Advanced Driver-Assistance Systems were created to address the driver's failures. All these ADAS are a part of the evolution of the vehicles towards whole automation. To complete its launch in the automotive market, autonomous vehicles have to pass safety tests to acquire the consumers’ trust. To receive the approval of the public, the self-driving car has to take into account the human feeling. The risk perceived by the driver is one of the new emotional form to integrate at the validation plan. The purpose of this study is to examine the perception of the risk of a self-driving car's driver.

Free contributors have successfully shown the potential in large/complex software co-creation in the Free and Open Source Software Movement, triggering many discussions and exploration ventures from academia to industry and to the government. Though many research efforts explored whether the same level of co-creation efforts could take place broadly in the hardware realm, very few research studies focus on profit-seeking hardware projects initiated by companies. In fact, the specific nature of being tangible and profitable makes company-led open source hardware projects suspicious to be really “open” to contributors. Community has been identified as the critical driver in many open projects. By reviewing the evolution of company-community interactions over time and different community behaviors in different open development context, authors in this paper hope to identify best community- company interaction forms for open source hardware companies. Using grounded theory and case studies, we construct a framework to describe and identify company community's different behaviors and different roles.

The application of agile development methods in response to increasing market dynamics and product complexity is a key lever in the automotive industry. Agile methods originally come from the software industry and enable fast, flexible and customer-oriented product development. These methods are also increasingly being used in hardware development. However, the evaluation of the benefits of agile methods in the context of automotive development has been primarily subjective. The publication aims to present a first data-based approach to objectify the benefits of agile methods in automotive development by highlighting the effects in the quality of collaboration within teams. A standardised procedure is therefore designed and presented. On the one hand, a model for measuring the agile maturity of teams is described. On the other hand, the quality of collaboration within a team is examined in different aspects using standardised key performance indicators. Based on the proposed procedure, a strong positive correlation was found between the considered key performance indicators of the quality of collaboration and the agile maturity of the development teams within the investigated organisation.

Today, information and knowledge as competitive factors influence the success of companies as much as traditional production factors like human resources or physical capital. However, the reuse of design knowledge still represents a major challenge for engineering organizations. That is, because barriers exist hindering a successful knowledge reuse. On the basis of a literature review, the research depicted in this paper analyses the relation between single information conveying design knowledge and barriers hindering a successful knowledge reuse. Developing a model-based approach, we propose a micro logic containing three steps and underlying methods enabling practitioners to identify situation-specific barriers within their organization. We illustrate the industrial application of the approach in a case study at a mining machinery OEM.

In the last few years, the growing need of highly reliable and time-effective strategies to perform preliminary design of complex systems has led industries to adopt the Model Based System Engineering (MBSE) approach. In MBSE, systems are split into multiple sub-systems and the relevant physical phenomena are described via analytical or numerical models. When a significant number of design variables are to be considered, a smart approach to reduce the number of analyses to perform would be to make use of the Global Sensitivity Analysis (GSA) to higlight those variables that have a more significant influence on the system output. Moreover, an even more significant reduction of computational cost to perform the GSA can be achieved if the complex system modelled via the MBSE approach is exported under the Functional Mock-Up Interface (FMI) norm. In this context, this paper proposes an original approach to address the study of two constructive solutions of an acceleration measuring device typically used on airbags for which the use of a new solution characterized by a porous material is compared with a classical one.

Lattice structures are lightweight and possess other unique mechanical and physical properties. Additive manufacturing techniques are often used to produce these structures. Additive manufacturing provides manufacturing freedom that significantly surpasses the one provided by conventional subtractive manufacturing. However, a gap exists between the manufacturing freedom and the geometric modelling freedom in additive manufacturing: it can be extremely challenging to model the designed part because of its high geometric complexity, such as heterogeneous lattice structures. While several tools on the market allow geometric modelling of such structures available on the market, the customization of lattice parameters can still be significantly improved. Moreover, no open-source tools exist to address this issue or to model lattice structures in general. This work presents a novel open-source library for the geometric modelling of lattice structures with customized parameters. The parameter customization is enabled with the function representation approach.

