Engineering Design Community Research Articles

Advancing engineering design problem-exploring practice: interviews with industry professionals

Studies highlight that conceptualising and identifying a new engineering design problem (EDP) is vital, as the solution can benefit society. However, this essential activity, referred to as engineering design problem-exploring (EDPE), is lacking in practice in engineering design. Design engineers appear to focus on providing an engineering design solution (EDS) while their role in EDPE is rarely practised. A new EDP drives innovations and inventions, and there is a need to encourage, advance and sustain the practice of identifying new EDPs. The aim of this study is to empirically highlight the underlying determinants of the scarce practice of EDPE and suggest how to advance and sustain the practice. Interviews were conducted with 32 professionals within the engineering design community, comprising 28 practitioners and four specialists – a lecturer, an inventor, and two expert trainers in creativity and problem-solving. The results of the analyses informed the suggested approaches in this study to advance and sustain the EDPE practice.

Design and Justice: A Scoping Review in Engineering Design

Abstract Our society faces complex social justice challenges, often exacerbated by existing engineered products and technologies. To avoid unintentionally contributing to social justice, engineering designers play a critical role in creating and studying products and technologies that can aim to address challenges of social injustice. There is a growing priority in the engineering design research community to incorporate justice into design and to do so meaningfully and intentionally. Therefore, there is a need to more deeply understand how scholars have integrated concepts of justice into design and to bring to light areas of future research. In this paper, we conduct a scoping review of design and justice in twelve scholarly venues relevant to the engineering design community. A scoping review allowed for a broad range of topics to be covered to identify major research themes and gaps, and to explore boundaries of the nascent study of design and justice. After searching the relevant venues, we conducted a thematic analysis to capture the major themes in the dataset of papers relating to justice and design. Along with relevant terminology used, we found that scholars connected justice to design in ways that we categorized into three main areas of design: Designers, Design Outcomes, and Design Process. Our analysis highlighted areas of future research in studying justice as relevant to Designers, Outcomes, and Processes, as well as identifying an overall call to redefine the field of design in the pursuit of justice.

Generative vs. Non-Generative Models in Engineering Shape Optimization

Generative models offer design diversity but tend to be computationally expensive, while non-generative models are computationally cost-effective but produce less diverse and often invalid designs. However, the limitations of non-generative models can be overcome with the introduction of augmented shape signature vectors (SSVs) to represent both geometric and physical information. This recent advancement has inspired a systematic comparison of the effectiveness and efficiency of generative and non-generative models in constructing design spaces for novel and efficient design exploration and shape optimization, which is demonstrated in this work. These models are showcased in airfoil/hydrofoil design, and a comparison of the resulting design spaces is conducted in this work. A conventional generative adversarial network (GAN) and a state-of-the-art generative model, the performance-augmented diverse generative adversarial network (PaDGAN), are juxtaposed with a linear non-generative model based on the coupling of the Karhunen–Loève Expansion and a physics-informed shape signature vector (SSV-KLE). The comparison demonstrates that, with an appropriate shape encoding and a physics-augmented design space, non-generative models have the potential to cost-effectively generate high-performing valid designs with enhanced coverage of the design space. In this work, both approaches were applied to two large foil profile datasets comprising real-world and artificial designs generated through either a profile-generating parametric model or a deep-learning approach. These datasets were further enriched with integral properties of their members’ shapes, as well as physics-informed parameters. The obtained results illustrate that the design spaces constructed by the non-generative model outperform the generative model in terms of design validity, generating robust latent spaces with no or significantly fewer invalid designs when compared to generative models. The performance and diversity of the generated designs were compared to provide further insights about the quality of the resulting spaces. These findings can aid the engineering design community in making informed decisions when constructing design spaces for shape optimization, as it has been demonstrated that, under certain conditions, computationally inexpensive approaches can closely match or even outperform state-of-the art generative models.

