The ideation process has a significant impact on the initial concept generation and final product creativity of the design. Visual stimuli play an important role in the process of innovative product design. With the increase in computing capability, generative design methods are widely implemented. In this paper, features of design targets and combinational objects in 2 combinational design tasks are fused using adversarial neural generative networks to form the generated stimuli. It is also used with combinational object pictures to investigate the impact on creativity in design ideation. The study invited designers to use and subjectively self-evaluate the two stimuli in a design task. Through analysis of participant data (n=20), the results showed that the generative stimuli had an advantage over the combinational image stimuli in terms of the smoothness of creativity in the design ideation of outcomes. And there is a positive correlation between designers' years of design education and their tendency to prefer generative stimuli. Based on the results obtained, ideas are provided for the study of the influence of visual and generative stimuli on the designer's ideation process.

Over the last 20 years, finite element analysis (FEA) has become a standard analysis tool for metal joining processes. When FEA tools are combined with design of experiments (DOE) methodologies, academic research has shown the potential for virtual DOE to allow for the rapid analysis of manufacturing parameters and their influence on final formed products. However, within the domain of bulk-metal joining, FEA tools are rarely used in industrial applications and limit DOE trails to physical testing which are therefore constrained by financial costs and time.

This research explores the suitability of an FEA-based DOE to predict the complex behaviour during bulk-metal joining processes through a case study on the staking of spherical bearings. For the two DOE outputs of pushout strength and post-stake torque, the FEA-based DOE error did not exceed ±1.2% and ± 1.5 Nm respectively which far surpasses what was previously capable from analytically derived closed-form solutions. The outcomes of this case study demonstration the potential for FEA-based DOE to provide an inexpensive, methodical, and scalable solution for modelling bulk-metal joining process

Prior work has demonstrated that gender identity affects team psychological safety, which is critical to the development of a shared understanding of the task. Further, we know that a shared understanding can increase team cohesion and team performance. Little work has investigated how gender differences affect communicative acts within the context of design, and more specifically how gender differences may affect the development of a shared understanding of the design concept between designers. As a first step towards filling this gap, the current work presents findings from a controlled study conducted at The Pennsylvania State University with 22 design dyads (44 designers). The findings from this study indicate that gender identity within design dyads does not affect participants’ shared understanding of a design concept.

Artificial intelligence (AI) capable of generating images from a text prompt are becoming increasingly prevalent in society and design. The general public can use their computers and mobile devices to ask a complex text-to-image AI to create an image which is in some cases indistinguishable from that which a human could create using a computer graphics package. These images are shared on social media and have been used in the creation of art projects, documents and publications. This exploratory study aimed to identify if modern text-to-image AI (Midjourney, DALL-E 2, and Disco Diffusion) could be used to replace the designer in the concept generation stage of the design process. Teams of design students were asked to evaluate AI generated concepts from 15 to a final concept. The outcomes of this research are a first of its kind for the field of engineering design, in the identification of barriers in the use of current text-to-image AI for the purpose of engineering design. The discussion suggests how this can be overcome in the short term and what knowledge the research community needs to build to overcome these barriers in the long term.

The industry's interest in Virtual and Augmented Reality (VR and AR) technologies started from the beginning of their appearance in the research world. Over the years, scholars observed ups and downs, to which various factors contributed. In recent years these technologies, now known as eXtended Reality (XR), have returned to fascinate the industrial world, mainly because most of the related enabling technologies have improved to the point of pushing companies to re-invest in them. The introduction of approaches such as the digital twin one and the recent hype on the metaverse is also a push in this direction. A few questions arise: what are the benefits of such technologies in the industry today, and what are the unexplored possibilities? Starting from a systematic literature review and exploring the practical implications of integrating technologies in the industrial field, the paper tries to answer these questions. The paper is not intended as a technological forecast but as a stimulus for future research.

