Human-centered design provides a means to help designers create products or systems with ‘people’ as the focus. Compassionate Design (CD), introduced in this paper, is an approach that addresses niche sensitive needs and involves a way of thinking where designers pay special attention to the users’ sense of dignity, empowerment, and security. These niche needs surfaced as a result of analyses of 12 cases situated in sensitive contexts where the users felt vulnerable, had a high level of emotional engagement and were negatively affected by the situation. The designers described their deep concern for the users in various talks and interviews. This paper explains the conception of CD and its development that resulted from iteratively and qualitatively analyzing these cases in which designers were intuitively focusing on niche user needs. Dignity, empowerment and security form the basis of CD and have been contextualized in Maslow’s hierarchy of needs after they emerged as a result of the analysis of data. This research sets the platform for a design approach that can help designers to consider the often unarticulated user needs of dignity, empowerment and security, in a more intentional manner and not be left to chance.

Human-centered design (HCD) offers a systematic approach to innovation practice, driven by customer research and feedback throughout the design process. Within the community of engineers and researchers who engage in design for global development, interest in HCD has grown in the past decade. In this paper, we examine the human-centered design for development (HCD+D) academic community to better understand the interactions between researchers. By building and evaluating a co-authorship network from a dataset of HCD+D papers, in which the nodes are researchers and the connecting links are co-authorship relationships, we provide a decade-long benchmark to answer a variety of questions about collaboration patterns within this emerging field. Our analysis shows that most HCD+D authors publish few papers and are part of small, well-connected sub-communities. Influential authors that bridge separate communities are few. HCD+D is emerging from disparate disciplines and widely shared scholarship across disciplines continues to be developed. Influential authors in HCD+D play a large role in shaping HCD+D, yet there are few authors that are in a position to connect and influence collaborative research. Our analysis gives rise to several implications including an increased need for cross-disciplinary collaboration and the need for a stronger core of HCD+D practitioners.

Combining additive manufacturing (AM) with carbon fiber reinforced polymer patched composites unlocks potentials in the design of individualized, lightweight biomedical structures. Arising design opportunities are geometrical individualization of structures using the design freedom of AM and the patient-individual design of the load-bearing components employing carbon fiber patch placement. To date, however, full exploitation of these opportunities is a complex recurring task, which requires a high amount of knowledge and engineering effort for design, optimization, and manufacturing. The goal of this study is to make this complexity manageable by introducing a suitable manufacturing strategy for individualized lightweight structures and by developing a digitized end-to-end design process chain, which provides a high degree of task automation. The approach to achieve full individualization uses a parametric model of the structure which is adapted to patients’ 3D scans. Moreover, patient data is used to define individual load cases and perform structural optimization. The potentials of the approach are demonstrated on an exoskeleton hip structure. A significant reduction of weight compared to a standard design suggests that the design and manufacturing chain is promising for the realization of individualized high-performance structures.

Additive manufacturing (AM) has gained prominence over the last 15 years and become a viable manufacturing option. Since there is great industrial interest to implement serial production of products using AM, the education of engineers in design for additive manufacturing (DfAM) is important today. DfAM extends on design for manufacturing to provide knowledge about the new design opportunities enabled by AM. A set of design heuristics for additive manufacturing (DHAM) to assist designers with conceptual-level DfAM has previously been proposed. In this paper, these DHAM are evaluated through workshops with 3 engineering companies and 19 engineers, in which the participants re-design existing company products for AM using the DHAM as an aid, and then give feedback. The DHAM are well received by the workshop participants, and they find that the DHAM are good for teaching others about the capabilities of AM, provide a checklist of items to consider to help avoid oversights, and encourage the generation of new ideas. Criticisms include the number of examples provided and the lack of information about how to implement the ideas concretely. It is also found that the DHAM fulfil 16 of 18 criteria for early design phase DfAM methods, identified in academic literature.

The impact of engineered products is a topic of concern in society. Product impact may fall under the categories of economic, environmental or social impact, with the last category defined as the effect of a product on the day-to-day life of people. Design teams lack sufficient tools to estimate the social impact of products, and the combined impacts of economic, environmental and social impacts for the products they are designing. This paper aims to provide a framework for the estimation of product impact during product design. To estimate product impact, models of both the product and society are required. This framework integrates models of the product, scenario, society and impact into an agent-based model to estimate product impact. Although this paper demonstrates the framework using only social impact, the framework can also be applied to economic or environmental impacts individually or all three concurrently. Agent-based modelling has been used previously for product adoption models, but it has not been extended to estimate product impact. Having tools for impact estimation allows for optimising the product design parameters to increase the potential positive impact and reduce potential negative impact.