DCG-GAN: design concept generation with generative adversarial networks

Abstract Generative adversarial networks (GANs) have recently been proposed as a potentially disruptive approach to generative design due to their remarkable ability to generate visually appealing and realistic samples. Yet, we show that the current generator-discriminator architecture inherently limits the ability of GANs as a design concept generation (DCG) tool. Specifically, we conduct a DCG study on a large-scale dataset based on a GAN architecture to advance the understanding of the performance of these generative models in generating novel and diverse samples. Our findings, derived from a series of comprehensive and objective assessments, reveal that while the traditional GAN architecture can generate realistic samples, the generated and style-mixed samples closely resemble the training dataset, exhibiting significantly low creativity. We propose a new generic architecture for DCG with GANs (DCG-GAN) that enables GAN-based generative processes to be guided by geometric conditions and criteria such as novelty, diversity and desirability. We validate the performance of the DCG-GAN model through a rigorous quantitative assessment procedure and an extensive qualitative assessment involving 89 participants. We conclude by providing several future research directions and insights for the engineering design community to realize the untapped potential of GANs for DCG.

Digital engineering transformation with trustworthy AI towards industry 4.0: emerging paradigm shifts

Digital engineering transformation is a crucial process for the engineering paradigm shifts in the fourth industrial revolution (4IR), and artificial intelligence (AI) is a critical enabling technology in digital engineering transformation. This article discusses the following research questions: What are the fundamental changes in the 4IR? More specifically, what are the fundamental changes in engineering? What is digital engineering? What are the main uncertainties there? What is trustworthy AI? Why is it important today? What are emerging engineering paradigm shifts in the 4IR? What is the relationship between the data-intensive paradigm and digital engineering transformation? What should we do for digitalization? From investigating the pattern of industrial revolutions, this article argues that ubiquitous machine intelligence (uMI) is the defining power brought by the 4IR. Digitalization is a condition to leverage ubiquitous machine intelligence. Digital engineering transformation towards Industry 4.0 has three essential building blocks: digitalization of engineering, leveraging ubiquitous machine intelligence, and building digital trust and security. The engineering design community at large is facing an excellent opportunity to bring the new capabilities of ubiquitous machine intelligence, and trustworthy AI principles, as well as digital trust, together in various engineering systems design to ensure the trustworthiness of systems in Industry 4.0.

MBSE WITHIN THE ENGINEERING DESIGN COMMUNITY – AN EXPLORATORY STUDY

AbstractObjective of this contribution is to present the use of Model-based Systems Engineering within the engineering design community. Based on a differentiation between SE and MBSE, the definition of three core MBSE elements, namely modelling method, modelling language, and modelling tool as well as the three major aspects of a consistent system model (requirements, behaviour, and structure) a structured review is conducted, focussing on the understanding and motivation as well as the modelling of systems. The review includes 93 publications from Design Society library and proceedings of the CIRP Design conferences during the period from 2011 to 2022. The review points out, that there is an increasing application of MBSE within the engineering design community, mainly focussing on architecture definition or combined engineering activities. Only a small portion of works (16 publications) are providing a consistent approach as these publications link all aspects of the system model and consider all three MBSE elements. It can be concluded, that there is a diffuse understanding of MBSE and different motivations are given to apply more formal system models as well as modelling tools.

A STUDY OF THE EARLY-STAGE ENGINEERING DESIGN ACTIVITIES IN PRACTICE

AbstractThe early-stage engineering design activities include conceptualising, identifying, and solving an engineering design problem. These activities are essential and standard roles of a design engineer. However, they seem to lack comprehensive practice within the engineering design community. In this study, semi-structured interviews conducted with 18 participants having engineering design backgrounds are presented. The aim of the interviews is to investigate the awareness and practice of the early stage engineering design activities. The participants interviewed practice in countries including France, the United Kingdom, and the United States. Contrary to standard expectations, the results of the interviews show that the early-stage engineering design activities are not comprehensively practised. The results suggest that design engineers' crucial role in identifying unknown problems lacks practice. Also, the data from the interviews provide empirical evidence on the determinants for the lack of comprehensive practice of early-stage engineering design activities. Recommendations on possible interventions to support the practice are presented to expedite innovations and inventions.