Aspect-based sentiment analysis (ABSA) provides an opportunity to systematically generate user's opinions of specific aspects to enrich the idea creation process in the early stage of product/service design process. Yet, the current ABSA task has two major limitations. First, existing research mostly focusing on the subsets of ABSA task, e.g. aspect-sentiment extraction, extract aspect, opinion, and sentiment in a unified model is still an open problem. Second, the implicit opinion and sentiment are ignored in the current ABSA task. This article tackles these gaps by (1) creating a new annotated dataset comprised of five types of labels, including aspect, category, opinion, sentiment, and implicit indicator (ACOSI) and (2) developing a unified model which could extract all five types of labels simultaneously in a generative manner. Numerical experiments conducted on the manually labeled dataset originally scraped from three major e-Commerce retail stores for apparel and footwear products indicate the performance, scalability, and potentials of the framework developed. Several directions are provided for future exploration in the area of automated aspect-based sentiment analysis for user-centered design.

Research on additive manufacturing has highlighted methods and guidelines to optimise the design process and improving finished product quality. There is still room for improvement in making AM as reliable as more traditional processes when considering industrial use. In terms of manufacturing, managing print parameters properly can improve reproducibility and repeatability of a part, in addition to its fidelity to the basic geometric model. However, a topological optimised geometry requires more than good parameterisation. Efforts are therefore being made to formalise knowledge so that it is explicit and accessible to designers. This paper proposes an approach based on the spatio-temporal evolution of a geometry during printing to quantify data at the meso scale. Previous studies have been conducted on the description of features in time, space and space-time, and on the influence of their arrangement within a part. Building on this work, a parameterised test specimen was designed to measure the quantitative impact of these arrangements on the final product. The method is then presented and illustrated through a case study to help the designer with quantitative predictive values of geometric parameters.

Selecting a suitable file format for data exchange in additive manufacturing is fundamental when designing these digital process chains. Within the scope of this investigation, alternatives to the de-facto industry standard STL are to be found to overcome the disadvantages of the STL-based digital process chain.

Therefore, suitable file formats are identified by conducting literature and market research and evaluated regarding their suitability to support a continuous digital process chain. In addition, typical use cases in additive manufacturing are defined, and their requirements for a file format for data exchange are derived. Finally, for each use case defined, recommended and suitable file formats are proposed.

Design education needs to be continually investigated and improved to stay relevant. The Singapore University of Technology and Design (SUTD) is a leading university focused on all elements of technology-based design. The overarching motivation of our research is to enhance design education curriculum at SUTD. Our research objectives are (1) to understand how design competencies are implemented in SUTD's curriculum and courses (2) to identify gaps in teaching of design competencies at SUTD. We analyse competencies in 28 design courses at SUTD. We develop the visualization and analysis of 12 skills grouped under two abilities from the Design competency assessment framework. Our main contributions are: (1) We present an approach to map the design competencies that are taught across design courses at a university with an interdisciplinary curriculum, (2) We present an approach for mapping design competency progression based on ITAE (Introduced, Taught, Assessed, Expected) categorization, (3) Based on the above, we provide preliminary findings on two skill gaps in the curriculum, substantiated with insights from interviews with course coordinators.

Model-Based Systems Engineering (MBSE) is increasingly used across industries for the integrated modelling of complex systems to support model-based development and provide enhanced traceability between requirements and verification and validation of the system. This paper seeks to strengthen the function modelling methodology in MBSE by introducing an approach based on flow heuristics guided by the System State Flow Diagram schema. This provides function representations with an enhanced integrity in MBSE facilitating the solution-agnostic architecture modelling, and supports integrated simulation and function failure reasoning based on MBSE. The approach is illustrated with a case study of an electric bicycle implemented in the MathWorks System Composer environment.

