Wearable devices have some shared characteristics. They should conform to the wearer's size and shape, not interfere with desired activities, perform intended functions, be easily usable and comfortable, among others. Due to these shared characteristics, a common design methodology should be possible for designing wearables that assists designers in taking a systematic approach. We propose a configuration design method for wearables and sketch its elements. An example of a family of passive exoskeleton suits that assists with walking is presented to illustrate the configuration design process.

This paper introduces a method to derive variation shares in engineering design, merging the Contact & Channel Approach (C&C²) with the model of PGE - Product Generation Engineering. It focuses on one-piece parts, enhancing precision in identifying component variations. The integration allows for detailed qualitative modeling and subcomponent analysis, improving design efficiency and innovation, illustrated with bottle examples. This research advances engineering design by enabling more accurate categorization of system variations.

This paper explores the suitability of Artificial Neural Networks (ANNs) as an enabler of Design Automation in the turbomachinery industry. Specifically, the paper provides 1) a preliminary estimation of the effectiveness of ANNs to define values for design variables of reciprocating compressors (RC) and 2) a comparison of ANNs performance with traditional and more computationally demanding methods like CFD. A tailored ANN trained on a dataset composed by 350+ Baker Hughes’ RC automatically assigns values to 8 geometrical variables belonging to multiple parts of the RC in order to satisfy two target conditions linked to their thermodynamic performance. The results highlight that the ANN-assigned parameters return an optimal solution for RC also when the target values do not belong to the training dataset. Their predictive capacity for RC thermodynamic performance, with respect to CFD, are comparable (i.e. less than 2% in terms of calculated absorbed power) and the approach enables a significant gain in terms of computational time (i.e. 2 minutes vs 10 hours). Future perspectives of this work may involve the integration of this tool in an advanced DA method to lead Design Engineers (DEs) during the whole design process.

There are innumerable design methods that exist across a wide spectrum of disciplines, ranging from engineering, to marketing, to psychology. However, the organic, multidisciplinary nature of methodological development in design leads to challenges in comparing or combining methods. Disciplinary perspectives can create conceptual 'boundaries' that may not align with the fluidity of the problems that designers may need to address. It is challenging to work between the boundaries of these design methods due to the unclear delimitation of exactly where and how methods may be integrated. Nomenclature is unstandardized and different terminologies may describe similar phenomena. To address this, a boundary object—the Actor-Abstraction matrix—is developed to recontextualize each of these divergent methods onto a common scale so they may be better understood in reference to their peers. A meta-analysis of four established design methods is performed to demonstrate the flexibility of this conceptual device. With this tool, existing design methods may be more easily examined to identify points of compatibility and gaps in their coverage, and could also serve as a powerful platform for the creation of new design methods in the future.

The number of digital solutions based on machine learning has increased in recent years. In many industrial sectors, they try to enhance automation in manual or repetitive tasks or provide decision support for complex problems. Data plays an essential role in the selection and implementation of ML algorithms, as it determines the quality of the training and the results. As data drive ML models, selecting the correct data with the suitable ML algorithm for a given use case is crucial but challenging. This paper reviews the application of machine learning in the embodiment design phase addressing the challenge. The work focuses on ML applications in conventional product development and non-conventional additive manufacturing processes. Based on the literature review, the required knowledge to implement the ML algorithms has been derived and presented in a systematic approach. This work highlights the importance of an initial analysis of the existing knowledge in the engineering and additive manufacturing processes in order to implement the proper ML algorithms.

With the continuously increasing integration of (mechanical) products, the identification and management of trade-offs becomes a major task in product synthesis, with substantial effect on optimality and robustness of the final solution. At the same time, a rigorous and comprehensive study of trade-offs through mathematical design optimisation is often impractical in design, as efforts spent on modeling and optimizing are likely wasted if a chosen design is changed. Extending research on configuration redesign based on a multiobjective monotonicty analysis (MOMA), this paper presents three levels of evaluation for early design or redesign: (I) informal evaluation, (II) opportunistic evaluation, and (III) exhaustive evaluation. The chosen level depends on what knowledge the designer wants to gain, and the higher the level, the larger the analysis effort, the lesser the re-use of the information gained from the initial MOMA analysis respectively. The approach is illustrated using a novel drug delivery device, the Self-Orienting Millimeter-Scale Applicator (SOMA), for the oral delivery of protein compounds such as insulin.

