Machine Learning (ML) techniques are showing increasing use and value in the engineering sector. Object Detection methods, by which an ML system identifies objects from an image presented to it, have demonstrated promise for search and retrieval and synchronised physical/digital version control, amongst many applications.

However, accuracy of detection often decreases as the number of objects considered by the system increases which, combined with very high training times and computational overhead, makes widespread use infeasible.

This work presents a hierarchical ML workflow that leverages the pre-existing taxonometric structures of engineering components and abundant digital models (CAD) to streamline training and increase accuracy. With a two-layer structure, the approach demonstrates potential to increase accuracy to >90%, with potential time savings of 75% and greatly increased flexibility and expandability.

While further refinement is required to increase robustness of detection and investigate scalability, the approach shows significant promise to increase feasibility of Object Detection techniques in engineering.

The paper proposes a methodology for developing an active learning module adapted from the Kansei Engineering methodology that intends to engage and familiarize middle school students with basics of product design and the Kansei product design and culture, and develop design skills. The module includes an identification of the design elements of a selected product, the creation of an items/categories matrix, product clustering according to different criteria (shape, material, principle of operation), an analysis of product functions and main product characteristics, and an evaluation of products with a Kansei scale created by the students. This series of activities lead to a design-thinking-based product-design project culminating in the construction and evaluation of prototypes. It carefully considers how students can gradually learn by going from activity to activity leading to a design project. In doing so, important skills such as, among others, analytical thinking, divergent thinking, and critical thinking are intended to be practiced.

Representation of design information using causal ontologies is very effective for creative ideation in product design. Hence researchers created databases with models of engineering and biological systems using causal ontologies. Manually building many models using technical documents requires significant effort by specialists. Researchers worked on the automatic extraction of design information leveraging the computational techniques of Machine Learning. But these methods are data intensive, have manual touch points and have not yet reported the end-to-end performance of the process. In this paper, we present the results of a new method inspired by the cognitive process followed by specialists. This method uses the Knowledge Graph with Rule based reasoning for information extraction for the SAPPhIRE causality model from natural language texts. Unlike the supervised learning methods, this new method does not require data intensive modelling. We report the performance of the end-to-end information extraction process, which is found to be a promising alternative.

Bio-inspired design (BID) involves generating innovative ideas for engineering design by drawing inspiration from natural biological phenomena and systems, using a form of design-by-analogy. Despite its many successes, BID approaches encounter research challenges including unstructured data and existing models that hinder comprehension and processing, limited focus on finding biological knowledge compared to defined problems, and insufficient guidance of the ideation process with algorithms. This paper proposes a knowledge-based approach to address the challenges. The approach involves transforming unstructured data into structured knowledge, including information about natural sources, their benefits, and applications. The structured knowledge is then used to construct a semantic network, enabling designers to retrieve information for BID in two ways. Furthermore, a three-step ideation method is developed to encourage divergent thinking and explore additional potential solutions by drawing inspiration and utilizing knowledge. The knowledge-based BID approach is implemented as a tool and design cases are conducted to illustrate the process of applying this tool for BID.

Providing the correct destination at the end of the product's use phase is essential for value recovery and to reduce the environmental impact at this lifecycle stage. To understand the e-waste recycling behavior among users, this article aims to identify the most common destinations given to smartphones when they are no longer used. A systematic literature review was carried out, and 13 studies were selected for a meta-analysis. The variable is the selection of the most common destinations for e-waste: reuse and recycling (recovered value), storage and informal collection (missed value), and household waste (destroyed value). The results present a summarized measure with the combined proportion of the studies for each category. Studies were weighted by the precision of confidence interval estimates presented in Forest Plots. The main results point out common problems and demonstrate how the strategies and policies adopted in each country can influence the chosen methods of disposal. These specificities require unique strategies to deal with local problems. As a secondary contribution, this study proposes guidelines to reduce e-waste generation and to create awareness and infrastructure to increase value recovery.