The evolution of Artificial Intelligence (AI) and Machine Learning (ML) enables new ways to envision how computer tools will aid, work with, and even guide human teams. This paper explores this new paradigm of design by considering emerging variations of AI-Human collaboration: AI used as a design tool versus AI employed as a guide to human problem solvers, and AI agents which only react to their human counterparts versus AI agents which proactively identify and address needs. The different combinations can be mapped onto a 2×2 AI-Human Teaming Matrix which isolates and highlights these different AI capabilities in teaming. The paper introduces the matrix and its quadrants, illustrating these different AI agents and their application and impact, and then provides a road map to researching and developing effective AI team collaborators.

Teaching the same design module to two different cohorts, traditional design students and industry-based students, the outcomes of the conceptual design stage has shown differences in divergence achieved, looking at both number and quality of concepts. The activities of both cohorts across two years are explored, combining on campus studio based teaching and online teaching, through comparison of teaching approaches for both cohorts and their effect on the design outcomes. Findings show that the traditional design students create significantly larger number of concepts, discussed in more detail and engage more fully in the divergence-convergence design process. Then the recommendations are provided for approaches and techniques that could be implemented to the industry-based student teaching to encourage divergence during idea generation. These include increased levels of studio work focused design work separated from industry needs, more structure and mandatory use of all instructed design techniques by inclusion in the assessment, increased focus on intermediate tasks and contextualisation of design terms to the fields they are familiar with.

In the midst of Industry 4.0 where digitalisation is stimulated through the Internet of Things (IoT), Big Data, and machine learning technologies, an increasing volume of valuable data has been acquired from sensors and interconnected devices. This data-driven paradigm can enable organisations to create new or improved products and services, build long-term customer relationships in a value co-creation manner, adapt to continuous business reconfiguration or address societal challenges such as sustainability. Scientific research addressing Data-driven design has increased steadily in the last few years. However, despite this, there is still a need for a comprehensive understanding of data-driven design processes. Thus, through a systematic literature review, we review the data-driven design activities observed in the new product and service development and types of data utilised in New Product Development (NPD) process. This paper contributes to design research and through reviewing the current landscape of Data-driven design identifies ten data-driven design activities and four-dimensional aspects in NPD process.

The Product Carbon Footprint (PCF) has been established over the last few years as a new control variable in product design to quantify the sustainable impact of a product. However, the calculation of the PCF is subject to numerous uncertainties and assumptions, which are no longer represented in the stand-alone value. The uncertainties and assumptions arise at different stages of the calculation of the PCF and consequently create a multidimensional problem, which means that the PCF does not provide a trustworthy basis for comparing different production scenarios. To face this multidimensional issue, in this paper, a methodology for categorization of the different issues and, therefore, of the final PCF is presented. Through this methodology, which is divided into five levels mainly based on the origin, the quality, and the uncertainty of the data, an assessment can be made as to whether the values of the PCFs are comparable in different scenarios. The methodology can therefore help to improve decisions in product development with regard to environmental sustainability.

Sense of agency is the sense that one is causing an action. The increase in machine or system autonomy leads to an increase in the loss of sense of agency for the operation causing the loss of pleasure in the operation or sense of responsibility for the consequences of operations. Designing a sense of agency is necessary, especially in the context of machine autonomy. This calls for the control of the sense of agency, which requires the construction of a model to predict the sense of agency and establishing a design methodology to manipulate the factors of sense of agency. We propose the mathematical model that predicts the sense of agency in a human-machine system based on the comparator model and free-energy principle and what to design to enhance the sense of agency. Proposed model explains the effects of prediction error, prediction uncertainty, and observation uncertainty for body, machine, and environment feedback on the sense of agency. The model generally reveals the interaction effect between prediction error and prediction uncertainty and between prediction error and observation uncertainty. The model prediction can be widely applied as a design guide for enhancing sense of agency of human-machine interfaces.

