This paper suggests that analysing the effect of visualisation technologies during design reviews should include variables related to design reviewers’ expertise and focus on the process variables rather than the outcomes. The experiment showed better averages in terms of design understanding for groups in desktop interface than for groups in virtual reality. However, the observed difference might also be due to experience with the technology. Finally, regardless of the visualisation technology, higher expertise group showed consistently better design understanding than lower expertise groups.

The development of cyber-physical systems requires close cooperation between stakeholders from different disciplines. Model-based systems engineering support this by the design of a system model. Non-identified domain knowledge by the stakeholders is a challenge when creating the system model. The CONSENS 3D-Modeling Method supports the domain-independent elicitation of domain knowledge using a 3D environment and enables the derivation of a SysML system model. We applyed the method by implementing a prototype, called 3D Engineer, to an application example from the automotive industry.

Sketching and prototyping are parts of a ‘reflective conversation with materials of a design situation’ (Schön, 1992). To support this conversation, we developed a reflective tool -the Reflection Canvas- that facilitates reflection activities through sketching and prototyping on the one hand and verbalisation on the other. We introduced the reflective tool to design students. Based on observation and answers from a questionnaire data reveal that guided reflection structured the process in a helpful way. It also turned out students had difficulties to switch from visualisation to verbalisation.

The capacity of building services in many hospitals exceeds the requirements by significant amounts. Oversizing of building services has a direct impact on building efficiency and operational costs, ultimately impacting upon patient care, by diverting much needed funding. A key factor leading to the oversizing is the excessive and uncoordinated application of design margins across various project stages. Based on a hospital case study, this paper analyses the reasons for the overdesign of a replacement cooling system and raises the importance of managing margins activity to avoid overdesign.

The advancements in the field of VR allow designers to use VR as a technology for sketching ideas in a virtual space. In this paper, Gravity Sketch (a VR sketching software) is used as an ideation sketching tool in the process of product styling. The goal of this research is to examine the advantages and points-of-pain when using virtual reality tools in the process of product styling. 29 Master students used the VR sketching tool in their design process and all of them had feedback and insights on the tool.

The terms IoT and Industry 4.0 are promising increasingly sophisticated solutions, but the realisation will depend on the inclusion of robust and reliable sensors. If the gathered data is flawed or inaccurate the performance of the whole system will be compromised. By reviewing research on robustness indicators, mechatronics and sensor properties as well as listing mechanical noise factors and providing an electromechanical trade-off example, the paper highlights the importance of considering both mechanical and electrical noise factors and robustness in early development of connected devices.

The multidisciplinary nature and lack of comprehensive ‘materials-product-manufacturing’ knowledge of Functionally Graded Additive Manufacturing (FGAM) require training to support the future Additive Manufacturing experts. INEX-ADAM, an EU funded project is building a transnational platform to promote FGAM. Brunel University London conducted two-day workshop on FGAM at the University of Zagreb in Croatia with academics and industry professionals. The workshop will strengthen the research capabilities to harness the potential of the FGAM and mitigate the constraints to industrial applications.

Boundaries between products and services vanish and companies increasingly offer hybrid solutions known as Product Service Systems (PSS). Thereby, a holistic view that includes both the product and service parts must be taken. This paper presents a discrete-event simulation of a PSS as a method for the specification of the product part. Besides product and service, the application scenario and the decision-making are also modeled. Based on the results for a case study, the customer requirements could be guaranteed as the essential product specifications were optimized to minimize total costs.

Early integration of sustainability considerations into decision making is seen as a key enabler for companies to understand the potential implications of their decisions on the triple bottom line aspects. Lack of the tools to support decisions when trade-off between sustainability aspects occur, however, may lead to uninformed decision-making and undesired outcomes. By consolidating the learnings from empirical work together with literature recommendations, we propose key criteria to be considered when developing decision support tools to manage sustainability-related trade-off situations.

