Visualisation of ideas and emergent designs is an essential ingredient in design practice. Sketching and CAD represent two widely used visualisation tools, each with complementary affordances that dictate their typical use during the design process. Sketching has affordances of fast and fluent visualisation whereas CAD affords easy modification of detailed designs. This paper proposes a hybrid tool, Digital Sketch Modelling, investigating the extent to which it can deliver complementary affordances of both sketching to CAD. Analysis of diary entries made by 62 postgraduate designers using sketching, digital sketch modelling and CAD within a design project forms the basis of the study. Results illustrate how digital sketching over crude 3d digital models, combined with benefits of digital image editing software enhance affordance for easy visualisation of ideas. Concurrently, the level of software used in Digital Sketch modelling led to fewer concerns over the level of difficulty to modify designs, enhancing the affordance for easy modification. As such we conclude Digital Sketch Modelling does combine affordances indicating its potential benefit in use between sketching and CAD.

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.

A refrigerant system (like that of a supermarket) is a complex system if we consider all the stakeholders throughout its lifecycle phases (use, maintenance, technological update, end of life). The lack of stakeholders' interaction during the design and other lifecycle stages of such a system generates issues and leads to sub-optimal system performances. We used the RID methodology to identify the main areas for improvement for these activities related to the refrigerant system. It is precisely designed to analyze, within the scope of activity, the major stakeholders' problems (user profiles) during lifecycle phases (use situations) to deduce areas for improvement (value buckets). Therefore, we built a process of interviews and data collection on existing practices to feed into a RID model. The first results are an archetypal description of the actors and problems encountered according to the lifecycle phases. The second part is a prioritized mapping of the areas to improve despite a certain number of known available solutions but proven insufficient.

Requirements represent a central element in product development. The large number of requirements inevitably results in an increased susceptibility to errors, an expenditure of time and development costs. The associated problems motivate the application of Artificial Intelligence in the form of Natural Language Processing (NLP). In Requirements Engineering one main task is the classification of requirements which serves as the input in architectural models e.g. in SysML. In mechanical engineering there is still little overview regarding the interface between requirements classification and modelling. This paper provides an overview of the requirement classes and entities used in the literature and analyses their utilisation in modelling. Existing requirements classes usually do not offer the flexibility to be transferred to other domains. However, basic structures can be adopted from those classifications. This enables a clear assignment of existing classes to object classes in modelling. Resulting from the conducted literature study the observed predominant focus of research on the software industry requires an extension of the existing requirement classes and entities to enable further use and transfer to mechanical engineering.

The control shifting between a human driver and a semi-autonomous vehicle is one of the most critical scenarios in the road-map of autonomous vehicle development. This paper proposes a methodology to study driver's behaviour in semi-autonomous driving with physiological-sensors-integrated driving simulators. A virtual scenario simulating take-over tasks has been implemented. The behavioural profile of the driver has been defined analysing key metrics collected by the simulator namely lateral position, steering wheel angle, throttle time, brake time, speed, and the take-over time. In addition, heart rate and skin conductance changes have been considered as physiological indicators to assess cognitive workload and reactivity. The methodology has been applied in an experimental study which results are crucial for taking insights on users’ behaviour. Results show that individual different driving styles and performance are able to be distinguished by calculating and elaborating the data collected by the system. This research provides potential directions for establishing a method to characterize a driver's behaviour in a semi-autonomous vehicle.

The comprehensive implementation of digital technologies in product manufacturing leads to changes in engineering processes and requires new approaches to data management. An important role belongs to the processes of organizing the collection, storage and reuse of research data obtained and used in the process of product, system or technology development, taking into account the FAIR data principles. This article describes a Research Data Management System for the organization of documentation and measurement requests in the research and development of new oxygen-free production technologies.

