Cold rolling involves large deformation of the workpiece leading to temperature increase due to plastic deformation. This process is highly nonlinear and leads to large computation times to fully model the process. This paper describes the use of dimension-reduced neural networks (DR-NNs) for predicting temperature changes due to plastic deformation in a two-stage cold rolling process. The main objective of these models is to reduce computational demand, error, and uncertainty in predictions. Material properties, feed velocity, sheet dimensions, and friction models are introduced as inputs for the dimensionality reduction. Different linear and nonlinear dimensionality reduction methods reduce the input space to a smaller set of principal components. The principal components are fed as inputs to the neural networks for predicting the output temperature change. The DR-NNs are compared against a standalone neural network and show improvements in terms of lower computational time and prediction uncertainty.

A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords derived from these research questions led to 975 records initially retrieved from 7 scientific search engines. Finally, 86 articles were selected for inclusion in the review. As the primary research finding, we identified 15 ML-based requirement elicitation tasks and classified them into four categories. Twelve different data sources for building a data-driven model are identified and classified in this literature review. In addition, we categorized the techniques for constructing ML-based requirement elicitation methods into five parts, which are Data Cleansing and Preprocessing, Textual Feature Extraction, Learning, Evaluation, and Tools. More specifically, 3 categories of preprocessing methods, 3 different feature extraction strategies, 12 different families of learning methods, 2 different evaluation strategies, and various off-the-shelf publicly available tools were identified. Furthermore, we discussed the limitations of the current studies and proposed eight potential directions for future research.

The goal of this paper is to develop and test a gamified design thinking framework, including its pedagogical elements, for supporting various learning objectives for school students. By synthesizing the elements and principles of design, learning and games, the authors propose a framework for a learning tool for school students to fulfil a number of learning objectives; the framework includes a design thinking process called “IISC Design Thinking” and its gamified version called “IISC DBox”. The effectiveness of the framework as a learning tool has been evaluated by conducting workshops that involved 77 school students. The results suggest that the gamification used had a positive effect on the design outcomes, fulfilment of learning objectives, and learners' achievements, indicating the potential of the framework for offering an effective, gamified tool for promoting design thinking in school education. In addition to presenting results from empirical studies for fulfilment of the objectives, this paper also proposes an approach that can be used for identifying appropriate learning objectives, selecting appropriate game elements to fulfil these objectives, and integrating appropriate game elements with design and learning elements. The paper also proposes a general approach for assessing the effectiveness of a gamified version for attaining a given set of learning objectives. The methodology used in this paper thus can be used as a reference for developing and evaluating a gamified version of design thinking course suitable not only for school education but also for other domains (e.g., engineering, management) with minimal changes.

Inspirational stimuli are known to be effective in supporting ideation during early-stage design. However, prior work has predominantly constrained designers to using text-only queries when searching for stimuli, which is not consistent with real-world design behavior where fluidity across modalities (e.g., visual, semantic, etc.) is standard practice. In the current work, we introduce a multi-modal search platform that retrieves inspirational stimuli in the form of 3D-model parts using text, appearance, and function-based search inputs. Computational methods leveraging a deep-learning approach are presented for designing and supporting this platform, which relies on deep-neural networks trained on a large dataset of 3D-model parts. This work further presents the results of a cognitive study (n = 21) where the aforementioned search platform was used to find parts to inspire solutions to a design challenge. Participants engaged with three different search modalities: by keywords, 3D parts, and user-assembled 3D parts in their workspace. When searching by parts that are selected or in their workspace, participants had additional control over the similarity of appearance and function of results relative to the input. The results of this study demonstrate that the modality used impacts search behavior, such as in search frequency, how retrieved search results are engaged with, and how broadly the search space is covered. Specific results link interactions with the interface to search strategies participants may have used during the task. Findings suggest that when searching for inspirational stimuli, desired results can be achieved both by direct search inputs (e.g., by keyword) as well as by more randomly discovered examples, where a specific goal was not defined. Both search processes are found to be important to enable when designing search platforms for inspirational stimuli retrieval.

