Additive manufacturing methods present prospects for designed mechanical deformation via the integration of controlled anisotropic lattice structure forms. Their assimilation into a Design for Additive Manufacturing (DfAM) process would create a novel framework for the design of compliant mechanisms (CM). The method uses lattice structures to replace rigid multi-part mechanisms, with integrated and controlled flexibility into a single, compact, and precise component. In recent years, a lot of research has gone into making algorithms that enable users to generate CMs for their designs. But by relying on algorithms to design solutions, are they neglecting to fully understand how these mechanisms work. This work undertakes the design and development of a novel DfAM Framework, that utilises controlled lattice structure deformations to create a standardised method of CM design. The authors have developed a method for this, whilst allowing users to tailor CMs to their design, by using a wide selection of pretested structures. Indicating suitable structures for their design using an integrated novel taxonomy. The framework is tested and developed using a series of case studies.

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.

Gaze sharing is an emerging technology which can enhance human communication and collaboration. As such, it is expected to play an important role in future educational practices. To date, its application was not explored in the context of design education. Additionally, it was mainly implemented in non-immersive environments, which are limited in their potential for engaging learners. Acknowledging the growing interest in immersive learning, as well as the promise made by gaze sharing technology, we strive to develop an immersive gaze sharing environment, to support design education. As a first step, we have implemented and tested an immersive gaze sharing system for supporting design learners. The system was then tested by focusing on a scenario of architectural design, with the aim of collecting valuable user feedback regarding its usability and potential. Our initial user study informs developers of such systems regarding potential issues that may be encountered during deployment in a real-world setting and proposes concrete ways to address these. These insights can help to pave the way for integrating gaze sharing system into design education practices in the near future.

The purpose of the study is to understand the design considerations for creating a provocative financial planning toolkit with services to help facilitate more constructive and meaningful conversations to build trust and empathy between financial advisors and senior people. We conducted four rounds of 60-minute co-creation workshops with eight invited participants from various design disciplines to work in pairs to generate four preliminary concepts suggesting design considerations. We used ATLAS.ti to do qualitative research analysis under an NCT (notice, collecting, thinking) model to identify 22 codes synthesized from verbal and behavioral data. The study concluded with three design principles: 1) the concept of financial planning is about people's expectation management, 2) a financial planning toolkit is designed under service systems, and 3) the tailor-made and modular design features can give financial advisors more flexibility to engage with senior people and enable them to share more about their life stories and needs to recommend financial planning packages precisely according to personal preference, health conditions, and financial status.

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.

Drive units of eBikes are used in every type of bicycle and for different riding scenarios and riders. Due to the different riders and bike types, an enormous variety of influencing parameters and load spectra must be considered during the design process. Therefore, in this paper, a systematic approach for the optimization of the drive unit is presented, which adopts and combines several approaches from design theory. The focus is on efficient modeling and simulation of the relevant parameters and load spectra to minimize uncertainties in the design process.

Based on a system analysis, dimension-reduced parameter spaces are formed for the simulation of the system, meta-models are integrated into the simulation model and the results of the simulation are transferred into a data-based surrogate model to cover the parameter space in an efficient way with a minimum number of time consuming FE simulations. Furthermore, a coordinate-based evaluation method is presented for the FE model in order to form the input for the surrogate model, reduces the amount of data, and to allows a geometry- and mesh-independent evaluation to compare different models.