Data-driven technologies have found their way into all areas of engineering. In product development they can accelerate the customization to individualized requirements. Therefore, they need a database that exceeds common product data management systems. The creation of this database proves to be challenging because in addition to explicit standards and regulations the product design contains implicit knowledge of product developers. Hence, this paper presents an approach for the semantic integration of the engineering design (SeED). The goal is an automated design of an ontology, which represents the product design in detail.

SeED fulfils two tasks. First, the ontology provides a machine-processable representation of the products design, which enables all kind of data-driven technologies. Among other representations, the ontology contains formal logics and semantics. Accordingly, it is a more comprehensible solution for product developers and knowledge engineers. Second, the detailed representation enables discovering of intrinsic knowledge, e.g. design patterns in product generations. Consequently, SeED is a novel approach for efficient semantic integration of the product design.

Product development companies are collecting data in form of Engineering Change Requests for logged design issues and Design Guidelines to accumulate best practices. These documents are rich in unstructured data (e.g., free text) and previous research has pointed out that product developers find current it systems lacking capabilities to accurately retrieve relevant documents with unstructured data. In this research we compare the performance of Search Engine & Natural Language Processing algorithms in order to find fast related documents from two databases with Engineering Change Request and Design Guideline documents. The aim is to turn hours of manual documents searching into seconds by utilizing such algorithms to effectively search for related engineering documents and rank them in order of significance. Domain knowledge experts evaluated the results and it shows that the models applied managed to find relevant documents with up to 90% accuracy of the cases tested. But accuracy varies based on selected algorithm and length of query.

In the era of knowledge networking, the structure and production mode of knowledge are constantly changing. This article creatively introduces the knowledge mapping method in design research, and based on the perspective of the National Natural Science Foundation of China (NSFC) to compile literature, uses word frequency analysis, co-word analysis, and citation analysis to construct knowledge graphs of design science. This study graphically shows the distribution and flow law of knowledge within design discipline and probes into the research frontier and evolution trend of Chinese design science.

One of the aims of systems engineering is to develop systems with a number of pre-defined configurations, in order to operate effectively and efficiently in different contexts and environments. Early in the design phase, system reconfiguration allows to propose and optimize these configurations. With regard to the literature review and industrial observation, pre-defining the standard configurations without relying on hints from end users has been raised as a major difficulty within the industry. In this paper, we propose a reconfiguration framework which considers data collected from the use phase in order to generate valid and optimized configurations with regard to stakeholders needs.

By applying data analytics to product usage information (PUI) from combinations of different channels, companies can get a more complete picture of their products’ and services’ Mid-Of-Life. All data, which is gathered within the usage phase of a product and which relates to a more comprehensive understanding of the usability of the product itself, can become valuable input. Nevertheless, an efficient use of such knowledge requires to setup related analysis capabilities enabling users not only to visualize relevant data, but providing development related knowledge e.g. to predict product behaviours not yet reflected by initial requirements.

The paper elaborates on explorations to support product development with analytics to improve anticipation of future usage of products and related services. The discussed descriptive, predictive and prescriptive analytics in given research context share the idea and overarching process of getting knowledge out of PUI data. By implementation of corresponding features into an open software platform, the application of advanced analytics for white goods product development has been explored as a reference scenario for PUI exploitation.

Cognitive assistants such as IBM Watson and Siri are at the forefront of social and technological innovation and have the potential to solve many unique problems. However, the lack of standardization and classification within the field impedes critical analysis of existing cognitive assistants and may further inhibit their growth into more useful applications. This paper discusses the development of an ontology, its classes, and subclasses that may serve as a foundation for defining and differentiating CAs. Specifically, the four suggested classes include: learning, intelligence, autonomy, and communication. Various assistants are described and categorized using the proposed system. Our novel ontological framework is the first step towards a classification system for this burgeoning field.

This paper is contextualized in a research project that aims to create a new paradigm to support the design process, substituting the sequential nature of design process models by a flexible structure. To implement this paradigm, we must identify the final and intermediate results of the design process, such as documents, models, artefacts, among others. However, design research is wide and multidisciplinary, resulting in non-uniformity of the terminology across research communities, what hinders the results identification by means of a literature review. This paper aims to identify the terms employed by different research communities to refer to the intermediate and final results of the design process, structuring synonym terms across research communities and establishing how those terms interrelate in the design ontology. Using literature review, the following terms were analysed: design objects, elements, deliverables, entities, information, components, data, and artefacts. The results provide a holistic view of how the terms are employed throughout research communities, supporting the creation of search strings and pointing out opportunities for improving the design ontology.

