In the product development process, digital support continues to advance. Some work steps during product development are still carried out without assistance. Sketch creation is one of these. Therefore, the content created here is rarely documented due to the effort required for digital transformation. An alternative can be sketching in virtual reality. This article explores whether 3D sketching in VR enables faster sketching and can offer the basic features of hand-drawn sketches. To verify this, a tool for 3D sketching was developed. 27 test subjects were asked to solve one out of two different design tasks using this tool. The experiments were evaluated using video coding to identify the subjects actions. The created solutions have been analyzed about quality. The study showed initial indications that sketching in VR generally enables faster processing while maintaining the same solution quality.

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.

Creativity is crucial in design. In recent years, growing computational methods are applied to improve the creativity of design. This paper aims to explore an approach to generate creative design images with specific feature or design style. A Generative Adversarial Network model is applied in the approach to learn the specific design style. The target products will be projected into the latent space of model to transfer their styles and generate images. The generated images combine the features of the specific design style and the features of the target product. In the experiment, the approach using the generated images to inspire the human designer to generate the creative design in according styles. According to the primary verification by participants, the generated images can bring novelty and surprise to participants, which gain the positive impact on human creativity.

Design thinking is a methodology that comes from the industrial design realm and is centred on culling better needs insight from users. Another popular methodology is based gaining insight on the potential of an opportunity through experimentation, testing, and iterating with users. This is commonly referred to as lean startup methods. However, from a research perspective, we still do not know the most effective way to implement these user focused design methods within the innovation process within organizations, and which aspects of the design process are the most impactful in developing new opportunities. In this research, we propose a high-level conceptual process model on how user focused design methods such as design thinking and lean startup methods can be integrated into the up-front innovation process within organizations. We review the conceptual model, associated activities, and process considerations. The article concludes with thoughts on future research.

Organizational competences are one of the main assets of companies. Models of these competences would allow for systematic reasoning for exploring technological innovations, enabled by combining and transposing organizational competences. Today, the literature linking organizational competencies to engineering design and systems engineering remains limited. In particular, a generic modelling approach for organizational competencies for engineering design and systems engineering seems to be missing, although first frameworks have been proposed for specific purposes. This paper presents a generic conceptual model of organizational competences. The objective is to link technology, product, and systems development with the corresponding organizational competencies and their future evolution in order to allow for a joint design of competencies and technologies, products, or systems. The conceptual model provides the basis for a competence combination framework which allows for modeling competence combinations in an organization. Finally, we validate our conceptual model using a case study from the automotive industry.

The importance of considering disturbance factors in the product development process is often emphasized as one of the key factors to a functional and secure product. However, there is only a small number of tools to support the developer in the identification of disturbance factors and none of them yet ensures that the majority of occurring disturbance factors is considered. Thus, it is the aim of this contribution to provide a tool in form of a control list for the systematic identification of disturbance factors. At the beginning of this contribution, the terms “disturbance factor” and “uncertainty” are defined based on a literature review and different approaches for the classification of uncertainty are presented. Subsequently, the fundamentals of multipole based model theory are outlined. Moreover, a first approach in terms of a control list for a systematic identification of disturbance factors is discussed. Based on the discussed approach and taking the identified weaknesses as a starting point, a control list is presented that combines the existing basic concept of the control list with the fundamentals of multipole based model theory.

User experience (UX) focused business needs to survive and plan its new product development (NPD) activities in a highly turbulent environment. The latter is a function of volatile UX and technology trends, competition, unpredictable events, and user needs uncertainty. To address this problem, the concept of design roadmapping has been proposed in the literature. It was argued that tools built on the idea of design roadmapping have to be very flexible and data-driven (i.e., be able to receive feedback from users in an iterative manner). At the same time, a model-based approach to roadmapping has emerged, promising to achieve such flexibility. In this work, we propose to incorporate design roadmapping to model-based roadmapping and integrate it with various user testing approaches into a single tool to support a flexible data-driven NPD planning process.

To design a more robust artifact, an artifact design based on a design rationale analysis is pivotal. Errors in previous design rationales that led to the degradation of artifact robustness in the past provide valuable knowledge towards improving the robust design. However, methods for exposing and analysing errors in design rationale remain unclear. This paper proposes a structured method for a design rationale analysis based on logical structuring. This method provides a well-constructed means of identifying and analysing errors in design rationale from the perspective of knowledge operation.