Among the topics of psychological ownership (PO) within current literature, a significant gap exists in understanding PO within a prescriptive lens. This study will examine how instigating the PO mapping method will help us understand how the PO mapping method can support an ownership journey. In addition, we want to see how we can create a prescriptive ownership structure that one follows rather than using the tool as a descriptive method. To do this we will follow a Research Through Design methodology and test the PO mapping method in an organisational case study. We believe that the PO mapping method can help frame and guide organisational project handovers. We want to examine the factors that influence the parties (project teams) emergence and relinquishment of ownership, and how that affects the feeling of ownership of a project over time. Based on this understanding we will derive prescriptive phases to integrate into our PO mapping method. Thus this study demonstrates how the PO mapping method can be used in different contexts to support and provide prescriptive guidance for ownership journeys.

Improvements in product development can increase the competitiveness of firms. However, new product development in CAD systems involves difficulties and uncertainties that increase along with the pressure to develop the products. A distinct characteristic of CAD modeling for new product development is its uncertainty. This is because the information is usually approximate and incomplete during CAD modeling. Thus, the main objective of this paper is to propose a robust and flexible CAD approach to reduce uncertainty and accelerate new product modeling in the context of design for manufacturing. This methodology permits the convergence towards different product forms depending on the selected manufacturing process. Application of this approach has shown that when uncertainty is high, approving a complete CAD modeling results in a delay in product development. In contrast, CAD modeling using fuzzy models results in a gain of valuable development time because the model is completed when knowledge about manufacturing technologies, company fit and capabilities, and markets is available.

The production preparation process (3P) enables collaboration between design and production engineers during product development but its efficiency is limited by the abundance of documentation of manufacturing constraints and capabilities. Empirical studies showed that use of production requirements can increase the efficiency of 3P, however, the support for production engineers to capture and share production requirements is scarce. A method to support production engineers in identifying, defining, structuring and sharing production requirements and collaborating with design engineers is presented. The method has three major parts - focus areas and requirement categories, a worksheet for production requirements capturing and prioritization, and a workflow for using the worksheet. The method was developed in collaboration with practitioners and contributes to the existing knowledge by providing production engineers with a structured way of working with production requirements. Evaluation of the method in the case company showed its usability when developing product variants and that additional work is needed to support the development of new product families and assembly lines.

The product engineering process as part of the product life cycle includes product and production system development as well as production. In integrated product and production engineering (PPE), knowledge transfer is an important success factor. Optimizing the efficiency and effectiveness of knowledge transfers can, for example, support the avoidance of costly, production-related changes to the product design. The current state of research describes different models of knowledge transfer as well as factors that influence it. Some results show how the speed and quality of knowledge transfer can be improved by implementing so-called interventions. However, those models either represent abstract contexts of knowledge transfer or focus only on product engineering. Therefore, a literature analysis is conducted to identify the system of objectives for a method, that supports the improvement of knowledge transfer in PPE. Subsequently, the system of objectives is operationalized to provide the basis for the InKTI – Interdepartmental Knowledge Transfer Improvement Method, which is applicable, supports the user in improving knowledge transfers in PPE, and aims to increase the quality and speed of knowledge transfers.

The part build orientation is a manufacturing variable that must be considered when designing a product to maximise AM opportunities. There are several approaches to selecting the best print direction in the scientific literature by considering different criteria. However, most of the studies are focused on specific AM technologies. It is missing a general method that evaluates a widespread number of criteria. Furthermore, such approaches expect designers establish weights for technical criteria that are too specific, especially during the preliminary design steps. Designers are familiar with criteria like cost-effectiveness, productiveness, quality and mechanical strength.

The paper presents a multi-criteria decision-making approach to optimise the build part orientation in additive manufacturing. The method considers five decision-making criteria (cost-effectiveness, rapidity, productiveness, quality and mechanical strength) and seventeen specific technical criteria. TOPSIS is the method used to optimise the build part orientation. A case study of three components exemplifies the five steps of the procedure.