Prototyping strategies and technology often focus on reducing the fabrication time and cost between design iterations, however, there is limited knowledge about the specific characteristics of change that prototyping strategies aim to impact. To investigate, and better understand these characteristics, this study curates and systematically analyses a representative dataset of 50 'real-world' prototype samples. The study aims to explore the various elements that constitute a design change and to determine their impact on the scale of volumetric change detected. The results highlight emergent patterns and correlations between study metrics to better understand the reasons for design change and the frequency and scale of changes detected in the sample dataset. Findings reveal that the purpose of a design change is, in certain cases, highly correlated to the scale of change affected, and that some changes are more prevalent in the dataset than others, with an average volumetric difference of 4.2% between sample versions detected. The study provides an initial characterisation of prototype change to guide iterative prototyping processes and improve the efficiency and effectiveness of design iterations.

Testing activities to gain specific design knowledge play an essential role in engineering design, when a structure needs to be developed, for which knowledge from analytical models or documentation is missing. As research into these testing activities is difficult, few insights into successful approaches exist.

In this contribution, we investigate testing activities to gain specific design knowledge through a laboratory task, where 10 engineering students optimize a system using a web-based process chain including rapid prototyping and testing. Design and testing data are acquired from 110 prototypes in 3 hours. A differentiation of performance is conducted and approaches of high- and low-performers are investigated to identify patterns.

Based on these patterns, hypotheses, and metrics indicating successful and non-successful approaches are derived as basis for development of metrics for testing to gain specific design knowledge. A successful approach was overstep the limit, where participants accept destruction of their system to identify boundaries. An unsuccessful approach was the change of many parameters in later tests. These hypotheses and their metrics can then be used in development and validation of support for testing.

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.

The objective of this paper is to present a mathematically grounded description of the two topological spaces for the design problem and the design solution. These spaces are derived in a generalized form such that they can be applied by researchers studying engineering design and developing new methods or engineers seeking to understand the influence that changes in the problem space have on the solution space. In addition to the formal definitions of the spaces, including assumptions and limitations, three types of supported reasoning are presented to demonstrate the potential uses. These include similarity analysis to compare spaces, an approach to sensitivity analysis of the solution space to changes in the problem space, and finally a distance measure to determine how far a current proposal is to the feasible solution space. This paper is presented to establish a common vocabulary for researchers when discussing, studying, and supporting the dyadic nature of engineering design (problem-solution co-evolution).

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.

The consideration of manufacturing in design is still a challenge for design engineers. This paper presents the results of an interview study, conducted with 15 participants. 4 challenges are identified in this study: Manufacturing-specific knowledge is mostly not sufficient. The design of manufacturing-oriented concepts in terms of costs is difficult. The feasibility of manufacturing is not assessed. The dimension of cost drivers is unknown, hindering optimisation in early stages of design. Based on these results the need for further research emerges regarding the impact of these challenges.

Purposeful qualitative modelling of embodiment function relations is a challenge in embodiment design. This contribution investigates the applicability and usefulness of the Contact and Channel Approach as a qualitative modelling approach in a survey study. From 23 development and research projects, advantages and challenges regarding applicability and usefulness are identified. A further result is that many different models are used additionally to the Contact and Channel Approach. Based on the findings, research potential for optimization and development of links to other models emerges.