With the quest for enhancing competitive position, fulfilling customer and sustainability demands, increasing profitability, asset manufacturing companies are now adapting assets towards product service systems (PSS) offered through performance contracts. Despite several benefits, the shift to performance PSS exposes industrial asset manufacturers' to performance challenges and risks. Currently, PSS designers face a challenge to exhaustively identify potential failures during PSS development. Knowledge of Product failures is critical prior to the engineering of PSS. This paper proposes a failure modes and effects analysis (FMEA) method to support designers' prioritise critical failures in performance PSS development. A case study of an optical sorting machine is used to demonstrate the method's application.

The recognition of the value of design has resulted in an increased number of programs and courses that include design and evaluate design competencies. However, there is no common reference system to (1) identify and assess the design competency of learners and the level of design competency aimed for by a course or curriculum; (2) universally recognize design competencies and competency levels.

Our research goal is to identify and define distinct levels of design competency and develop a framework to help instructors, design learners, institutes as well as employers assess and/or recognize competency. This paper introduces our DesCA (Design Competency Assessment) framework and places it in the context of other frameworks. We describe how DesCA helps: (1) identify and assess design competencies associated with different design activities planned for a course or curriculum; (2) formulate learning outcomes and select appropriate competency levels, methods and tools; (3) plan and develop the design content of courses and curricula; (4) ensure curricular consistency across courses.

The vision is to make DesCA a digital platform that can serve as an international standard for design teaching, learning and curriculum development.

The impact of products is becoming a topic of concern in society. Product impact may fall under the categories of economic, environmental, or social impact and is defined by the effect of a product on day-to-day life. Design teams lack sufficient tools to predict the impact of products they are designing. In this paper we present a framework for the prediction of product impact during product design. This framework integrates models of the product, scenario, society, and impact into an agent-based model to predict product impact. Although this paper focuses on social impact, the framework can also be applied to economic or environmental impacts. An illustration of using the framework is also presented. Agent-based modeling has been used previously for adoption models, but it has not been extended to predict product impact. Having tools for impact prediction allows for optimizing the product design parameters to increase potential positive impact and reduce potential negative impact.

Start-ups tend to form with a central idea that differentiates them from their competitors in the market. It is crucial for them to efficiently transform the idea into a marketable product. Prototyping helps to iteratively achieve a minimum viable product and plays a crucial role by enabling teams to test their ideas with limited resources early on. However, the prototyping process may have wrong focus leading to a suboptimal allocation of resources. Previously, we proposed role-based prototyping for fuzzy front-end development in small teams. It supports (1) resource allocation, (2) the definition of responsibilities, and (3) structuring the development process with milestones. In recent research this was a promising yet incomplete approach. We extend the previous work by refining the prototyping process by adding a prototyping matrix with two dimensions (purpose and lens), a prototyping cycle (plan, execute, test, reflect, assimilate), and a modified Kanban board (Protoban) for planning, managing, and reflecting cycles. This process, named PETRA was tested with a start-up developing an autonomous trash picking robot. The extended approach supported the team significantly in providing a clear idea of what to do at what time.

This study undertakes a systematic analysis of literature within Circular Economy (CE) in an industrial perspective, with a focus on understanding the consideration of the biological and technological cycles, as well as dual circularity. The paper articulates the key research differences, gaps and trends on the basis of publication evolution, key subject areas, influential journals and keywords co-occurrence mapping. The analysis shows the increasing publication trend with dominance of technological cycle and a wide variety of subject areas incorporated in CE biological, technological and dual cycles. Due to the multidisciplinary and transversal nature of CE, as well as its diverse interpretation and applications, an expansion and consolidation of the subject areas and journals are expected in the years to come. Analysis of co-occurrence on the authors' keywords underlined a limited focus of a business perspective research within the biological cycle, heterogeneous and proactive technological cycle but fragmented research on dual circularity. Further analysis of synergies and limitations is necessary to enhance business effectiveness towards enhanced sustainability.

De-manufacturing and re-manufacturing are fundamental technical solutions to efficiently recover value from post-use products. Disassembly in one of the most complex activities in de-manufacturing because i) the more manual it is the higher is its cost, ii) disassembly times are variable due to uncertainty of conditions of products reaching their EoL, and iii) because it is necessary to know which components to disassemble to balance the cost of disassembly. The paper proposes a methodology that finds ways of applications: it can be applied at the design stage to detect space for product design improvements, and it also represents a baseline from organizations approaching de-manufacturing for the first time. The methodology consists of four main steps, in which firstly targets components are identified, according to their environmental impact; secondly their disassembly sequence is qualitatively evaluated, and successively it is quantitatively determined via disassembly times, predicting also the status of the component at their End of Life. The aim of the methodology is reached at the fourth phase when alternative, eco-friendlier End of Life strategies are proposed, verified, and chosen.