In many cases, the use of damping technologies is the only option to reduce undesired vibrations. Despite various damping techniques available on the market, the design of a precise damping behaviour still needs a lot of experimental testing and engineering experience. This is also the case for particle damping. However, for lightweight structures, technologies such as particle damping provide an opportunity to improve the structural dynamic behaviour without a large mass gain. With respect to this conflict, a hybrid numerical and experimental design approach is presented based on frequency based substructuring (FBS). With this technique, the use of experimental data for design optimization is possible and detailed modelling of the nonlinear particle damping system can be avoided. Moreover, based on the FBS, an approach to optimize damping and weight is proposed. All results are compared to experiments, and a subsequent discussion shows that the predictions for particle damping with FBS are accurate for defined operating points from which realistic designs can be derived. Generally, it is shown that methodical design approaches may strongly improve not only product development processes but also structural mechanical design.

The design process often requires work by teams, rather than individuals. During team based design it is likely that situations will arise in which individual members of the team have different opinions, yet a group decision must still be made. Unfortunately, Arrow’s impossibility theorem indicates that there is no method for aggregating group preferences that will always satisfy a small number of ‘fair’ conditions. This work seeks to identify methods of combining individual preferences that can come close to satisfying Arrow’s conditions, enabling decisions that are fairer in practice. First, experiential conjoint analysis was used to obtain individual empirical utility functions for drinking mug designs. Each empirical utility function represented individual members who were part of a design team. Then, a number of functions for constructing group preference were analysed using both randomly generated preferences and empirical preferences derived from the experiential conjoint survey. The analysis involved checking each of Arrow’s conditions, as well as assessing the potential impact of strategic voting. Based on the results, methods that should be used to aggregate group preference within a design team in practice were identified and recommended.

Enterprise social network messaging sites are becoming increasingly popular for team communication in engineering and product design. These digital communication platforms capture detailed messages between members of the design team and are an appealing data set for researchers who seek to better understand communication in design. This exploratory study investigates whether we can use enterprise social network messages to model communication patterns throughout the product design process. We apply short text topic modelling (STTM) to a data set comprising 250,000 messages sent by 32 teams enrolled in a 3-month intensive product design course. Many researchers describe the engineering design process as a series of convergent and divergent thinking stages, such as the popular double diamond model, and we use this theory as a case study in this work. Quantitative and qualitative analysis of STTM results reveals several trends, such as it is indeed possible to see evidence of cyclical convergence and divergence of topics in team communication; within the convergence–divergence pattern, strong teams have fewer topics in their topic models than weaker teams; and teams show characteristics of product, project, course, and other themes within each topic. We provide evidence that the analysis of enterprise social networking messages, with advanced topic modelling techniques, can uncover insights into design processes and can identify the communication patterns of successful teams.

Co-evolution accounts have generally been used to describe how problems and solutions both change during the design process. More generally, problems and solutions can be considered as analytic categories, where change is seen to occur within categories or across categories. There are more categories of interest than just problems and solutions, for example, the participants in a design process (such as members of a design team or different design teams) and categories defined by design ontologies (such as function-behaviour-structure or concept-knowledge). In this paper, we consider the co-evolution of different analytic categories (not just problems and solutions), by focussing on how changes to a category originate either from inside or outside that category. We then illustrate this approach by applying it to data from a single design session using three different systems of categorisation (problems and solutions, different designers and function, behaviour and structure). This allows us to represent the reciprocal influence of change within and between these different categories, while using a common notation and common approach to graphing quantitative data. Our approach demonstrates how research traditions that are currently distinct from each other (such as co-evolution, collaboration and function-behaviour-structure) can be connected by a single analytic approach.

Mass Personalisation (MP) is becoming more significant to answer diversifying customer needs, as a result of the advancements in digital manufacturing. In contrary to the modular design in mass customisation, Design for MP (DfMP) proposes more profound changes in product and active user involvement in the design process, while maintaining mass efficiency. Traditional product development methodologies fall short in guiding MP, as it has the distinct differences with product variability and the customer involvement with specific needs. In this study, a dedicated design methodology for MP is presented, focussing on these key dimensions. The proposed methodology guides the designer through the development process of a user modifiable design and demonstrates how to facilitate the user involvement in reaching a personalised design. It proposes a flexible and adaptable seed design architecture, and an interactive customer co-creation process. The development of a seed design, construction of its design space, and management of the solution space with a design solution algorithm are elaborated. The application of the methodology was illustrated on the personalisation of knitted footwear, and 3D printed saxophone mouthpiece. The examples show the potential of the methodology to deal with coupled MP cases. A systematic approach to DfMP will allow expanding MP to more products, and acts as a foundation for the customer co-creation oriented design in the context of this emerging paradigm.