As the demands for new complex products/services increase, leading to strict constraints on budget and time-to-market, it is hard to learn from experience and improve practice. Improvement can be exercised in all aspects contributing to project management: the skill set of project personnel, the project structure, and the development process. People are the key asset of the project. Identifying the key participants in a project whose role is influential is important for improving the project's success. These people can receive support, remove their burdens, make sure their communication channels work well, etc. This paper offers a multilayer network-based method to examine an actor's influence in a project while combining two additional organizational key aspects: products and processes. Considering these three aspects together allows for a more informed evaluation of the actors' influence on the project. Using the insight from graph theory, we gain indicators related to each network actor. The influence of the actors in a multidimensional network makes it possible to present a clearer picture to decision-makers in the organization to make better decisions related to increasing the effectiveness of the development project.

Social innovations in the energy sector (SIE) are essential for accelerating the transition to clean, renewable, and democratic energy while encouraging citizens' involvement. However, SIE lacks clear boundaries, making it challenging to make design decisions. Clear and effective design decisions can help identify opportunities and constraints that may impact the success of social innovations. To support decision-making in SIE design, this paper proposes a multi-objective decision-support model based on the definition and exploration of the SIE design space. The model integrates various objective functions related to economic, environmental, and social perspectives, to ensure that selected solutions are tailored to the needs of citizens. By exploring the SIE design space, the model allows designers to evaluate the feasibility and effectiveness of different design options and select the most suitable solutions. To illustrate the proposed approach, this paper applies the model to a specific case of SIE: photovoltaic self-consumption. The findings of this paper provide a decision support model to assist SIE designers in making informed design decisions.

With increasing life expectancy, the risk of diseases of the central nervous system, such as cancer, strokes, etc., also increases. Strokes often result in injury to the sciatic nerve, which is responsible for controlling the calf muscles (plantar and dorsal flexors). A so-called ankle joint orthosis (AFO) helps to support the pathological gait and to avoid foot drop during gait. Passive orthoses are of particular importance for research, as they do not require additional incoming energy from outside to the orthotic system. However, current passive orthoses are often not personalized. On the one hand, because they usually have only a temporary muscle-building function and, on the other hand, because the individual design process is computationally time consuming and thus expensive. This paper presents a possibility to pre-dimension and pre-design passive orthoses fast and cost-efficiently by reducing the complexity of the model based on volume-optimized truss elements. Therefor a traditional high calculation intensive design procedure is compared with the complexity reduced model to show its effectiviness and the similarity of the results.

Motor disorders are diseases affecting the muscle function of the human body. A frequently occurring motor disorder affects the lower leg muscles resulting in a pathological gait called foot drop. Patients have a higher risk of stumbling and falling. The most common treatment is the use of a passive ankle-foot-orthosis (AFO). However, the compensation of foot drop is only limited due to the non possible support of all rotational directions of the ankle joint. Therefore, a newly developed concept for a passive AFO is currently in work. To ensure a best possible treatment of the patient, the provided support by the AFO and required support by the patient have to be in accordance. Thus, in this contribution a method is presented that integrates model order reduced finite element analysis for computing the provided support of the AFO and musculoskeletal human models for representing the patients' gait behaviour. With the method, the design of the force generating structures of the AFO can be realized regarding the patients' requirements. The presented method is further evaluated with a specific use case. The main focus lies here in the principal functionality of the method and the provision of valid results.

System maps are established tools in design practice and education as well as non-design research, both as a process and an outcome of systemic analysis and synthesis. Applying causal loop diagrams (CLD's) in (systemic) design research could be of great value to tackle the growing societal complexity. At this moment there is, however, no clear research protocol to include the rich data necessary for systemic research and ensure a scientifically valid system map in the context of design research. In this paper, we propose a protocol using CLD's as a research method and outcome in design-inclusive research. The protocol synthesises rich data from qualitative research in a way that the representation yields higher validity compared to the regular approach to systems mapping, using a process of reflection and iteration on boundary setting. An iterative process of qualitative research methods with lean validation methods was used to come to a conceptual proposal. The protocol can support systemic design researchers and practitioners to include a scientifically grounded CLD in the explorative research actions phase, thus bridging the research towards a phase of creative design actions.