Data-enabled design (DED) is a promising new methodology for designing with users from within their own context in an iterative and hands-on fashion. However, the agile and flexible qualities of the methodology do not directly translate to every context. In this article, we reflect on the design process of an intelligent ecosystem, called ORBIT, and a proposed evaluative study planned with it. This was part of a DED project in collaboration with a medical hospital to study the post-operative behavior in the (remote) context of bariatric patients. The design and preparation of this project and the process towards an eventual study rejection from the medical ethical committee (METC) provide rich insights into (1) what it means to conduct DED research in a clinical context, and (2) where the boundaries of the method might lie in this specific application area. We highlight insights from carefully designing the substantial infrastructure for the study, and how different aspects of DED translated less easily to the clinical context. We analyze the proposed study setup through the lenses of several modifications we made to DED and further reflect on how to expand and scale up the methodology and adapt the process for the clinical context.

Machlup used the words alpha, beta, and gamma to identify humanities, science, and social science as three distinct fields of academic learning and knowing, in addition to general knowledge. Gilles and Paquet identified a fourth type of disciplinary knowledge and labeled it as delta. This includes the knowledge of creative disciplines such as design, law, and economy. Since the time of these road-paving works, a lot has changed. In the last two decades, various concepts and manifestations of intellectualized engineered systems have appeared. A paradigmatic feature of these systems, exemplified by smart cyber-physical systems, is that they collect, infer, or extract massive amount of synthetic system knowledge (M-SSK) based on some pre-programmed human knowledge. The amount of this type of knowledge grows continuously. It can be aggregated on system level and on system of systems level. This paper argues that this aggregated M-SSK is not covered by the abovementioned four genres of knowledge. In fact, it represents a new genre. The conducted literature study underpins this claim. Therefore, the paper suggests dealing with it as a new genre, called epsilon-knowledge. Artificial intelligence, system engineering, cyber-physical systems, and knowledge engineering are the disciplines dealing with epsilon-knowledge. The paper refers to sympérasmology as the proper conceptual framework of studying this genre of knowledge.

This research envisages an automated system to inform engineers when opportunities occur to use existing features or configurations during the development of new products. Such a system could be termed a "predictive CAD system" because it would be able to suggest feature choices that follow patterns established in existing products. The predictive CAD literature largely focuses on predicting components for assemblies using 3D solid models. In contrast, this research work focuses on feature-based predictive CAD system using B-rep models. This paper investigates the performance of predictive models that could enable the creation of such an intelligent CAD system by assessing three different methods to support inference: sequential, machine learning, or probabilistic methods using N-Grams, Neural Networks (NNs), and Bayesian Networks (BNs) as representative of these methods. After defining the functional properties that characterize a predictive design system, a generic development methodology is presented. The methodology is used to carry out a systematic assessment of the relative performance of three methods each used to predict the diameter value of the next hole and boss feature type being added during the design of a hydraulic valve body. Evaluating predictive performance providing five recommendations ($k = 5$) for hole or boss features as a new design was developed, recall@k increased from around 30% to 50% and precision@k from around 50% to 70% as one to three features were added. The results indicate that the BN and NN models perform better than those using N-Grams. The practical impact of this contribution is assessed using a prototype (implemented as an extension to a commercial CAD system) by engineers whose comments defined an agenda for ongoing research in this area.

The successful planning of future product generations requires reliable insights into the actual products’ problems and potentials for improvement. A valuable source for these insights is the product use phase. In practice, product planners are often forced to work with assumptions and speculations as insights from the use phase are insufficiently identified and documented. A new opportunity to address this problem arises from the ongoing digitalization that enables products to generate and collect data during their utilization. Analyzing these data could enable their manufacturers to generate and exploit insights concerning product performance and user behavior, revealing problems and potentials for improvement. However, research on analyzing use phase data in product planning of manufacturing companies is scarce. Therefore, we conducted an exploratory interview study with decision-makers of eight manufacturing companies. The result of this paper is a detailed description of the potentials and challenges that the interviewees associated with analyzing use phase data in product planning. The potentials explain the intended purpose and generic application examples. The challenges concern the products, the data, the customers, the implementation, and the employees. By gathering the potentials and challenges through expert interviews, our study structures the topic from the perspective of the potential users and shows the needs for future research.

