A parallel kinematic machine (PKM) topology can only give its best performance when its geometrical parameters are optimized. In this paper, dimensional synthesis of a newly developed PKM is presented for the first time. An optimization method is developed with the objective to maximize both workspace volume and global dexterity of the PKM. Results show that the method can effectively identify design parameter changes under different weighted objectives. The PKM with optimized dimensions has a large workspace to footprint ratio and a large well-conditioned workspace, hence justifies its suitability for large volume machining.

The use of carbon fibre composites is growing in many sectors but their use remains stronger in very high value industries such as aerospace where the demands of the application more easily justify the high energy input needed and the corresponding costs incurred. This energy and cost input is returned through gains over the whole life of the product, with for example, longer maintenance intervals for an aircraft and lower fuel burn. Thermoplastic composites however have a different energy and cost profile compared to traditional thermosets with notable differences in recyclability, but this profile is not well quantified or documented. This study considers the key process control parameters and identifies an optimal window for processing, along with the effect this has on the final characteristics of the manufactured parts. Interactions between parameters and corresponding sensitivities are extracted from the results.

The increasing need to understand complex products and systems with long life spans, presents a significant challenge to designers who increasingly require a broader understanding of the operational aspects of the system. This demands an evolution in current design practice, as designers are often constrained to provide a subsystem solution without full knowledge of the global system operation. Recently there has been a push to consider value centric approaches which should facilitate better or more rapid convergence to design solutions with predictable completion schedules. Value Driven Design is one such approach, in which value is used as the system top level objective function. This provides a broader view of the system and enables all sub-systems and components to be designed with a view to the effect on project value. It also has the capacity to include value expressions for more qualitative aspects, such as environmental impact. However, application of the method to date has been restricted to comparing value in a programme where the lifespan is fixed and known a priori. This paper takes a novel view of value driven design through the surplus value objective function, and shows how it can be used to identify key sensitivities to guide designers in design trade-off decisions. By considering a new time based approach it can be used to identify optimum programme life-span and hence allow trade-offs over the whole product life.

The transformation in manufacturing capability being driven by new processes, such as additive manufacturing, offers huge potential for product innovation and opportunity to create bespoke designs tailored to individual specifications or needs. However, current design systems and tools are not yet capable of fully capitalizing on these new technologies and new approaches are needed. Many current methodologies are top-down and sequential, offering limited flexibility and an overly constrained design space. Post-processing is needed to ensure that a design can be manufactured. This work presents a novel bottom-up methodology to generate designs that can be tightly integrated with the additive manufacturing environment and that can respond flexibly to changes in that environment. Focusing on overhang as an exemplar manufacturing constraint, the method engenders changes in the design either by locally adjusting the geometry to stay within limits or by adding an appropriate support structure. The method is bio-inspired, based on strategies observed in natural systems, particularly in biological growth and development. The design geometry is grown in a computer-aided design-based, bio-inspired generative design system called ‘Biohaviour’. This process is similar to plant growth, and the design’s final configuration, shape, and size are informed by both the manufacturing capability and internal design stresses. The approach is demonstrated for overhang limit and build orientation and is extensible to any general situation.

Following PRISMA-ScR guidelines, this scoping review systematically maps the landscape of Large Language Models (LLMs) in mechanical engineering. A search of four major databases (Scopus, IEEE Xplore, ACM Digital Library, Web of Science) and a rigorous screening process yielded 66 studies for final analysis. The findings reveal a nascent, rapidly accelerating field, with over 68% of publications from 2024 (representing a year-on-year growth of 150% from 2023 to 2024), and applications concentrated on front-end design processes like conceptual design and Computer-Aided Design (CAD) generation. The technological landscape is dominated by OpenAI’s GPT-4 variants. A persistent challenge identified is weak spatial and geometric reasoning, shifting the primary research bottleneck from traditional data scarcity to inherent model limitations. This, alongside reliability concerns, forms the main barrier to deeper integration into engineering workflows. A consensus on future directions points to the need for specialized datasets, multimodal inputs to ground models in engineering realities, and robust, engineering-specific benchmarks. This review concludes that LLMs are currently best positioned as powerful ‘co-pilots’ for engineers rather than autonomous designers, providing an evidence-based roadmap for researchers, practitioners, and educators.