Developments in artificial intelligence (AI) are opening the possibilities for the development of more advanced design tools. An example of these innovations are generative design tools, in which the generation of complex and high performing products is possible. This study investigates the use of generative design tools and how they may influence the design process and designer behaviour. Six interviews of interdisciplinary designers were conducted to understand the implications of using generative design tools. It was observed that generative design tools primarily allow for quantitative inputs to the tool while qualitative metrics, such as aesthetics, are considered indirectly by designers. The subjectivity of the designer and how they incorporate the quantitative and qualitative metrics in the generative design tool can lead to differing outcomes between designers. Notable differences in tool usage are also observed between expert and novice computational designers. Additional studies should be conducted to further understand the extent generative design tools impact the design process, designer behaviour, and design outcomes.

Optimization-driven design offers advantages over traditional experience-based mechanical design. As an example, topology optimization can be a powerful tool to generate body shapes for Additive Manufacturing (AM). This is helpful, when (1) load paths are non-intuitive due to complex design domains or boundary conditions, or (2) the design process is to be automated to minimize effort associated with experience-based design. However, practically relevant boundary conditions are often difficult to put into a formal mathematical language to, for example, either feed it into a topology optimization algorithm, or provide precise quantitative criteria for CAE-supported manual design. This paper presents a survey of three industry use cases and identifies three types of requirements: the first can be directly cast into parts of an optimization problem statement (∼ 40%), the second is considered indirectly by adapting the optimization problem without explicit reference to the requirement (∼ 20%), and the third is only assessed after the design is finalized (∼ 40%). For categories 2 and 3 we propose directions of improvement to support formulating complex design tasks as unambiguous design problems.

A digital twin is the mapping of a physical twin between hybrid spaces. The lifecycle of digital and physical twins occurs through the concepts of foetal, child, and adult twins. This technology can be used to assist clients and designers with real-time data. The use of digital twin technology in architectural design can be realised at various stages, from design to operation. Designers will be able to gain knowledge of the past, present, and future using this technology. This will reveal possible design scenarios. In this study, a hypothetical scenario is designed, in which designers build a building while already having a digital twin template. To do this, Building Information Modelling (BIM) is used as a reference model for digital twins, along with the fidelity levels of digital twins and the level of detail-development of BIM. When designers want to design a new project related to their predecessors, they already use the same type of digital twin-building portfolio they can use for their new design. A digital twin will help optimise the new process. Therefore, the digital twin of a building with a similar building type can be used to extract relevant data for the design process.

Hybrid manufacturing, a combination of additive and subtractive manufacturing capabilities in one system, has recently become a more viable production option across several industries. Although current hybrid manufacturing research covers a broad range of topics, there is a lack of focus on how this new technology impacts both the designer and the operator of hybrid systems. This paper identifies areas of literature across design theory and Industry/Operator 4.0 research efforts and presents a path for applying this research to hybrid manufacturing users. The unique relationship between operator and designer is highlighted as they learn new strategies and develop new intuitive judgements over time to become the first experienced/expert users of hybrid manufacturing. The potential impact of excessive cognitive workload due to the novel combination of processes is discussed. This paper begins a critical discussion about proper knowledge transfer to other hybrid designers and operators, as well as towards efforts of monitoring, inspecting, and automating hybrid manufacturing processes.

Digital design tools and technologies offer new opportunities for designers to generate a diverse range of design solutions. Previous research have discussed the multifaceted use of such technologies for 1) rapid visualisations, 2) generating design options, and 3) predicting design solutions. However, such research have focused more on simplifying design for fabrication and less on the integration of individual needs in design processes. This research adopts a human-centric design approach to merge user-to-design and design-to-fabrication processes. Through a scoping review on homelessness, design, and fabrication, we contribute a user-design-fabrication framework devised for the specific and dynamic needs of homeless individuals living in Melbourne, Australia. Our findings suggests that to optimise digital design processes for individuals with specific and dynamic needs, designers need to understand, translate, and embed the social, design, and fabrication complexities of a design problem. Future research should therefore test the real-world application of our user-design-fabrication framework and evaluate the impact of such digital design processes, for the provision of more individualised homeless housing design solutions.

AR/VR applications are a valuable tool in product design and lifecycle. But the integration of AR/VR is not seamless, as CAD models need to be prepared for the AR/VR applications. One necessary data transformation is the tessellation of the analytically described geometry. To ensure the usability, visual quality and evaluability of the AR/VR application, time consuming optimisation is needed depending on the product complexity and the performance of the target device.

Widespread approaches to this problem are based on iterative mesh decimation. This approach ignores the varying importance of geometries and the required visual quality in engineering applications. Our predictive approach is an alternative that enables optimisation without iterative process steps on the tessellated geometry.

The contribution presents an approach that uses surface-based prediction and enables predictions of the perceived visual quality of the geometries. This contains the investigation of different geometric complexity metrics gathered from literature as basis for prediction models. The approach is implemented in a geometry preparation tool and the results are compared with other approaches.