Shape queries based on shape embedding under a given Euclidean, affine, or linear transformation are absent from current CAD systems. The only systems that have attempted to implement shape embedding are the shape grammar interpreters albeit with promising but inconclusive results. The work here identifies all possible 14 cases of shape embedding with respect to the number of available registration points, four for determinate cases and ten for indeterminate ones, and an approach is sketched to take on the complexities underlying the indeterminate cases. All visual calculations are done with shapes consisting of straight lines in the Euclidean plane within the algebra Uij for i = 1 the dimension of lines and j = 2 the dimension of space in which the lines are defined, transformed and combined. Aspects of interface design and integration to current work design workflows are deliberately left aside.

The study of interpretation is of major importance for our understanding of design cognition. When interacting with design representations, designers often rely on metaphorical descriptions as interpretive devices, which aid in coping with the task at hand. Consequently, such descriptions can enlighten us regarding the designer's perspective of the situation, and their analysis can deepen our knowledge of design cognition. We observe designers as they metaphorically interpret design representations during a simple task of spatial configuration, and introduce an approach for modeling this practice, as a means for getting insights into the designer's mental world. In this, we draw on traditional practices of protocol analysis, as well as on state-of-the-art theoretical frameworks for situated design and discourse analysis. Our integrated approach demonstrates how important relations between external and internal reality in design activity can be mapped and visualized. This sheds some light on the cognitive process of interpretation in design. The proposed method can both serve as a basis for detailed analyses of design cognition and for the enhancement of current models for situated design agents.

The capture and analysis of diverse data is widely recognized as being vital to the design of new products and services across the digital economy. We focus on its use to inspire the co-design of visitor experiences in museums as a distinctive case that reveals opportunities and challenges for the use of personal data. We present a portfolio of data-inspired visiting experiences that emerged from a 3-year Research Through Design process. These include the overlay of virtual models on physical exhibits, a smartphone app for creating personalized tours as gifts, visualizations of emotional responses to exhibits, and the data-driven use of ideation cards. We reflect across our portfolio to articulate the diverse ways in which data can inspire design through the use of ambiguity, visualization, and inter-personalization; how data inspire co-design through the process of co-ideation, co-creation, and co-interpretation; and how its use must negotiate the challenges of privacy, ownership, and transparency. By adopting a human perspective on data, we are able to chart out the complex and rich information that can inform design activities and contribute to datasets that can drive creativity support systems.

Shapes are perceived unanalyzed, without rigid representation of their parts. They do not comply with standard symbolic knowledge representation criteria; they are treated and judged by appearance. Resolving the relationship of parts to parts and parts to wholes has a constructive role in perception and design. This paper presents a computational account of part–whole figuration in design. To this end, shape rules are used to show how a shape is seen, and shape decompositions having structures of topologies and Boolean algebras reveal alternative structures for parts. Four examples of shape computation are presented. Topologies demonstrate the relationships of wholes, parts, and subparts, in the computations enabling the comparison and relativization of structures, and lattice diagrams are used to present their order. Retrospectively, the topologies help to recall the generative history and establish computational continuity. When the parts are modified to recognize emergent squares locally, other emergent shapes are highlighted globally as the topology is re-adjusted. Two types of emergence are identified: local and global. Seeing the local parts modifies how we analyze the global whole, and thus, a local observation yields a global order.