Design grammars enable the formal representation of a vocabulary and rules that describe how designs can be synthesized just as the grammar rules of a spoken language define how to formulate valid, i.e., grammatically correct, sentences. Design grammars have been successfully applied in numerous engineering disciplines and enable the automated synthesis of designs within a defined design language. Design grammar development, however, is challenging and lacks methodological support. In this paper, a novel method is presented that supports the development and application of design grammars using transition graphs. In these, nodes represent generated designs and edges represent grammar rules that transform one design into another. Rather than using a tree structure to represent the possible application of rules, transition graphs are automatically generated and used to help designers better understand the developed grammar. The grammar designer is given feedback on (a) the rules, and (b) rule application sequences. This feedback can be used to (a) improve the grammar, and (b) apply it more efficiently. Two case studies, a gearbox synthesis task and a sliding tile puzzle, demonstrate the method. The results show the feasibility of the method to support design grammar development and application.

The design of a connected engineered system requires numerous design decisions that influence one another. In a connected system that comprises numerous interacting decisions involving concurrency and hierarchy, accounting for interactions while also managing uncertainties, it is imperative to make robust decisions. In this article, we present a method for robust design using coupled decisions to identify design decisions that are relatively insensitive to uncertainties. To account for the influence among decisions, design decisions are modelled as coupled decisions. They are defined using three criteria: the types of decisions, the strength of interactions and the decision levels. In order to make robust decisions, robust design methods are classified based on sources of uncertainty, namely, Type I (noise factors), Type II (design variables) and Type III (function relationship between design variables and responses). The design of a one-stage reduction gearbox is used as a demonstration example. To illustrate the proposed method for robust design using coupled decisions, we present the simultaneous selection of gear material and gearbox geometry in a coupled decision environment while managing the uncertainties involved in designing gearboxes.

Complex systems are challenging to design, particularly when they contain multi-level organizations with non-obvious relationships among design components. Here, we investigate engineering students’ capacity to search for optimal nanoscale biosystem designs with stochastic component and system behaviors. The study aims to characterize information types that facilitate human learning and improve their complex system understanding and design proficiency. It is hypothesized that learning parametric system relationships and/or inter-level causal mechanisms improves design proficiency; these relationships and mechanisms are teachable through software interfaces. Two contrasting learning/design interfaces were developed that presented differing information types: an interface with performance charts that emphasized parametric relationship learning and an interface with agent-based animations that emphasized inter-level causality learning. Users improved on pre-/post-learning design tasks with both interfaces; users who demonstrated inter-level causal relationship understanding, which occurred primarily with the animation interface, had greater improvement. All users were then presented contrasting animations of systems with opposing emergent behaviors, resulting in many more participants demonstrating an understanding of inter-level causal behaviors. These findings reveal the difficulties in understanding and designing multi-level systems and that interactive software tools may convey crucial information that supports engineering design, particularly with respect to the development of reasoning skills for how system components relate across levels.

Research Through Design (RTD) needs to reconsider the meaning of “designing” in the research process of “through design.” We propose Research Through Co-design (RTC) as a new application of Control System Theory (CST) that includes a research problem assigned to a co-design process in RTD. It embeds the participatory paradigm through collaborative design practice and makes the research a collaborative process for learning from all the participants. To sustain the RTC theory, we present a cognitive model of RTC. It is a “model for” – rather than a “model of” – describing how the co-design, as a neural network process, works through its nodes’ collaboration to find co-designed solutions and the research answer. Diversity increases as non-experts and non-designers with different backgrounds participate. This is valuable for the RTC learning system. The discussions highlight the possibility of considering (i) the RTC model as useful for describing a robust RTD process through CST; (ii) RTC as a cognitive model for explaining the value of co-design in research processes; and (iii) RTC as a strategy for applying the participative paradigm in formal research. Finally, new insights and implications are highlighted, including using RTC as a predictive tool through artificial intelligence.

Engineering design induces mental stress for students and the sources of stress for each stage of design are unique. Therefore, strategies are needed to manage the stress of engineering design that are applicable across the design process. This study investigated the effect of a brief mindfulness-based intervention on first-year students’ cognitive stress during concept generation, concept selection and physical modelling. It was found that the mindfulness-based intervention did increase one aspect of students’ state mindfulness (though the effect was small). While prior work indicates that increased mindfulness can lower perceived stress, the increase in students’ state mindfulness during this study was not found to have an observable impact on students’ stress experience. However, students were receptive to completing a mindfulness-based activity in-class and perceived multiple benefits. Physical modelling was the most stressful of the design tasks while concept generation and concept selection produced similar levels of stress. Students used five reoccurring mechanisms for coping with the stress of design including focusing on the task, minimising the importance of their performance, breathing, taking a break and avoidance/distraction. More research should be conducted with longer duration mindfulness-based interventions to understand their potential as a stress management strategy for engineering design.