The design of topologically optimized structures is straightforward, although the main problem is really to derive the correct structure in each instance. During the development of structures for additive manufacturing in particular, saving weight is crucial because weight is proportional to cost.

In this contribution firstly, different approaches to topological optimization are presented and discussed. While computer-assisted tools provide high accuracy and demand defined conditions, approaches utilizing a pen and paper can be conducted relatively quickly, although these only provide little guidance.

Secondly, a new approach is presented which is advantageous with regard to effort and affordability, yet which maintains an accuracy of results. To support the designer, an artificial neural network is trained to adapt suitable Michell structures within a given design area. These structures depict optimal paths to conduct the loads through a component and provide guidance in designing an appropriate topology.

Evaluation has demonstrated that this new approach is capable of supporting designers in achieving lightweight structures.

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.

Lightweight potential is a powerful indicator – but not as powerful as it could be. Current methods for analyzing a product's potential to be reduced in mass only deal with a few of the most important criteria for lightweight design. The amount of literature dealing with lightweight design is significant, yet it can help to understand these versatile criteria. Firstly, the literature on this topic will therefore be reviewed to derive a broad set of criteria used in contemporary lightweight design. Secondly, a further review will reveal the criteria used to derive lightweight potential. Subsequently, both sets will be compared to identify the missing criteria used for the derivation of lightweight potential. This will support designers in two ways. On the one hand, matching and combining both criteria sets will enable the most representative criteria for a particular design case to be chosen, thus leading to a more comprehensible derivation of lightweight potential. On the other hand, the combination set will provide a basis for designers and design teams to refine their understanding of their own motivations for conducting lightweight design.

Even though providing excellent specific stiffness and strength properties, high specific energy absorption and a great degree of design freedom, fibre-reinforced plastics still have to make their way into higher volume applications. Addressing the manufacturing challenges, particularly efficient production techniques are Automated Tape Laying (ATL) and Automated Fiber Placement (AFP), as pointed out by various studies and use cases. However, current Computer Aided Engineering approaches for optimised laminate design still lack the capability to produce results suitable for ATL/AFP. A new method for deriving tape courses from any finite element laminate optimisation result is presented and applied to a virtual demonstrator. An outlook is given on further necessities of extending current laminate optimisation approaches.

Multi-material structures are a trending topic for the industry. With a high application potential, such as lightweight or extended life cycle, different manufacturing technologies are further developed for this intent. One of these technologies is Tailored Forming, a process-chain capable of joining different metals and creating massive hybrid components. In parallel to this development, new challenges rise for design, which has the responsibility of finding an optimal use of this technology and produce higher- performance products. However, this task cannot be solved by conventional engineering approach, since strong manufacturing constraints are involved and a lack of understanding about the joining zone formed between the materials still exists. To fill this gap, the objective of this study is to analyse the influence of the joining zone design over the structure behaviour and establish a suitable design method. For that, a computer-aided environment was constructed and a parametric sensitivity analysis was executed, taking a hybrid shaft as example. At the end, the simulation's results allowed a multi-objective optimisation and were able to generate first design guidelines.

A workflow for the design process of technological products was derived from a model that describes the interplay of lightweight design principles in the human locomotive system. This workflow is not yet ready to be used. In this paper, starting points for new research with the goal to enable the use of the workflow are discussed. Using the interplay of lightweight design principles of the human body in technical applications is approached because it is claimed that the interplay leads to an additional reduction of mass. This was proven for a technological system in a previous study. This study lead to a workflow to consider the interplay of the principles in the design process of the technological system. In this paper, the essential parts of this workflow are described in an abstract diagram as a calculation workflow. Subsequently, inputs and outputs of the workflow are identified. Then, the calculation workflow is integrated into the process of design. Afterwards, it is discussed that tension chording, which is one of the lightweight design principles, needs further investigation, because the interplay of the principles can only be used if the principles themselves are used.

Shortening product development cycles while improving cost efficiency and quality epitomize a key challenge in today's competitive market environment. Integrated approaches simultaneously taking into account a conceptual design, material and processing definition methodologically facilitate the progress of promising product solutions most effectively. However, assorted approaches in the field of lightweight design as well as material selection mostly trying to cover alternative solutions on a component-specific level exclusively, yet.

Thus, this contribution outlines a cross-component material selection for function-based lightweight design within the Extended Target Weighing Approach covering the identification and evaluation of lightweight design potentials. The developed method is based on Ashby's material selection additionally taking into account project objectives for mass, costs and CO2 emissions in individual functional design spaces. Resulting in material combinations fitting to clearly stated project targets, the product engineer is already supported in an early phase of product development when initially assessing feasible materials for the overall system development.