ARE GENERATIVE ADVERSARIAL NETWORKS CAPABLE OF GENERATING NOVEL AND DIVERSE DESIGN CONCEPTS? AN EXPERIMENTAL ANALYSIS OF PERFORMANCE

AbstractGenerative Adversarial Networks (GANs) have shown stupendous power in generating realistic images to an extend that human eyes are not capable of recognizing them as synthesized. State-of-the-art GAN models are capable of generating realistic and high-quality images, which promise unprecedented opportunities for generating design concepts. Yet, the preliminary experiments reported in this paper shed light on a fundamental limitation of GANs for generative design: lack of novelty and diversity in generated samples. This article conducts a generative design study on a large-scale sneaker dataset based on StyleGAN, a state-of-the-art GAN architecture, to advance the understanding of the performance of these generative models in generating novel and diverse samples (i.e., sneaker images). The findings reveal that although StyleGAN can generate samples with quality and realism, the generated and style-mixed samples highly resemble the training dataset (i.e., existing sneakers). This article aims to provide future research directions and insights for the engineering design community to further realize the untapped potentials of GANs for generative design.

CROWDSOURCING ENGINEERING DESIGN PROBLEMS: LEARNING FROM EXPERIENCES

AbstractThe availability of digital data in the fourth industrial revolution brings different trends with new opportunities and challenges for the engineering design community. As an opportunity, these trends would impact engineering design. However, the challenge is finding applications for these trends in engineering design. Crowdsourcing is one of the trends inspired by digital data. It is outsourcing an individually performed task to be mass-performed. This paper explores the application of crowdsourcing in identifying engineering design problems. Identifying an engineering design problem is an aspect of engineering design considered challenging but necessary for inventions. Secondary data from 63 invention-related cases and an interview with a renowned UK inventor are presented. The data contains scenarios on how the engineering design problems solved to qualify for a UK patent grant or application are identified. Lessons from the case studies are presented and discussed, especially regarding crowdsourcing engineering design problems. These seem to be promising ways of supporting the identification of new engineering design problems with inventive benefits once solved.

Three-Dimensional Numerical Simulation for Lateral Distortional Buckling Behaviour of Steel-Concrete Composite Beams

Lateral Distortional Buckling (LDB) is a mode of instability in Steel-Concrete Composite (SCC) beams that is yet to be fully understood by the structural engineering research and design community. This work investigated numerical methods to understand the LDB behavior of SCC beams under negative moments that exhibit nonlinear behavior. Focus of the study was to develop and validate 3-Dimensional finite element (FE) models which capture the LDB behavior of SCC beams precisely. Detailed 3-Dimensional FE models of the SCC beams were developed using ABAQUS. For modeling concrete, a nonlinear damage plasticity model was considered. A quad-linear curve was used to describe the stress-stain relationship for the steel beam. The stress-stain relationship for the rebar was described using a bi-linear curve while an elastic perfectly plastic model was assigned to the headed stud shear connectors. Steel hardening behavior was simulated using an isotropic hardening model. The interaction between the concrete slab, steel beam, and studs was described using appropriate interface elements combined under suitable constraints. The FE model’s accuracy was validated by results obtained from previous experiments found in literature together with a brief description of the experiments done by previous researchers to ensure completeness. Validation of the model was done by comparing Moment-Rotation curves obtained from the experimental tests, lateral displacements of the bottom flange along the axial direction and comparison of the failure modes between the numerical model and experimental test specimens. It was observed that the maximum relative error for the ultimate moment capacity of the composite beams from FE analysis and the experimental tests was less than 2% which shows that the FE model was in strong agreement with the experimental results. Accordingly, the numerical model developed herein proves to be capable of accurately representing the LDB phenomenon.

A Computational Approach to Identifying Engineering Design Problems

Abstract Identifying new problems and providing solutions are necessary tasks for design engineers at early-stage product design and development. A new problem fosters innovative and inventive solutions. Hence, it is expected that engineering design pedagogy and practice should equally focus on engineering design problem-exploring (EDPE)—a process of identifying or coming up with a new problem or need at the early-stage of design, and engineering design problem-solving (EDPS)—a process of developing engineering design solutions to a given problem. However, studies suggest that EDPE is scarcely practiced or given attention to in academia and industry, unlike EDPS. The aim of this paper is to investigate the EDPE process for any information relating to its scarce practice in academia and industry. This is to explore how emerging technologies could support the process. Natural models and phenomena that explain the EDPE process are investigated, including the “rational” and “garbage can” models, and associated challenges identified. A computational framework that mimics the natural EDPE process is presented. The framework is based on Markovian model and computational technologies, including machine learning. A case study is conducted with a sample size of 43 participants drawn worldwide from the engineering design community in academia and industry. The case study result shows that the first-of-its-kind computational EDPE framework presented in this paper supports both novice and experienced design engineers in EDPE.