The maintenance of an industrial plant is mainly based on analogue and manual methods and processes. In the future, the maintenance data of the real object should be determined digital and transferred to a PDM system in real time. This is made possible with a digital method of component identification. This publication presents a system-based component identification using Mixed Reality. Coordinates are determined by the terminal device and compared with a reference structure. After successful identification, maintenance data can be determined and automatically transferred to a PDM System.

This study aims at proposing an AI-agent model that helps designers to understand and interpret clients’ intent and create customized design outcomes. We recorded and analysed the purchasing process of collecting product information, selecting alternative products and making decision to understand the influence of artefacts on the client's intent during the purchasing process and collect the basic data for proposing an AI-agent model. We analysed the process by Linkography and found the relationship between the decision-making patterns and the artefacts that presented by the AI-agent.

Current geometrical modelling approaches are unable to handle complex geometrical objects such as heterogeneous lattice structures. In this work, a framework for a novel bio-inspired geometric modelling method is proposed. The method can potentially support geometric modelling of heterogeneous lattice structures. The method utilises discretisation algorithms that are based on cell division processes encountered in nature. The method is verified on two 2D use-cases.

In the domain of optical engineering, optomechatronic systems are predominantly developed using conventional ray tracing methods such as sequential and non-sequential ray tracing. However, the increasing complexity of these systems in combination with the demand for high efficiency and high image quality leads to the fact that conventional methods to develop these systems reach their limits. In order to be able to develop highly efficient systems with high image quality, this contribution introduces a hybrid ray tracing method using an advanced optimization function.

The main design activity in engineering practice is to adapt existing designs or to create variants of existing products to new demands, which require a robust model against both parametric and topological changes. To design such a kind of model becomes a big challenge, especially in the development of structural components due to the number of load application points, variable load cases and restrictions from manufacturing technologies. Thus, the generative parametric design approach is applied to generating high dynamic product models, allowing them to be adjusted for changes feasibly.

This paper describes an approach for designing lightweight components produced through additive manufacturing (AM). Lightweight design is often done through topology optimization (TO). However, the process of manually interpreting mesh-based and imprecise results from a TO into a geometry that fulfils all requirements is complex. To aid in this process, this paper suggest an approach based on combining overhang-constrained TO with lattice-based TO to automate complex tasks, retain parametric control, and to minimize manufacturing cost. The approach is validated through a benchmark part.

Engineering work is mostly done in 3D CAD software throughout the engineering process from conceptual design and layout of products. Physics-Based Virtual Prototypes are very valuable addition on Computer Aided Engineering enabling product development simulators, training simulators and digital twin concept in product lift-cycle process. In this work, we present a framework, how such virtual prototypes can be developed from 3D CAD models with meaningful effort.

With industries striving towards increased customisation of complex products through engineer-to-order, methods are continuously sought to rationalise the product development process. To this end, a framework is proposed using CAD configurators, utilising design automation and knowledge-based engineering to integrate sales and design processes in product development. The application of this framework to the design of spiral staircases is described and analysed, with results showing decreased lead-times and a decreased risk for design errors.

Human designers often work in a visual design space, projecting step-by-step design progression through evolving mental images. The strategic evolution of that design leverages heuristics based on experience and domain knowledge. The methodology presented in this paper brings together the visual nature of design problem solving and design heuristics in a deep learning computational agent framework that emulates and enables human-mirrored design. When applied to a truss design task, results demonstrate superior results to those of human designers who provided the initial data.

Computer tools are commonly used to assess designs. We develop a toolchain using open source code libraries in Python to provide an open source, interactive robust design improvement toolchain. A reference folder contains a script that reads an input parameter value file and runs the simulation. The toolchain executes uncertainty quantification steps by replicating the reference folder. This is repeated for design points, and mean and sigma graphs generated versus each design variable. This fits within a workflow of defining variation modes, design variables, and toolchain execution.

Modern machine learning methods have the potential to supply industrial product lifecycle management (PLM) with automated classification of product components. However, there is only little work in the literature on this topic. We propose to apply supervised machine learning on component meta-data. By analysing an industrial case study, we identify requirements and opportunities for automating classification, e.g. in part numbers and product structures. We validate our novel approach through a classification experiment comparing four machine learning methods on a realistic component dataset.