ABSTRACT

Solution spaces are sets of designs that meet all quantitative requirements of a given design problem, aiding requirement management. In previous works, ways of calculating subsets of the complete solution space as hyper-boxes, corresponding to a collection of permissible intervals for design variables, were developed. These intervals can be used to formulate independent component requirements with built-in tolerance. However, these works did not take physical feasibility into account, which has two disadvantages: first, solution spaces may be useless, when the included designs cannot be realized. Second, bad designs that are not physically feasible unnecessarily restrict the design space that can be used for requirement formulation.

In this paper, we present the new concept of a requirement space that is defined as the largest set of designs that (1) allows for decomposition (e.g., into intervals when it is box-shaped), (2) maximizes the useful design space (good and physically feasible), and (3) excludes the non-acceptable design space (bad and physically feasible). A small example from robot design illustrates that requirement spaces can be significantly larger than solution spaces and thus improve requirement decomposition.

Autonomous vehicles for the last mile are a promising use case for advancing autonomous driving in real-world traffic. For this purpose, traditional car manufacturers and newcomer companies develop a new vehicle concept: the autonomous shuttle. During the development, components from the automation domain, such as the sensors, must be placed and integrated into the vehicle body. The trade-offs between the functional performance of the perception and the exterior design must be evaluated early in the design process. For this purpose, a model of the vehicle exterior is needed. In this contribution, we present a method for parametric modeling of the vehicle exterior of autonomous shuttles. We define 17 input parameters and use computer-aided design to create a virtual model of the body and the wheelhouses. In the results, we validate our method by ensuring that existing shuttles can be modeled with our approach and also analyze the limitations. The model supports decision-making in the early design phase by enabling quick iterations between sensor placement and exterior design.

Robust Design offers a coherent and widely appreciated approach for the parametric exploration of the design space by means of simulation or experimentation, which is well-established in the quality-by design domain. From the perspective of design research, however, this only addresses a relatively narrow part of the design process and is not fully integrated with other design decisions such as concept exploration, the suitable configuration of system elements, or the design of interfaces. Particularly in light of the growing importance of developing technologically advanced and “smart” systems, it seems that a new methodical perspective on Robust Design is needed. Against this background, this paper consolidates knowledge and insights from different research fields and industry sectors. On this basis, new angles to the discussion on product robustness in different domains are explored in order to suggests directions for action and new research areas, both with respect to a methodical RD approach as well as the question of systematic research procedures.

Failure is part of the design process, and yet there is limited knowledge around how product design students perceive failure in their work. This pilot study aims to understand how a small sample size of undergraduate product design students conceptualize success and failure during specific stages of their design projects. This study uses a two-step data collection and analysis process. First, we collected responses from students on topics related to success and failure in a survey. Second, interviews were conducted with a subset of the survey respondents where these emergent topics were discussed and refined. In analyzing the responses, the research team used the Double Diamond Design process framework to organize what factors students deemed a success or failure within each stage. In summary, our preliminary findings indicate that determining success or failure is driven by the connection to the problem statement regardless of the stage; that student designers refer to failure as a spectrum but then in their examples showcase a binary view on the topic; and that examples of failure are often the opposite of success, reinforcing the notion of binary success vs. failure during student design projects.

The transportation sector is responsible for a relevant share of the total emissions and offers great potentials. It is necessary to implement as many zero-emission mobility systems as possible in the shortest time. For fast ferries, which are a relevant transport manner for a large share of the global population, technical issues could be solved and the successful operation was demonstrated. Up to today high-speed ships have been fully individually designed because physical effects demand for an individual optimisation for each use-case. Specifically for battery electric ships the overall efficiency is crucial to ensure not only an ecological but also economical operation.

With today's methods the design and production of such an individual designed ferry does take too long. To cover the rising demand, new approaches for mass production need to be established.

In this paper we describe a method for designing a platform for ships with the example of a battery electric fast ferry. The focus is on the actual modularisation, as other aspects like requirements or results of our example case are published elsewhere and are therefore just included briefly.

The method is validated on the world's first battery powered high-speed ferry.

New trends and technologies in product creation increase complexity, but at the same time create new potentials such as efficiency rise in task processing by Artificial Intelligence. Established models in the early phase of product creation such as the W-model or the Aachener Innovation Management model, do not fully exploit these new potentials in the field of strategic product planning and innovation management (SPPIM). For this reason, existing models are analysed in SPPIM in order to derive a requirements profile consisting of potentials and goals for a new model. A new model in SPPIM lays the foundation to support companies in enabling a more efficient task fulfilment by taking advantage of new technologies and trends. To guide the development of advanced SPPIM models, the derived potentials and goals are applied to the guideline VDI 2220:1980.

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.

Context and problem: Reuse is positioned as a strategy capable of countering single-use overconsumption and disposal. For refill at home FMCGs, consumers are responsible for carrying out behaviours that enable this, such as keeping and using products for a prolonged period. However, it is not known if consumers actually fulfil these responsibilities. Aim: This research aims to understand the extent to which consumer reuse behaviours supports the intended reduction in impact and, if not, consider how best to improve it. Method: In-depth interviews with 15 consumers were conducted where the behaviour chain method was used to map resource journeys for 31 refill at home offerings. Results: Five models of consumer behaviour increased the impact of reuse. The critical moments which led consumers to carry out these behaviours were identified, uncovering intervention areas. Conclusions: The behaviour models and critical moments offer a first attempt to systematically analyse how and where actual consumer behaviour can increase the impact of refill at home FMCGs. The results call for focussed interventions across the consumer journey that support reuse components as part of a system.