Patent analysis is a popular topic of research. However, designers do not engage with patents in the early design stage, as patents are time-consuming to read and understand due to their intricate structure and the legal terminologies used. Manually produced graphical representations of patent working principles for improving designers’ awareness of prior art have been demonstrated in previous research. In this paper, an automated approach is presented, utilising Natural Language Processing (NLP) techniques to identify the invention working principle from the patent independent claims and produce a visualisation. The outcomes of this automated approach are compared with previous manually produced examples. The results indicate over 40% match between the automatic and manual approach, which is a good basis for further development. The comparison suggests that the automated approach works well for features and relationships that are expressed explicitly and consistently but begin to lose accuracy when applied to complex sentences. The comparison also suggests that the accuracy of the proposed automated approach can be improved by using a trained part-of-speech (POS) tagger, improved parsing grammar and an ontology.

In order to efficiently design and deliver customized products, it is crucial that the process of translating customer needs to engineering characteristics and into unique products is smooth and without any misinterpretations. The paper proposes a method that combines design optimization with value-driven design to support and automate configuration of customized products. The proposed framework is applied to a case example with spiral staircases, a product that is uniquely configured for each customer from a set of both standard and customized components; a process that is complex, iterative and error-prone. In the case example, the optimization and value-driven design models are used to automate and speed-up the process of delivering quotations and design proposals that could be judged based on both engineering characteristics as well as their added value, thereby increasing the knowledge at the sales stage. Finally, a multi-objective optimization algorithm is employed to generate a set of Pareto-optimal solutions that contain four clusters of solutions that dominate the baseline design. Hence the decision-maker is given a set of optimal solutions to choose from when balancing different economical and technical characteristics.

Geographic distance between supply and demand reduces spatial accessibility. Vehicles have been contributing to improved spatial accessibility by offering transport services for people and goods. From traditional trucks to mobile health clinics, vehicles can offer a wide range of functionalities on wheels. To develop context-specific vehicles-based services, we propose a novel engineering framework based on a supply chain perspective. Requirements are derived from supply chain stakeholders and translated into vehicle-based services and vehicle components.

Engineering design is typically a collaborative process, and in the era of digital engineering, online collaboration platforms are increasingly being used to perform the work. Despite the development of e-collaboration technologies, there is a significant gap between actual collaboration and what is really needed. However, improving collaboration requires a proper measurement system. Yet, the common methods to measure and improve collaboration are challenging, usually not compatible with digitalized collaboration, and have limited scalability. This paper presents a new data-driven method for measuring, visualizing, and monitoring Active Engagement (AE) in web-based teamwork, which is a key element of effective collaboration. We applied the method in a case study of four engineering teams during a Technology Planning and Road-mapping course. The results suggest that measuring AE in web-based teams, with an available history log, is technically feasible and can meaningfully represent the team's collaboration. The presented approach can be used to upgrade e-collaboration platforms as a toolkit or for further investigation on improving web-based collaborative design and learning through monitoring dashboards and feedback systems.

This paper proposes a method to automatically generate a multi-domain matrix (MDM) from textual activity specifications. The format for specifying these activities is based on a structured grammar derived from natural language and consists of two types of activities: goal activities and transformation activities. A goal activity describes the purpose of an action performed by an actor for the benefit of another actor in the system. A transformation activity describes an activity from the viewpoint of a single actor, who receives, generates, and outputs information or artifacts. If one describes activities using these two types of activity specifications, dependencies can be automatically derived between actors, activities, and parameters of the system and visualized in an MDM. Thus the generated MDM presents an organization DSM (actors), a process DSM (activities), and a parameter DSM (flows of information or objects), as well as the mapping matrices coupling the different domains. An illustrative house construction example demonstrates the effectiveness of the proposed activity specification format. The method may provide an outcome in understanding and managing complex systems.