Mission Engineering and Design using Real-Time Strategy Games: An Explainable-AI Approach

Abstract In this paper, we adapt computational design approaches, widely used by the engineering design community, to address the unique challenges associated with mission design using RTS games. Specifically, we present a modeling approach that combines experimental design techniques, meta-modeling using convolutional neural networks (CNNs), uncertainty quantification, and explainable AI (XAI). We illustrate the approach using an open-source real-time strategy (RTS) game called microRTS. The modeling approach consists of microRTS player agents (bots), design of experiments that arranges games between identical agents with asymmetric initial conditions, and an AI infused layer comprising CNNs, XAI, and uncertainty analysis through Monte Carlo Dropout Network analysis that allows analysis of game balance. A sample balanced game and corresponding predictions and SHapley Additive exPlanations (SHAP) are presented in this study. Three additional perturbations were introduced to this balanced gameplay and the observations about important features of the game using SHAP are presented. Our results show that this analysis can successfully predict probability of win for self-play microRTS games, as well as capture uncertainty in predictions that can be used to guide additional data collection to improve the model, or refine the game balance measure.

DATA-DRIVEN CREATIVITY: COMPUTATIONAL PROBLEM-EXPLORING IN ENGINEERING DESIGN

AbstractCreativity is required in engineering design. It is required in the aspects of problem-solving - conceptualizing a new solution to a problem, and problem-exploring - conceptualizing a new problem. Studies show that, in both aspects, creativity is a difficult task in practice. The aim of this study is to support the engineering design community by easing the difficulty in the problem-exploring practice. To achieve this, a computational problem-exploring (CPE) model is developed to mimic how design engineers identify a valid design problem. Consequently, a CPE tool - Pro-Explora V1 is developed based on the CPE model. The CPE model consists of a synergy of emergent computational technologies including data retrieval and machine learning. A Markovian model is employed in the CPE model to enable a data-driven random process for exploring design problems. In pilot test, Pro-Explora V1 generated some engineering design-related problems which are meaningful, unique, and could not be distinguished from naturally generated ones. It provides support to design engineers in problem-exploring at the early stage in engineering design. This study contributes to the global effort towards data-driven processes in the fourth industrial revolution.

Announcing a New Webinar Series for the Engineering Design Community

Abstract I am pleased to announce the launch of the new JMD Webinar Series, an initiative taken by the editorial board of the Journal of Mechanical Design (JMD), serving the engineering design research community. Our goal of creating this webinar series is to share the latest research and development being published in JMD and offer a platform that allows networking among the engineering design research community. All presentation videos can be accessed from the JMD companion website https://www.asmejmd.org/webinar.html after the event.

Designing P4 healthcare interventions for managing cognitive decline and dementia: where are we at?

ABSTRACT This paper presents a systematic literature review aimed at assessing how well current technology-based interventions that focus on dementia and other cognitive impairments align with the principles of the P4 vision for healthcare: Predictive, Preventive, Personalised and Participative. A search of the SCOPUS database yielded 887 articles, of which 48 were ultimately selected for analysis. Looking at whether and how each intervention implements each ‘P’-principle, our results suggest a partial and non-systemic embrace of the P4 vision. Reasoning on possible explanations for this state-of-the-art, we propose that our findings represent an opportunity for the engineering design community to engage with P4-based healthcare delivery models through the development of design frameworks, new indicators for assessing the success of such healthcare delivery models, as well as tools and methods.