With the emergence of Internet of Things (IoT) as a new source of “big” data and value creation, businesses encounter novel opportunities as well as challenges in IoT design. Although recent research argues that digital technology can enable new kinds of development processes that are distinctive from their counterparts in the 20th century, minimal attention has been focused on the IoT design process. In order to contextualize New Product Development (NPD) processes for IoT, this paper comprehensively interrogates existing, and emerging development approaches for products, services, software, and integrated products, and several factors that affect designing IoT. This discussion includes the generic development process, the commonalities and differences of different development approaches, and processes. The paper demonstrates that only a few existing approaches reflect vital characteristics of networked artifacts or the integration of data science within the development model, which is one of the key attributes of IoT design. From these investigations, we propose “The Mobius Strip Model of IoT Development ProcessI,” a conceptual process for IoT design, which is distinctive to others. The continuous loops of the IoT design integrate the attributes and phases of different processes and consist of two different development approaches and strategies. Understanding the particular attributes of the IoT NPD process can help novice and experienced researchers in both feeding and drawing insight from the broader design discourse.

Fault propagation analysis is a process used to determine the consequences of faults residing in a computer system. A typical computer system consists of diverse components (e.g., electronic and software components), thus, the faults contained in these components tend to possess diverse characteristics. How to describe and model such diverse faults, and further determine fault propagation through different components are challenging problems to be addressed in the fault propagation analysis. This paper proposes an ontology-based approach, which is an integrated method allowing for the generation, injection, and propagation through inference of diverse faults at an early stage of the design of a computer system. The results generated by the proposed framework can verify system robustness and identify safety and reliability risks with limited design level information. In this paper, we propose an ontological framework and its application to analyze an example safety-critical computer system. The analysis result shows that the proposed framework is capable of inferring fault propagation paths through software and hardware components and is effective in predicting the impact of faults.

This paper presents a preliminary study into the spatial features that can be used to distinguish creativity andefficiency in design layouts, and the distinct pattern of cognitive and metacognitive activity that is associated with creative design. In a design experiment, a group of 12 architects were handed a design brief. Their drawing activity was recorded and they were required to externalize their thoughts during the design process. Both design solutions and verbal comments were analysed and modelled. A separate group of experienced architects used their expert knowledge to assign creativity and efficiency scores to the 12 design solutions. The design solutions were evaluated spatially. Protocol analysis studies including linkography and macroscopic analysis were used to discern distinctive patterns in the cognitive and metacognition activity of designs marked with the highest and least creativity scores. Entropy models of the linkographs and knowledge graphs were further introduced Finally, we assessed how creativity and efficiency correlates to experiment variables, cognitive activity, metacognitive activity, spatial and functional distribution of spaces in the design solutions, and the number and type of design constraints applied through the course of design. Through this investigation, we suggest that expert knowledge can be used to assess creativity and efficiency in designs. Our findings indicate that efficient layouts have distinct spatial features, and that cognitive and metacognitive activity in design that yields a highly creative outcome corresponds to higher frequencies of design moves and higher linkages between design moves.

This paper discusses the development of an experimental software prototype that uses surrogate models for predicting the monthly energy consumption of urban-scale community design scenarios in real time. The surrogate models were prepared by training artificial neural networks on datasets of urban form and monthly energy consumption values of all zip codes in San Diego county. The surrogate models were then used as the simulation engine of a generative urban design tool, which generates hypothetical communities in San Diego following the county's existing urban typologies and then estimates the monthly energy consumption value of each generated design option. This paper and developed software prototype is part of a larger research project that evaluates the energy performance of community microgrids via their urban spatial configurations. This prototype takes the first step in introducing a new set of tools for architects and urban designers with the goal of engaging them in the development process of community microgrids.