In this paper, a case study of a redesign process for 3D-printed parts has been analysed. The purpose was to compare the implementation of specialist knowledge in hands-on engineering tasks with the previous experience-based approach. Here, specialist knowledge refers to systematic experimental work as a basis for Computer Aided Engineering (CAE). The case involves a set of compliant arms for an oil extraction device developed by a start-up company. Tensile tests of 3D printed dog-bone were performed to characterise the Young's modulus, tensile strength, and orthotropic behaviour of the material to build a material model based on Finite Element Analysis (FEA). With the material characteristics and three simple tests to estimate the optimisation constraints, the existing solution was disproven. Then, new solution candidates were generated and evaluated with input from the start-up company. The process resulted in a feasible solution as well as a reduction of maximum stress from 54MPa to 20MPa. The case highlights the value of specialist knowledge for characterisation of new technologies and design space constraints to reduce and improve iterations to solve a practical design problem.

Advancements in prototyping technologies – haptics and extended reality – are creating exciting new environments to enhance stakeholder and user interaction with design concepts. These interactions can now occur earlier in the design process, transforming feedback mechanisms resulting in greater and faster iterations. This is essential for bringing right-first-time products to market as quickly as possible.

While existing feedback tools, such as speak-aloud, surveys and/or questionnaires, are a useful means for capturing user feedback and reflections on interactions, there is a desire to explicitly map user feedback to their physical prototype interaction. Over the past decade, several hand-tracking tools have been developed that can, in principle, capture product user interaction.

In this paper, we explore the capability of the LeapMotion Controller, MediaPipe and Manus Prime X Haptic gloves to capture user interaction with prototypes. A broad perspective of capability is adopted, including accuracy as well as the practical aspects of knowledge, skills, and ease of use. In this study, challenges in accuracy, occlusion and data processing were elicited in the capture and translation of user interaction into design insights.

The digitalization trend is finding its way more and more into product development, resulting in new frameworks to enhance product engineering. An integral element is the application of new techniques to existing data, which offers an enormous potential for time and cost savings, because duplicate work in product design and subsequent steps is avoided. The reduction of costs can be further increased through the application as early as possible in the product development process. One solution is outlined in this publication, where the source of available data is principle sketches from engineering design. These represent the basic solution for technical products in a simplified way and are often deployed in the early stages of the development process. This representation enables not only a search of similar sketches but also other fields of interest such as product optimization or the search of CAD-geometries. To utilize this data in a practical way, a procedure is presented which recognizes the symbols of the sketches and subsequently converts them into graphs. An exemplary dataset from different gearbox layouts is used to present the application opportunities by performing similarity searches with multiple input formats.

Smart manufacturing enterprises rely on adapting to rapid engineering changes while minimizing the generated risk. Making informed decisions related to engineering changes and managing risks against unexpected costs requires more information to be extracted from limited data. However, limited information in early-stage design can come in many forms, namely text and images. The development of innovative design tools and processes to link multisource data together is essential to assist designers in building model-based engineering (MBE) systems. However, the formal computational linking of multisource data is yet to be realized in MBE. We propose a framework to implement transfer learning and integrate domain specific knowledge to bridge this information gap. A synthetic dataset is created using web scraping techniques based on keywords extracted from the requirements. Requirement-image pairs are used to fine tune a contrastive language-image pretraining model to acquire domain knowledge. The results demonstrate how the content of images can be used to indicate all affected requirements for tracing engineering changes in a complex system.

Decisions made in the concept generation phase have a significant effect on the product. While product- related risks typically can be considered in the early stages of design, risks such as supply chain and manufacturing methods are rarely easy to account for in early phases. This is because the currently available methods require mature data, which may not be available during concept generation. In this paper, we propose an approach to address this. First, the product and the non-product (manufacturing and/or supply chain) attributes are modelled using the enhanced function means (EF-M) modelling method. The EF-M method provides the opportunity to model alternative solutions-set for functions. Dependencies are then mapped within the product and the manufacturing models, and also in between them. An automatic combinatorial method of concept generation is employed where each generated instance is a design concept-manufacturing method pair. A risk propagation algorithm is then used to assess the risks of all the generated alternatives.