Lightweight Design as an engineering domain is becoming more and more important in terms of sustainable mobility. Therefore, a large number of researchers is developing methods for utilisation of modern, but as well more complex materials with high lightweight potential. One subgroup of these materials are fibre-reinforced plastics (FRP). A lot of work is done supporting the design engineer in exploiting the structural and mechanical behaviour as good as possible. Whereas variations of laminate parameters, resulting from production, are poorly studied. Their impact especially on defined measures under load is of high importance, e.g. having a look on clearances in automotive industry. Because of the high complexity of FRP-parts, resulting from many laminate parameters, tolerancing is not an intuitive process. This is reflected in the fact that there is no defined procedure for tolerancing of FRP- parts. To support the design engineer the authors perform sensitivity analysis for simple loadcases to identify layers with a high importance on a defined measure. The results then are generalised to provide general guidelines to the design engineer.

Combinational creativity is a significant element of design in supporting designers to generate creative ideas during the early phases of design. There exists three driven approaches to combinational creativity: problem-, similarity- and inspiration-driven. This study provides further insights into the three combinational creativity driven approaches, exploring which approach could lead to ideas that are more creative in the context of practical product design. The results from a case study reveal that the problem- driven approach could lead to more creative and novel ideas or products compared with the similarity- and inspiration-driven approach. Products originating from the similarity- and inspiration-driven approach are at comparable levels. This study provides better understanding of combinational creativity in practical design. It also delivers benefits to designers in improving creative idea generation, and supports design researchers in exploring future ideation methods and design support tools employing the concept of 'combination'.

In this paper, we present the results of a survey of new product development practitioners regarding their design review experiences. We surveyed 128 product development professionals on their experience and preferences in design reviews. We found that the goals and type (location / synchronicity) of design reviews change over the course of a product development project. We found that the majority of design review meetings continue to be held as co-located, live, in-person meetings. For reviewing 3D models, we found that a native CAD package (rather than a viewer, or fixed views, or a physical prototype) is the most commonly used tool. We found a difference between Designers (more likely to be product engineers) and Non-Designers and their access to CAD software, as well as their preference for which tool to use at the design review for 3D model evaluation. We hope that our findings spark future work related to better understanding design reviews and design reviewers in context. Design reviews are an important part of industrial product development processes, so we believe future studies have a large potential to improve these design activities

Experiments that study engineering behavior in design often rely on participants responding to a given design prompt or a problem statement. Moreover, researchers often find themselves testing multiple variables with a relatively small participant pool. In such situations multiple design prompts may be used to boost replication by giving each participant an equivalent problem with a different experimental condition. This paper presents a systematic approach to compare given design prompts using a two-step process that allows an initial comparison of the prompts and a post-experiment verification of the similarity of the given prompts. Comparison metrics are provided which can be used to evaluate a level of similarity of existing prompts as well as develop similar problems. These metrics include complexity (size, coupling, and solvability), familiarity, and prompt structure. Statistical methods are discussed for post-experiment verification. Guidelines are provided for a post-experiment survey which may be used for an additional perspective of prompt similarity. The proposed approach is demonstrated using an experiment where two design prompts were used for within-subject replication.

The Engineering Design field is growing fast and so is growing the number of sub-fields that are bringing value to researchers that are working in this context. From psychology to neurosciences, from mathematics to machine learning, everyday scholars and practitioners produce new knowledge of potential interest for designers.

This leads to complications in the researchers’ aims who want to quickly and easily find literature on a specific topic among a large number of scientific publications or want to effectively position a new research.

In the present paper, we address this problem by using state of the art text mining techniques on a large corpus of Engineering Design related documents. In particular, a topic modelling technique is applied to all the papers published in the ICED proceedings from 2003 to 2017 (3,129 documents) in order to find the main subtopics of Engineering Design. Finally, we analyzed the trends of these topics over time, to give a bird-eye view of how the Engineering Design field is evolving.

The results offer a clear and bottom-up picture of what Engineering design is and how the interest of researchers in different topics has changed over time.

Insufficient design often causes challenges to users on a cognitive level, hindering them from interacting with products smoothly. There is a lack of effective design tools and supporting materials that can help designers to understand human cognition and how it affects the way that users experience and use products and services. This paper aims to identify current approaches that can be applied to address this issue, and to examine their strengths and weaknesses. This helps to identify future directions for developing and improving cognitive design supports. A literature review was conducted of research publications in the fields of both design and cognition. Four key approaches are identified: cognitive design principles/guidelines, the demand-capability approach, cognitive walkthrough and cognitive modelling. Their strengths and weaknesses are analyzed from a design standpoint. The paper also analyses the underlying causes of the insufficient uptake of cognitive design approaches by designers.