Literature suggests that people typically understand knowledge by induction and produce knowledge by synthesis. This paper revisits the various modes of reasoning – explanatory abduction, innovative abduction, deduction, and induction – that have been proposed by earlier researchers as crucial modes of reasoning underlying the design process. First, our paper expands earlier work on abductive reasoning – an essential mode of reasoning involved in the process of synthesis – by understanding its role with the help of the “SAPPhIRE” model of causality. The explanations of abductive reasoning in design using the SAPPhIRE model have been compared with those using existing models. Second, the paper captures and analyzes various modes of reasoning during design synthesis with the help of the “Extended Integrated Model of Designing”. The analysis of participants' verbal speech and outcomes shows the model's ability to explain the various modes of reasoning that occur in design. The results indicate the above models to provide a more extensive account of reasoning in design synthesis. Earlier empirical validation of both the models lends further support to the claim of their explanatory capacity.

Machine learning components such as deep neural networks are used extensively in cyber-physical systems (CPS). However, such components may introduce new types of hazards that can have disastrous consequences and need to be addressed for engineering trustworthy systems. Although deep neural networks offer advanced capabilities, they must be complemented by engineering methods and practices that allow effective integration in CPS. In this paper, we proposed an approach for assurance monitoring of learning-enabled CPS based on the conformal prediction framework. In order to allow real-time assurance monitoring, the approach employs distance learning to transform high-dimensional inputs into lower size embedding representations. By leveraging conformal prediction, the approach provides well-calibrated confidence and ensures a bounded small error rate while limiting the number of inputs for which an accurate prediction cannot be made. We demonstrate the approach using three datasets of mobile robot following a wall, speaker recognition, and traffic sign recognition. The experimental results demonstrate that the error rates are well-calibrated while the number of alarms is very small. Furthermore, the method is computationally efficient and allows real-time assurance monitoring of CPS.

Though they can be traced back to different roots, both smart design and smart systems have to do with the recent developments of artificial intelligence. There are two major questions related to them: (i) What way are smart design and smart systems enabled by artificial narrow, general, or super intelligence? and (ii) How can smart design be used in the realization of smart systems? and How can smart systems contribute to smart designing? A difficulty is that there are no exact definitions for these novel concepts in the literature. The endeavor to analyze the current situation and to answer the above questions stimulated an exploratory research whose first findings are summarized in this paper. Its first part elaborates on a plausible interpretation of the concept of smartness and provides an overview of the characteristics of smart design as a creative problem solving methodology supported by artificial intelligence. The second part exposes the paradigmatic features and system engineering issues of smart systems, which are equipped with application-specific synthetic system knowledge and reasoning mechanisms. The third part presents and elaborates on a conceptual model of AI-based couplings of smart design and smart systems. The couplings may manifest in various concrete forms in real life that are referred to as “connectors” in this paper. The principal types of connectors are exemplified and discussed. It has been found that smart design tends to manifest as a methodology of blue-printing smart systems and that smart systems will be intellectualized the enablers of implementation of smart design. Understanding the affordances of and creating proper connectors between smart design and smart systems need further explorative research.

Aiming to help researchers capture output from the early stages of engineering design projects, this article presents a new research tool for digitally capturing physical prototypes. The motivation for this work is to collect observations that can aid in understanding prototyping in the early stages of engineering design projects, and this article investigates if and how digital capture of physical prototypes can be used for this purpose. Early-stage prototypes are usually rough and of low fidelity and are thus often discarded or substantially modified through the projects. Hence, retrospective access to prototypes is a challenge when trying to gather accurate empirical data. To capture the prototypes developed through the early stages of a project, a new research tool has been developed for capturing prototypes through multi-view images, along with metadata describing by whom, why, when, and where the prototypes were captured. Over the course of 17 months, this research tool has been used to capture more than 800 physical prototypes from 76 individual users across many projects. In this article, one project is shown in detail to demonstrate how this capturing system can gather empirical data for enriching engineering design project cases that focus on prototyping for concept generation. The authors also analyze the metadata provided by the system to give understanding into prototyping patterns in the projects. Lastly, through enabling digital capture of large quantities of data, the research tool presents the foundations for training artificial intelligence-based predictors and classifiers that can be used for analysis in engineering design research.