Prototyping is a well-established and valued design process activity. However, capturing prototypes and the tacit knowledge that led to and was gained from their creation is a challenge. Beyond that, questions remain on how best to utilise that captured data. This paper looks at how one can exploit and generate insights from data that has been captured, specifically looking at graph databases, the network analysis techniques they permit and the differing fidelities of visualisation and interactivity that they enable.

Among the increasing number of researches about design thinking, several studies, empirically investigate the report between design process and different sources of inspiration. Visualization of Images represents one of the most current stimuli in the architectural design.

This work focuses on the link between the active part of design process and images of precedents when visualized by the designer at the beginning of his design activity. It aims to identify and measure the impact of such visualization on the cognitive process of ideation.

We use the protocol analysis method. Data are collected through design experiment and coded by the semio-morphic coding scheme. Results show that the visualization of images of precedents enhances the productivity of the ideation process. The process consistency is also improved by the apearence of homogeneous phases. Moreover the ideation process becomes more creative cognitively, by making the genesis of primitive chains of actions faster, easier and similar.

Accordingly, this paper communicate the effect of a common practice such images’ visualization on the architectural design process to get insight on the cognitive befits of this practice.

In the realm of process engineering, the pursuit of sustainability is paramount. Traditional approaches can be time-consuming and often struggle to address modern environmental challenges effectively. This article explores the integration of generative AI, as a powerful tool to generate solution ideas and solve problems in process engineering using a Solution-Driven Approach (SDA). SDA applies nature-inspired principles to tackle intricate engineering challenges. In this study, generative AI is trained to understand and use the SDA patterns to suggest solutions to complex engineering challenges.

The work introduced two novel multiscale multifunctional tire designs developed using the Domain Integrated Design (DID) method and modelled with the LatticeQuery geometric modelling software. Furthermore, this research validates the meta-level parameter “interaction area” proposed for selecting biological analogy in the DID method. These two use cases were simulated with Abaqus. The concepts covered in this work are an example of multi-functional design. The obtained results validate the meta-level parameter derived from the DID methodology.

To respond to today's needs, engineers must be able to develop sustainable and environmentally compatible products and systems. To do so, they have to carry out new or changed activities and tasks within the product development process and therefore have to obtain new or changed competences. This publication examines which specific competences from the competence groups system thinking and communication are especially important for the development of Ecodesign products apart from technical know-how and should thus be included in a future higher education engineering course.

During engineering design, designers employ three types of model: physical, virtual and cognitive. The role and contribution of each is documented in literature albeit fragmented in nature. Consequentially, a gap in understanding exists in terms of how these models and the transitions between them impact the designer and design process. This paper begins to address this through a characterisation of each model class and an appraisal of the transitional pathways including their alignment to seminal design frameworks.

Physical prototyping during early stage design typically represents an iterative process. Commonly, a single prototype will be used throughout the process, with its form being modified as the design evolves. If the form of the prototype is not captured as each iteration occurs understanding how specific design changes impact upon the satisfaction of requirements is challenging, particularly retrospectively.

In this paper two different systems for digitising physical artefacts, structured light scanning (SLS) and photogrammetry (PG), are investigated as means for capturing iterations of physical prototypes. First, a series of test artefacts are presented and procedures for operating each system are developed. Next, artefacts are digitised using both SLS and PG and resulting models are compared against a master model of each artefact. Results indicate that both systems are able to reconstruct the majority of each artefact's geometry within 0.1mm of the master, however, overall SLS demonstrated superior performance, both in terms of completion time and model quality. Additionally, the quality of PG models was far more influenced by the effort and expertise of the user compared to SLS.