In industry, there is at once a strong need for innovation and a need to preserve the existing system of production. Thus, although the literature insists on the necessity of the current change toward Industry 4.0, how to implement it remains problematic because the preservation of the factory is at stake. Moreover, the question of the evolution of the system depends on its innovative capability, but it is difficult to understand how a new rule can be designed and implemented in a factory. This tension between preservation and innovation is often explained in the literature as a process of creative destruction. Looking at the problem from another perspective, this article models the factory as a site of creative heritage, enabling creation within tradition, i.e., creating new rules while preserving the system of rules. Two case studies are presented to illustrate the model. The paper shows that design in the factory relies on the ability to validate solutions. To do so, the design process can explore and give new meaning to the existing rules. The role of innovation management is to choose the degree of revision of the rules and to make it possible.

As the society is already permeated by data, a data-driven approach to inform design for sustainable behaviour can help to identify misbehaviours and target sustainable behaviours to achieve, as well as to select and implement the most suitable design strategies to promote a behavioural change and monitor their effectiveness. This work addresses the open challenge of providing designers with a model for Human-Machine Interactions (HMI) that helps to identify relevant data to collect for inferring user behaviour related to environmental sustainability during product use.

We propose a systematic modelling framework that combines constructs from existing representation techniques to identify the most critical variables for resources consumption, which are the determinants of potential misbehaviours related to HMI. The analysis is represented as a Behaviour-Inefficiency Model that graphically supports the analyst/designer to link user behaviours with a quantitative representation of resources consumption.

The paper describes the model through an example of the use of a kettle and an additional application of the same approach to a washing machine, in order to point out its versatility for modelling more complex interactions.

Intelligent manufacturing (IM) embraces Industry 4.0 design principles to advance autonomy and increase manufacturing efficiency. However, many IM systems are created ad hoc, which limits the potential for generalizable design principles and operational guidelines. This work offers a standardizing framework for integrated job scheduling and navigation control in an autonomous mobile robot driven shop floor, an increasingly common IM paradigm. We specifically propose a multi-agent framework involving mobile robots, machines, humans. Like any cyberphysical system, the performance of IM systems is influenced by the construction of the underlying software platforms and the choice of the constituent algorithms. In this work, we demonstrate the use of reinforcement learning on a sub-system of the proposed framework and test its effectiveness in a dynamic scenario. The case study demonstrates collaboration amongst robots to maximize throughput and safety on the shop floor. Moreover, we observe nuanced behavior, including the ability to autonomously compensate for processing delays, and machine and robot failures in real time.

Virtual Reality (VR) is progressively adopted at different stages of design and product development. Consequently, evolving interaction requirements in engineering design and development for VR are essential for technology adoption. One of these requirements is real-time positional tracking. This paper aims to present an experimental design of a new real-time positional tracking device (tracker), that is more compact than the existing solution, while addressing factors such as wearability and connectivity. We compare the simulation of the proposed device and the existing solution, discuss the results, and the limitations. The new experimental shape of the device is tailored towards research, allowing the engineering designer to take advantage of a new tracker alternative in new ways, and opens the door to new VR applications in research and product development.

To transfer methods from science to industrial application is an important task of engineering design researchers. However, the way in which this is done leaves still room for improvement. A look beyond the horizon into the intra-industrial transfer of methods can therefore be helpful. Based on general requirements and success factors as well as successful intra-industry transfer examples, this paper proposes the P4I process for the transfer of methods from academy to industry.

The increase availability of operational data from the fleets of cars in the field offers opportunities to deploy machine learning to identify patterns of driver behaviour. This provides contextual intelligence insight that can be used to design strategies for online optimisation of the vehicle performance, including compliance with stringent legislation. This paper illustrates this approach with a case study for a Diesel Particulate Filter, where machine learning deployed to real world automotive data is used in conjunction with a reliability inspired performance modelling paradigm to design a strategy to enhance operational performance based on predictive driver behaviour. The model-in-the-loop simulation of the proposed strategy on a fleet of vehicles showed significant improvement compared to the base strategy, demonstrating the value of the approach.