a crucial activity in engineering projects and is becoming ever more challenging with the increase in the number of components, component interactions, and component dependencies, in both a structural and a functional sense. For these reasons, tools and methods to support the identification and monitoring of component interactions and dependencies continues to be an active area of research. In particular, design structure matrices (DSMs) have been extensively applied to identify and visualize product and organizational architectures across a number of engineering disciplines. However, the process of generating these DSMs has primarily used surveys, structured interviews, and/or meetings with engineers. As a consequence, there is a high cost associated with engineers' time alongside the requirement to continually update the DSM structure as a product develops. It follows that the proposition of this paper is to investigate whether an automated and continuously evolving DSM can be generated by monitoring the changes in the digital models that represent the product. This includes models that are generated from computer-aided design, finite element analysis, and computational fluid dynamics systems. The paper shows that a DSM generated from the changes in the product models corroborates with the product architecture as defined by the engineers and results from previous DSM studies. In addition, further levels of product architecture dependency were also identified. A particular affordance of automatically generating DSMs is the ability to continually generate DSMs throughout the project. This paper demonstrates the opportunity for project managers to monitor emerging product dependencies alongside changes in modes of working between the engineers. The application of this technique could be used to support existing product life cycle change management solutions, cross-company product development, and small to medium enterprises who do not have a product life cycle management solution.