Patents are an invaluable source of data that can be beneficial for Engineering Design (ED). Patenting is one of the main means for disclosing the inventive process. For this reason, the description of the problem solved should also be included in any patents.

The ED literature lacks a proper definition of a problem, resulting in a fragmented scenario. Prior studies have employed Text Mining (TM) to extract problems from patents. We argue that TM can assist ED researchers in understanding how problems are articulated in text. Based on the literature, we propose two hypotheses: (1) problem-related text exhibits a negative sentiment polarity compared to other sections of patents; (2) problem-related keywords identified in the literature are predominantly used to describe problems rather than other aspects.

We analyse Japanese patents to validate our hypotheses, since they explicit Problem and Solution in the abstract. Finally, we compare our results with a set of problem-related sentences extracted from USPTO patents.

Our study reveals a higher positive sentiment in problem-related sentences compared to solution-related ones and highlights the inadequacy of using problem-related keywords alone to differentiate between the two.

Digital Twins are virtual representations of a product-service-instance and, as a technology, represent an important part of the realization of Industry 4.0. They manage data of the associat-ed product instance and can also have functions for simulation to achieve cost and resource savings while simultaneously increasing product quality. In this paper, a need for action for the implementation of a systematic approach for the returning of data of Digital Twins into the product design is identified and a methodology is developed as an answer. This methodology realizes an information management, which supports holistic data and information flows. It de-fines necessary steps for the implementation of data and information transport, starting from a data management up to information provision in product design. Based on a performed potential analysis for the identification of intended uses in the context of product design, the overall ap-plication focus is narrowed down to the development of new product generations to support the requirements development. The concept structure consists of Digital Twins, a data mining sys-tem for the transformation of data into information and a presentation system for managing the information provided.

Fourth industrial revolution called Industry 4.0 has radically transformed production systems in manufacturing companies by the integration of emerging technologies. However, manufacturers must overcome several barriers, such as the lack of qualified talent to develop and manage various high-technology systems. Assembly system design aims to define proper assembly line configurations with the optimal performances to overcome increased competitiveness in the market. Nowadays, assembly system design should consider industry 4.0 concepts integration beyond traditional aspects like system balancing and sequencing. In this paper, we introduce a project-based learning approach to teach engineering students assembly system design taking into account industry 4.0 dimension. This project is carried out in collaboration with an industrial partner to design and implement car doors assembly line. The project demonstrated students interest and prepared them better for industry 4.0 era.

The current engineering environment demands for an increasing level of interdisciplinarity, innovation, creativity and cross-domain thinking as well as the consideration of sustainability aspects. New concepts, such as Digital Twins and complex product systems lead to the need for integrated product development approaches and new methods that put the user perspective in focus. This also needs to be an integral part in today's teaching concepts of the next generation of engineers.

At the Department of Industrial Information Technology of the Technical University of Berlin, a case study was conducted by applying a concept of project-based learning in the engineering domain to address these challenges. In this paper, the case study as well as the method and its validation are presented. Students from different engineering disciplines had the task of developing virtual and physical prototypes for a sustainable, complex product system with a digital twin and respective sustainable business models. Within a structured survey, the teaching concept and the applied method were validated and lessons learned as well as further improvement measures are derived.

The climate change and the new regulations imposed to slow down this process have pushed major actors around the world to apply sustainable measures in order to meet governmental obligations. The packaging industry has a major role in fighting climate change and the environmental social and financial disasters. In fact, packaging requires substantial number of natural resources and is often disposed of in ways that harm the environment. Supply chain managers realized that packaging design is the root to solve all the problems packages create throughout their entire life cycle. Therefore, a thorough study about influencing the design process should be prioritized. This paper provides a systematic review of industrial packaging eco-design applied to the supply chain. It aims to conceptualize the definition of industrial packaging eco-design and supply chain sustainability to categorize main parameters to its implementation and to identify the limitations of current research and its prospects.

It is important for organizations to balance exploration and exploitation in order to respond quickly and sustainably to the needs of society and users in a rapidly changing business environment. However, there is insufficient research on design methods to balance exploration and exploitation in product design, and a method to objectively identify the product groups to be balanced has not yet been established. In this paper, on the basis of the characteristics of exploration and exploitation in design, a cluster analysis using functional and attribute distances between products is proposed. To validate the proposed method, it was applied to past product cases in which the relationship between exploration and exploitation was known. The results showed that in the cases of cameras, in addition to known product groups forming large clusters, reasonable minor classifications that had not been identified were also obtained. This indicates that the proposed method is capable of analyzing reasonable clusters in the cases and is potentially effective in identifying product groups taking into consideration the relationship between exploration and exploitation.