It is generally accepted in industry and academia that trade-offs between functional design objectives are an inevitable factor in the development of mechanical systems. These trade-offs can have a large influence on the achievable robustness and performance of the final design, with many products only functioning in narrow sweet-spots between different objectives. As a result, the design process of multi- functional products can be prolonged when designers concurrently attempt to find sweet-spots between a number of potentially interdependent trade-offs. This paper will show that designers only have six different approaches available when attempting to manage a trade-off while trying to ensure robustness and a sufficient performance. These fall within one of three categories; accept, optimise, or redesign. Selecting the wrong approach, can result in consequences downstream which can be difficult to predict, amongst others a lack of robustness to geometric variation, constrained performance, and long development lead time. This points to a substantial potential in the synthesis of design methods that support the identification and management of trade-offs in early product development.

The ever-increasing competitiveness, due to the market globalization, has forced the industries to modify their design and production strategies. A key point is the development of products that fulfil the individual customer needs as close as possible. ETO companies manufacture new products according to the customer technical requirements given in the request for proposal.

Computational Design Synthesis is the research area focused on activities to automate the design phase in the production of products such ETO structures. In this context, Knowledge Based Engineering applications are usually applied to automate design routines and to implement a multidisciplinary product design. Knowledge should be elicited and formalized, so that it can allow the past cases retrieval and the connection between customer specifications and the product configuration tasks. This paper proposes an approach for the rapid definition of the product structure related to a ETO product, including the early cost evaluation in configurations. The research scope aims at defining a framework to support the knowledge repository, which is the Knowledge Based used to design new products and estimate their costs.

In embodiment design, functions are implemented in a technical systems embodiment. For doing so, design engineers need to understand the relations of embodiment and function. Many systems change their states during function fulfilment which complicates their relations and leads to ambiguity in design decisions. The challenge for design engineers is that they often need to make important decisions about the design before they can use sophisticated analytical models to investigate them. This contribution presents a structure for the C&C²-Sequence Model as a non-analytical model to support design engineers in modelling embodiment function relations. This structure contains four dimensions that are derived from the state of the art and preliminary work. It enables the structuring of gained knowledge about embodiment function relations and supports their communication in design engineering teams. Two development projects in academic and corporate environment are conducted using the structure to investigate its applicability. In these projects, design engineers were able to document and use gained knowledge about the investigated complicated systems.

The context of product development can be understood as transformation of needs into technical solutions under the continuous handling of uncertainties. These result particularly in early development phases from a lack of technical knowledge. In order to counter the uncertainties, companies are increasingly implementing agile approaches, which mostly originate in the area of software development. Although these are suitable for flexible handling of project management activities and lead to an increased reactivity of the development team, they do not address the early and continuous integration of technical knowledge into the process. With the aim of optimizing mechatronic systems with regard to their lightweight design potentials, in this article a method is developed that supports agile development with the goal of lightweight design. Therefore, it combines a method for functional modelling with a function-based lightweight design method. The targeted integration of technical knowledge has shown that lightweight design potentials can be optimized iteratively in agile approaches. As an initial validation, the applicability of the method was demonstrated in a development project.

Testing contributes to success of engineering design as it plays an important role in gaining insights about the system in development. Literature indicates that for success in engineering design, gaining insights about relations of embodiment and function is crucial. In this contribution, an investigation of how insights about embodiment function relations are gained in testing, is conducted. For this, the testing documentation in a student development project is analysed. The results show a correlation of gaining insights about embodiment function relations to success in engineering design. Potential for improvement in data acquisition and processing is uncovered, which will be used in a succeeding study to investigate this issue in more detail.

Embodiment design (ED) is an early phase of product development. ED problems consist of finding solution principles that satisfy product requirements such as physics behaviors and interactions between components. Constraint satisfaction techniques are useful to solve constraint-based models that are often partial, heterogeneous, and uncertain in ED. This paper proposes new constraint satisfaction techniques to tackle piecewise-defined physics phenomena or skill-based rules and multiple categories of variables arising in design applications. New search heuristics and a global piecewise constraint are introduced in the branch and prune framework. The capabilities of these techniques are illustrated with both academic and real-world problems. Complete models of the latter are presented.