Home hand rehabilitation for stroke is becoming increasingly important due to logistic and financial challenges. Developing Daily-life Integrated Hand-rehabilitation Products (DIHP) aims to enable the application of at-home rehabilitation. The materials of these products are essential for their success, however, selecting materials for DIHP has not been investigated yet. Previous research on material selection showed that it is done strictly on material properties or based on a human-centered approach. Hence, in this study, we propose a hybrid model for choosing materials for DIHP. To achieve this, we first combined the findings of previous material selection processes into a comprehensive material selection model. We applied this model in a case study, in which we first selected three materials based on their properties. Following, we 3d printed a DIHP out of the chosen materials and tested the feeling of the materials with multiple expert groups. Our findings suggest that the proposed material selection method is promising and highlights that our comprehensive model provides more insights when compared to a strict material property-based selection.

The product development process faces several challenges, such as an increasing and differentiated number of customer requirements, increasing product complexity, and shortened time-to-market. To address these challenges, the implementation of automation approaches in form of machine learning (ML) algorithms appears promising. However, companies lack the implementation of these approaches in their processes, inter alia due to inadequate knowledge and experience in this field. Therefore, the aim of this paper is to develop a structured formulized way of characterising ML algorithms, which can support non-experts in identifying the optimal algorithm to solve a given problem. First, existing approaches covering the determination of appropriate ML algorithms for a given task are examined. Based on this, a pattern language approach is introduced to characterise ML algorithms and problems, allowing matching to be performed to identify the most suitable one for a given task. Due to their broad application, the concept is demonstrated by creating patterns for decision trees and artificial neural networks. A study is conducted to prove that the proposed concept is appropriate to support the ML algorithm selection.

Work-integrated learning (WIL) – a pedagogy that integrates academic studies with workplace experiences – presents an excellent opportunity for students to “deliberately practice” their design skills. To date there has been little investigation into the effect(s) of WIL experiences on developing novice designers’ design skills.

We performed a series of longitudinal interview case studies following three engineering students through the course of a 4-month work term. Interviews were semi-structured to gather rich contextual descriptions of participant experiences designing in WIL settings. Transcripts were analysed using an iterative thematic analysis approach.

Results indicate specific areas where WIL helps develop novice designers’ engineering design skills and mindsets beyond their early experiences in the engineering classroom. These include their experiences interacting with clients/users, the importance of project transition considerations, resource coordination, teamwork/collaboration, and the design process. We discuss how the structure of design tasks and their environment differ from the classroom experience, highlighting how WIL can supplement traditional design education.

Real vehicle usage rarely matches the predictions made during early phases of vehicle development and sales processes at commercial road vehicle manufacturers. The automotive industry needs multidisciplinary vehicle design methods to predict real-world vehicle operations by considering the vehicle level and the transport system level simultaneously, in a more holistic approach. The aim of this study was to analyse how realistic vehicle usage of driverless multipurpose vehicles can be modelled in Vehicle Routing Problems (VRPs) by conducting a systematic literature review. We found that real vehicle usage modelling of driverless multipurpose vehicles in VRPs mainly depended on the following elements: VRP variant, energy consumption model, energy consumption rate class, number of vehicle-specific design variables and transport system-level factors. Furthermore, we identified in the literature five classes of energy consumption rate edge behaviour in VRPs. These findings can support decision-making in the modelling process to select the most suitable combination of elements, and their level of detail for the overall modelling aim and purpose.

Stakeholder perspective taking is a critical skill in early-stage problem exploration and framing. We examined stakeholder perspective taking within an early-stage design team of engineers at NASA to begin to understand in what ways and under what conditions designers adopt stakeholder perspectives in the context of professional engineering organizations. Our findings identify a spectrum of perspective taking during problem framing deliberations that ranges from (1) fully taking the stakeholder's point of view (POV), (2) interpreting the stakeholder's POV using the designer's POV, (3) implanting the stakeholder's POV into the designer's POV, to (4) fully taking the designer's own POV. We also identify and describe conditions that appeared to encourage or hinder perspective taking in this setting. These findings are significant because they suggest ways to gauge and encourage the skill of stakeholder perspective taking among professional engineers working on real-world design challenges with societal implications.