Commercially available metal Laser Powder Bed Fusion (L-PBF) systems are steadily evolving. Thus, design limitations narrow and the diversity of achievable geometries widens. This progress leads researchers to create innovative benchmarks to understand the new system capabilities. Thereby, designers can update their knowledge base in design for additive manufacturing (DfAM). To date, there are plenty of geometrical benchmarks that seek to develop generic test artefacts. Still, they are often complex to measure, and the information they deliver may not be relevant to some designers. This article proposes a geometrical benchmarking approach for metal L-PBF systems based on the designer needs. Furthermore, Geometric Dimensioning and Tolerancing (GD&T) characteristics enhance the approach. A practical use-case is presented, consisting of developing, manufacturing, and measuring a meaningful and straightforward geometric test artefact. Moreover, optical measuring systems are used to create a tailored uncertainty map for benchmarking two different L-PBF systems.

An important step when designers use machine learning models is annotating user generated content. In this study we investigate inter-rater reliability measures of qualitative annotations for supervised learning. We work with previously annotated product reviews from Amazon where phrases related to sustainability are highlighted. We measure inter-rater reliability of the annotations using four variations of Krippendorff's U-alpha. Based on the results we propose suggestions to designers on measuring reliability of qualitative annotations for machine learning datasets.

Companies offer products in different variants to reach more customers. This increases internal variety and cost. However, reducing those cost is difficult due to complexity. Complexity arises from: combinatorics; many design variables interacting with each other; coupling of technical and economical perspectives. This paper presents an approach based on (1) building a complex system model of modular models; (2) identifying the potential for standardization from a technical perspective; (3) cost-optimizing the degree of standardization. A product family of electric vehicles was optimized.

Laser Powder Bed Fusion is the most widespread additive manufacturing process for metals. In literature, there are several analytical models for estimating the manufacturing cost. However, few papers present sensitivity analyses for evaluating the most relevant product and process parameters on the production cost. This paper presents a cost model elaborated from previous studies used in a sensitivity analysis. The most relevant process parameters observed in the sensitivity analysis are the 3D printer load factor, layer thickness, raw material price and laser speed.

Scenario planning is often used in design practice to foster futures thinking, reduce uncertainties and improve decision-making. Scenarios are especially relevant for innovation activities in manufacturing companies, such as technology development, a particularly uncertain process where many trade-offs occur. This study is, to the authors knowledge, the first to empirically measure the effect of scenarios in decision-making quality, in the context of technology development. In a quasi-experiment, engineers from a manufacturing company and university students were independently asked to analyse a trade-off situation between environmental and financial aspects of a technology concept, with and without scenarios. The quality of decision-making quality for control and experiment groups was measured through a standardized questionnaire. The results show that scenarios had a positive impact in 6 of the 7 quality decision-making practices (QMDP), although the effect size is small. The results suggest that both expert and novice designers may benefit from using scenario planning when performing early-stage design activities by having awareness of the decision context, a more structured decision process, and clearer decision criteria.

The biggest advantage of Additive Manufacturing is the individualization of products. Mass Customization is well known as a promising future application. The use of Additive Manufacturing for assembly groups is mostly not reasonable, however combining it with conventional manufacturing processes can lead to new opportunities.

This paper works out concepts to join, by using similar material combinations, an injection molded part with an additive deposited geometry by the Fused-Deposition-Modeling (FDM) process. Therefore, two of the main industrially used polymers, acrylonitrile butadiene styrene (ABS) and polypropylene (PP), are selected for further study. In particular, this investigation focuses on the procedural potentials and surface preparation of the injection molded part. By the variation of adhesive bonding, the fusion of similar materials can be identified and tested in several series of testing.

First in general a direct joining function by the FLM process will be tested. After proving this hypothesis, the results will be summarised in a recommendation of joining similar materials, which are manufactured in different ways.

The amount of information in our society and its opportunities have given rise to Big Data research. The systems supplier industry needs suitable tools and methods to ensure the harvest and utilization of Big Data in their product development. This paper used Systems Thinking to analyze the current state in the industry and suggested leverage points for further research direction. The findings suggest that the research project should emphasize the industry cases, the collaboration between the companies and academia, develop a Big Data systems architecture, and maintain a socio-technical view.