Perspectives on Innovation: The Role of Engineering Design

AbstractThe aim of the paper is to foster a discussion in the engineering design community about its understanding of the innovation phenomena and the unique contribution that comes from engineering design. The paper reports on the dialouge originating from a series of workshops with participants from different backgrounds in engineering design, systems engineering, industrial design psychology and business.Definitions of innovation are revisited as used in business, management and engineering design contexts. The role of innovation is then discussed related to product development from (i) the management perspective, (ii) a systems architecture perspective and (iii) in relation to sustainable development as one driver of innovation.It is argued that engineering design has a central role in how to realise the novelty aspect of innovation and often plays a critical role in maturing these into the valuable products, and there is a need to articulate the role of engineering design in innovation to better resonate with the business and management research.

Industry Trends to 2040

AbstractThe engineering design community needs to development tools and methods now to support emerging technological and societal trends. While many forecasts exist for technological and societal changes, this paper reports on the findings of a workshop, which addressed trends in engineering design to 2040. The paper summarises the key findings from the six themes of the workshop: societal trends, ways of working, lifelong learning, technology, modelling and simulation and digitisation; and points to the challenge of understanding how these trends affect each other

A state–behavior–function model for functional modeling of multi-state systems

Since functional modeling can be very useful in conceptual design, it has received considerable attention from engineering design community. However, existing functional models still cannot effectively assist designers in analyzing the functionalities of multi-state systems during conceptual design. As a result, designers often have to manually carry out functionality analysis according to their experiences, which are not only tedious, but also error-prone. Therefore, this paper proposes a state–behavior–function model for functional modeling of multi-state systems, which can provide designers with automated functional analysis support. The approach involves not only a state–behavior–function model for representing components, but also a model for representing the functionalities of multi-state systems. A prototype software is then developed for demonstrating the proposed model, with the functional modeling of a peeler centrifuge as an example to illustrate the proposed functional modeling approach.

Enabling Resilience of Complex Engineered Systems Using Control Theory

Successful recovery from a disrupted state to maintain optimal performance is a key feature that a resilient complex engineered system should have. In the engineering design community, the current focus of engineering resilience research is primarily directed toward improving overall system performance in the presence of likelihood failures. Little attention has been given to the study of how the system responds during and/or after the occurrence of a failure event. This paper proposes the use of control theory as a strategy to enable resilient behavior in complex engineered systems. Control theory has various benefits in its application to a resilient engineered system, with the main advantage being its ability to regulate and govern system states, even while the failure is taking place. In the context of implementation within a complex engineered system, such a controller should be designed such that, when a disturbance occurs, the controller should simultaneously be able to take timely action to correct the shift in system performance. To date, the fusion of control theory with engineering resilience has not been explored in-depth by the engineering design community. This paper, thus, presents a resilience modeling and analysis approach using fundamental control theory. The resilience of a power distribution system is employed as a case study to demonstrate the effectiveness of the proposed approach. The presented study also expects to aid in the concurrent development of resilience functions in complex engineered systems under uncertainty.

Function modeling using the system state flow diagram

AbstractThis paper introduces a rigorous framework for function modeling of complex multidisciplinary systems based on the system state flow diagram (SSFD). The work addresses the need for a consistent methodology to support solution-neutral function-based system decomposition analysis, facilitating the design, modeling, and analysis of complex systems architectures. A rigorous basis for the SSFD is established by defining conventions for states and function definitions and a representation scheme, underpinned by a critical review of existing literature. A set of heuristics are introduced to support the function decomposition analysis and to facilitate the deployment of the methodology with strong practitioner guidelines. The SSFD heuristics extend the existing framework of Otto and Wood (2001) by introducing a conditional fork node heuristic, to facilitate analysis and aggregation of function models across multiple modes of operation of the system. The empirical validation of the SSFD function modeling framework is discussed in relation to its application to two case studies: a benchmark problem (glue gun) set for the engineering design community; and an industrial case study of an electric vehicle powertrain. Based on the evidence from the two case studies presented in the paper, a critical evaluation of the SSFD function modeling methodology is discussed based on the function benchmarking framework established by Summers et al. (2013), considering the representation, modeling, cognitive, and reasoning characteristics. The significance of this paper is that it establishes a rigorous reference framework for the SSFD function representation and a consistent methodology to guide the practitioner with its deployment, facilitating its impact to industrial practice.