Design is multi-modal, and depending on the current stage in the process, progress can be facilitated through working in either the physical or virtual domain with frequent iterations commonly required between. Traditionally, prototyping workflows are sequential, although current trends such as Digital Twinning and Mixed Reality (MR) enable decreased domain transition times, reducing the cycle time. This leads towards fully integrated digital-physical prototypes, enabling work in both domains simultaneously by increasing synchronicity of select variables. This paper considers those variables involved, the sensors that measure them and their rate of synchronisation, thereby investigating the feasibility of MR workflow interventions, and exploring the benefits that may be realised. The paper identifies four components of MR implementations in prototyping and myriad methods by which domain transition may occur and uses these in context of a case study to propose four levels of workflow synchronisation. It was found achieving some high rates of synchronicity is possible, but achieving the highest levels as prescribed by digital twinning is neither feasible nor pragmatic against current MR capabilities and design prototyping workflows.

Spatial Augmented Reality (SAR) differs from other forms of AR by allowing the projection of digital images onto a model. This allows the AR to be more tangible and for interaction to be more realistic. The scale of the model plays a role in the realism but may be constrained by technical factors. This study attempts to understand the influence scale has on a design session by analysing the concept generation process, the ease of designing and the design behaviour. Understanding how these factors are influenced by the model scale betters the understanding of how SAR can influence design.

As product, production systems and material strongly influence each other, an integrated view on these domains offers huge potentials during development in order to adjust characteristics being mutually dependent. Considering manufacturing aspects within the design process certainly constitute a widespread approach to make sure that the products can be produced to predefined costs and quality.

As product characteristics are realised through material characteristics and manufacturing processes, material aspects need to be integrated into this view. Moreover, different geometries have to be assembled in order to create particular structures, why joints devote a special attention.

For this reason, a definition approach that integrates product, production systems, material plus joints and considers the ecological performance apart from the regular technical and economic aspects offers a huge potential for successful future solutions. This paper reviews relevant development approaches as well as supporting IT tools in the different domains. As a result, an extendedly integrated view is introduced and a method that supports the integrated selection of solutions regarding a technical, economic and ecological performance is introduced.

Prototypes are a critical aid in the product development process, allowing designers translate concepts to reality. However, the execution of prototypes depends heavily on the designer experience, evidenced in research in the need of creating design support tools to establish a standard for a prototyping effort. To improve on these findings, the Proto-Arch is introduced as a partial result of creating a standard for executing prototypes in each product development phase based on the prototyping roles and purposes. Proto-Arch defines the prototyping activities in the embodiment design phase.

Introducing more customized products causes complexity costs, which are hard to estimate and measure. Researchers and practitioners have therefore developed approaches to measure these costs. This paper reviews the current approaches and reveals a lack of a holistic view on the underlying problem and, hence, deficits to certain economic questions. We therefore suggest using the Extended Axiomatic Design (EAD) framework as an interdisciplinary model that adresses the economic consequences of variety-induced complexity.

This paper investigates the use of ANNs to model human behaviour in design by comparing the predictive capability of ANNs and engineering students. Function structure models of 15 products are used as input for prediction. The type of information provided varied between topology and vocabulary. Analysis of prediction accuracy showed that ANNs perform comparably to students. However, students are more precise with their predictions. Finally, limitations and future work are discussed, with research questions presented for subsequent research.

Digital engineering is increasingly established in the industrial routine. Especially the application of machine learning on geometry data is a growing research issue. Driven by this, the paper presents a new method for the classification of mechanical components, which utilizes the projection of points onto a spherical detector surfaces to transfer the geometries into matrices. These matrices are then classified using deep learning networks. Different types of projection are examined, as are several deep learning models. Finally, a benchmark dataset is used to demonstrate the competitiveness.

In this paper, a proposal for a new method to design load-adaptive microlattice structures for PBF-LB/M manufacturing is presented. For this purpose, a method was developed to stiffen microlattice structures in particular by using self-similar sub-cells to ensure their manufacturability. The quality of the stiffness increase was investigated and verified by finite element simulations. Subsequently, the simulation results were critically discussed with respect to their potential for future design processes for architected materials.

This piece of work is concerned with how shapes are generated, explored and transformed during the architectural designing process. It postulates that the relations and connections between sketches, produced during the design activity, can be defined in terms of shape transformations and described according to a closed list of shape operators. These latters provide a formal description of the shape exploration process and allow a deep understanding of its logic. To achieve its goal, this study creates a model to describe the different shape transformations, performed by designers, during the sketching activity.