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.

Systems design, being a socio-technical discipline, is highly affected by available technologies, the global economy, the state of the environment, and social issues. Hence, in recent years, the ultimate objective of design started to shift from best-performance systems to ones providing value to customers, enterprises, and society. This paper presents a holistic concept of system value, equipping the stakeholders participating in the design process with a broad view of this measure. The contribution of this paper includes a proposal for general system value taxonomy, which can be used as a foundation for a comprehensive, case-specific, system value model (SVM). As an all-round perspective of value is possible only when all stakeholders are represented, we suggest deploying the PSI framework for mapping the relevant stakeholders. The system value analysis of Tesla's Model Y electrical vehicle is demonstrated, as a test case for SVM application. We conclude that a detailed analysis of SVM, performed by a carefully chosen group of diverse stakeholders, highlights less conventionally discussed aspects of the system during design decision processes, hence is expected to improve the system's overall value.

Transferring natural language requirements to use case diagrams helps to avoid inherent ambiguities. However, this is usually a manual, time-consuming task that can be accelerated by utilizing Artificial Intelligence in terms of Natural Language Processing. Thus, this contribution proposes a conceptual framework for automatically grouping interrelated functional requirements and deriving use case diagrams by combining formerly isolated approaches. Moreover, the latter are evaluated by a qualitative potential analysis to support their future industrial application.

Innovation projects are characterized by numerous uncertainties. Typical concepts in development management like the application of safety coefficients imply limitations of the solution space. In contrast, explicit handling of uncertainties can support engineers in understanding the problem space and in utilising the full potential of the design space along iterative product development steps. As a result from literature analysis, there is a lack of a support for product development that addresses the specific problem of uncertainty and risk in the context of requirement changes. The aim of the contribution at hand is to enhance the efficient development of complex interdisciplinary systems by enabling uncertainty handling in requirements change management. Based on a classification of uncertainty types resulting in a descriptive model, risk management measures are identified to support requirements engineers. The proposed method includes identification & modelling, analysis, treatment and monitoring of risks and counter-measures. By applying this method, engineers are supported in adopting agile approaches and enabling flexible Requirements Engineering.

The paper presents the application of non-specialized lexical database and semantic metrics on transcripts of co-design protocols. Three different and previously analyzed design protocols of co-creative sessions in the field of packaging design, carried out with different supporting tools, are used as test-bench to highlight the potential of this approach. The results show that metrics about the Information Content and the Similarity maps with sufficient precision the differences between ICT- and non-ICT-supported sessions so that it is possible to envision future refinement of the approach.

One of the foundations of product design is the division between production and design. This division manifests as designers aspiring to create fixed iconic archetypes and production replicates endlessly in thousands or millions. Today innovation and technological change are challenging this idea of product design and manufacturing. The evolution of Rapid Prototyping into Additive Manufacturing (AM), is challenging the notion of mass manufacture and consumer value. As AM advances in capability and capacity, the ability to economically manufacture products in low numbers with high degrees of personalisation poses questions of the accepted product development process. Removing the need for dedicated expensive tooling also eliminates the cyclical timescales and commitment to fixed designs that investment in tooling demands. The ability to alter designs arbitrarily, frequently and responsively means that the traditional design process need not be applied and because of this, design processes and practice might be radically different in the future. In this paper, we explore this possible evolution by drawing parallels with principles and development models found in software development.

Based on circular economy readiness assessments of six value chain layers, 18 design strategies and five collaboration strategies for circular economy were identified. The design strategies have many applications, from the design of materials, products, and processes to business models, and while some are specific to determined layers, others can be addressed by companies in multiple layers. Furthermore, collaboration across the value chain was found to affect positively the employment of circular economy design strategies, contributing to the fulfilment of solutions’ circularity potential.

Sustainable Design Engineering (SDE) is an emerging research field and the development of programmes aiming at educating sustainable design engineers is very limited. One example is the SDE program at the Aalborg University in Copenhagen, which is based on a Problem Based Learning (PBL) model. In this article we aim to address the following three research questions: i) why Sustainable Design Engineering? ii) what is Sustainable Design Engineering? iii) How can Sustainable Design Engineering be implemented? By means of two examples from master thesis projects in the building and food sectors.

We developed a wearable experimental sensor setup featuring multimodal EEG+fNIRS neuroimaging applicable for in situ experiments of human behavior in interaction with technology. A low-cost electroencephalography (EEG) was integrated with a wearable functional Near-Infrared Spectroscopy (fNIRS) system, which we present in two parts. Paper A provide an exhaustive description of setup infrastructure, data synchronization process, a procedure for usage, including sensor application, and ensuring high signal quality. This paper (Paper B) demonstrate the setup';s usability in three distinct use cases: a conventional human-computer interaction experiment, an in situ driving experiment where participants drive a car in the city and on the highway, and an ashtanga vinyasa yoga practice in situ. Data on cognitive load from highly ecologically valid experimental setups are presented, and we discuss lessons learned. These include acceptable and unacceptable artefacts, data quality, and constructs possible to investigate with the setup.

This paper presents an educational game fostering a new experience-based approach to teaching knowledge transfer using a codification strategy alone. The goal is to address and highlight some common issues and challenges that occur during knowledge transfer in product development and that are often difficult for especially students to grasp through exclusively a theoretical teaching approach. The game is introduced to 60 students in the final year of their Master's curriculum. In parallel, the game has been applied in a similar setting in a comparable higher educational institution, as well as in a product development organization.

“Sometimes you win—other times you lose and learn.”

The importance of forming concepts in one's mind has been argued from various perspectives in design studies. This experimental study examines how the co-creative process affects concept formation considering its depth.

The authors conducted a learning experiment applying three processes; non-interactive (NI), interactive but non-co-creative (NC), and interactive and co-creative processes (C). To evaluate whether and how deep the concept is formed in the examinee's mind, mimetic Japanese words, which contain several different explicit concepts underlying a certain integrated implicit concept, were chosen as learning materials. The examinees without any knowledge about mimetic Japanese words were gathered globally and the experiment was conducted fully online using English. Examinees were tested several times to measure how they had formed these concepts for comparing the processes.

The findings suggest that the co-creative process enhances the depth of concept formation: involvement load and willingness to participate in the co-creative process lead to deeper concept formation.

Boundaries between products and services vanish and companies increasingly offer hybrid solutions known as Product Service Systems (PSS). Thereby, a holistic view that includes both the product and service parts must be taken. This paper presents a discrete-event simulation of a PSS as a method for the specification of the product part. Besides product and service, the application scenario and the decision-making are also modeled. Based on the results for a case study, the customer requirements could be guaranteed as the essential product specifications were optimized to minimize total costs.

Designing products with a focus on self-explanatory assembly can reduce the use of procedural instructions and the associated problems. This paper describes how different groups of students, in two different design-engineering courses designed or redesigned products in an attempt to make the assembly of the product self-explanatory. The design outcomes are discussed in relation to the design context and linked to existing theory on design for meaning.