Sustainability considerations are traditionally difficult to trade-off with technical and business requirements in an early design phase. Hence, design teams need support to reflect early on in the process, on how sustainability may affect profitability and customer value fulfilment in the long term. The commoditisation of modelling and simulation techniques points to gamification and serious gaming as emerging approaches to raise awareness among the design team – as well as users and stakeholders – of the expected behaviour of a solution along its life cycle. The objective of this paper is to explore how serious games can be used to inform decision-makers about the value versus cost implications of being (or not being) ‘sustainability compliant’ when designing products and systems. The paper initially presents the findings from a descriptive study focused on the definition of ‘design support’ intended to raise sustainability awareness through serious gaming. It further describes the development, application and testing of one of such games for material selection in the aerospace industry.

This paper presents a new crowdsourcing approach to the construction of patent clusters, and systematically benchmarks it against previous expert and algorithmic approaches. Patent databases should be rich sources of inspiration which could lead engineering designers to novel solutions for creative problems. However, the sheer volume and complexity of patent information means that this potential is rarely realised. Rather than the keyword driven searches common in commercial systems, designers need tools that help them to understand patents in the context of the problem they are considering. This paper presents an approach to address this problem by using crowd intelligence for effective generation of patent clusters at lower cost and with greater rationale. A systematic study was carried out to compare the crowd’s efficiency with both expert and algorithmic patent clusters, with the results indicating that the crowd was able to create 80% more patent pairs with appropriate rationale.

Recent advances in early stage failure analysis approaches have introduced behavioral network analysis (BNA), which applies a network-based model of a complex engineered system to detect the system-level effect of ‘local’ failures of design variables and parameters. Previous work has shown that changes in microscale network metrics can signify system-level performance degradation. This article introduces a new insight into the influence of the community structure of the behavioral network on the failure tolerance of the system through the role of bridging nodes. Bridging nodes connect a community of nodes in a system to one or more nodes or communities outside of the community. In a study of forty systems, it is found that bridging nodes, under attack, are associated with significantly larger system-level behavioral degradation than non-bridging nodes. This finding indicates that the modularity of the behavioral network could be key to understanding the failure tolerance of the system and that parameters associated with bridging nodes between modules could play a vital role in system degradation.

An important source for technical lightweight design is the human musculoskeletal system. The musculoskeletal lightweight design mainly results from the coordinated interplay of different principles. Prevailing solutions that use musculoskeletal lightweight design principles neglect the coordinated interplay of these principles. Moreover, transfer is limited to isolated principles. Therefore, further potential for lightweight design can be expected. Due to the kinematic similarities of the human extremities to technical systems that can be described as open kinematic chains, in this paper the lightweight design potential is examined by applying the interaction of the aforementioned lightweight design principles to technical systems. A new bioinspired approach is developed, which implements the control and optimization running synchronously in nature in a sequential approach for technology. The bioinspired approach is implemented by coupling multibody simulation and topology optimization in an iterative process. The results of the bioinspired approach show that, compared to a classical approach, mass can be saved and deformations can be minimized. The synthesized geometry is mainly optimized for compressive stresses and therefore easier to manufacture than a bending stiff structure in the classical case. The examinations in this paper do not take application-specific requirements into account. Therefore the application to special technical systems, and furthermore advantages and disadvantages of the new approach are discussed.

Designers can involve users in the design process. The challenge lies in reaching multiple users and finding the best way to use their input in the design process. Affordance based design (ABD) is a design method that focuses in part on the perceived or existing interactions between the user and the artifact. The shape and physical characteristics of the product enable the user to perceive some of its affordances. The goal of this research is to use ABD, along with an optimization tool, to evolve the shape of products toward better perceived solutions using the input from users. A web application has been developed that evolves design concepts using an interactive multi-objective genetic algorithm (IGA) relying on the user assessment of product affordances. As a proof of concept, a steering wheel is designed using the application by having users rate specific affordances of solutions presented to them. The results show that the design concepts evolve toward better perceived solutions, allowing designers to explore more solutions that reflect the preferences of end users. Relationships between affordances and product design variables are also explored, revealing that specific affordances can be targeted with changes in design parameter values and highlighting the tie between physical characteristics and affordances.