Style is an ordering principle by which to structure artifacts in a design domain. The application of a visual order entails some explicit grouping property that is both cognitively plausible and contextually dependent. Central to cognitive–contextual notions are the type of representation used in analysis and the flexibility to allow semantic interpretation. We present a model of visual style based on the concept of similarity as a qualitative context-dependent categorization. The two core components of the model are semantic feature extraction and self-organizing maps (SOMs). The model proposes a method of categorizing two-dimensional unannotated design diagrams using both low-level geometric and high-level semantic features that are automatically derived from the pictorial content of the design. The operation of the initial model, called Q-SOM, is then extended to include relevance feedback (Q-SOM:RF). The extended model can be seen as a series of sequential processing stages, in which qualitative encoding and feature extraction are followed by iterative recategorization. Categorization is achieved using an unsupervised SOM, and contextual dependencies are integrated via cluster relevance determined by the observer's feedback. The following stages are presented: initial per feature detection and extraction, selection of feature sets corresponding to different spatial ontologies, unsupervised categorization of design diagrams based on appropriate feature subsets, and integration of design context via relevance feedback. From our experiments we compare different outcomes from consecutive stages of the model. The results show that the model provides a cognitively plausible and context-dependent method for characterizing visual style in design.

Functional modeling is an effective method of depicting products in the design process. Using this approach, product architecture, concept generation, and physical modeling all contribute to the design process to generate a result full of quality and functionality. The functional basis approach provides taxonomy of uniform vocabulary to produce function structures with consistent functions (verbs) and flows (nouns). Material and energy flows dominate function structures in the mechanical engineering domain with only a small percentage including signal flows. Research suggests that the signal flow gap is due to the requirement of “carrier” flows of either material or energy to transport the signals between functions. This research suggests that incorporating controls engineering methodologies may increase the number of signal flows in function structures. We show correlations between the functional modeling and controls engineering in four facets: schematic similarities, performance matching through flows, mathematical function creation using bond graphs, and isomorphic matching of the aforementioned characteristics allows for analogical solutions. Controls systems use block diagrams to represent the sequential steps of the system. These block diagrams parallel the function structures of engineering design. Performance metrics between the two domains can be complimentary when decomposed down to nondimensional engineering units. Mathematical functions of the actions in controls systems can resemble the functional basis functions with bond graphs by identifying characteristic behavior of the functions on the flows. Isomorphic matching, using the schematic diagrams, produces analogies based upon similar functionality and target performance metrics. These four similarities bridge the mechanical and electrical domains via the controls domain. We provide concepts and contextualization for the methodology using domain-agnostic examples. We conclude with suggestion of pathways forward for this preliminary research.

The ability to understand the implications of the geometry of solid objects is an important aspect of intelligent behavior. This paper presents work designed to enable reasoning with relatively loose conceptualizations of geometry. The method operates by comparing target geometry to known geometry, and involves the manipulation of models based upon prototypes. In particular, three techniques of simplification, approximation, and transformation are discussed. Finally, an application of the method to the domain of stress concentration prediction is presented.

Ontologies are an emerging means of knowledge representation to improve information organization and management, and they are becoming more prevalent in the domain of engineering design. The task of creating new ontologies manually is not only tedious and cumbersome but also time consuming and expensive. Research aimed at addressing these problems in creating ontologies has investigated methods of automating ontology reuse mainly by extracting smaller application ontologies from larger, more general purpose ontologies. Motivated by the wide variety of existing learning algorithms, this paper describes a new approach focused on the reuse of domain-specific ontologies. The approach integrates existing software tools for natural language processing with new algorithms for pruning concepts not relevant to the new domain and extending the pruned ontology by adding relevant concepts. The approach is assessed experimentally by automatically adapting a design rationale ontology for the software engineering domain to a new one for the related domain of engineering design. The experiment produced an ontology that exhibits comparable quality to previous attempts to automate ontology creation as measured by standard content performance metrics such as coverage, accuracy, precision, and recall. However, further analysis of the ontology suggests that the automated approach should be augmented with recommendations presented to a domain expert who monitors the pruning and extending processes in order to improve the structure of the ontology.

This paper describes work in progress aimed at developing an interactive modeling tool that assists engineers with the task of physical modeling in finite element analysis. Physical modeling precedes the numerical simulation phase of finite element analysis and involves applying modeling idealizations to real world physical systems so that complex engineering problems are more amenable to numerical computation. In the paper, the nature of physical modeling is explored, a cognitive model of how engineers are thought to model complex problems is described and based on this model a knowledge-based modeling assistant is proposed. The AI approach taken is based on Chandrasekaran's propose-critique-modify design model adapted for the task of physical modeling. Within this framework, the AI paradigms of case-based reasoning, derivational analogy and model-based reasoning are exploited. By representing fundamental thermal modeling scenarios as cases, complex physical systems can be modeled in a piecewise fashion. Derivational analogy permits generative adaptation of retrieved cases by using model-based engineering traces thereby providing a basis for critiquing case solutions. An initial prototype is described which has been implemented for the domain of convection heat transfer analysis.

Research efforts to implement a Bayesian belief-network-based expert system to solve a real-world diagnostic problem – the diagnosis of integrated circuit (IC) testing machines – are described. The development of several models of the IC tester diagnostic problem in belief networks also is described, the implementation of one of these models using symbolic probabilistic inference (SPI) is outlined, and the difficulties and advantages encountered are discussed. It was observed that modeling with interdependencies in belief networks simplifies the knowledge engineering task for the IC tester diagnosis problem, by avoiding procedural knowledge and focusing on the diagnostic component’s interdependencies. Several general model frameworks evolved through knowledge engineering to capture diagnostic expertise that facilitated expanding and modifying the networks. However, model implementation was restricted to a small portion of the modeling, that of contact resistance failures, which were due to time limitations and inefficiencies in the prototype inference software we used. Further research is recommended to refine existing methods, in order to speed evaluation of the models created in this research. With this accomplished, a more complete diagnosis can be achieved.

This paper proposes a fuzzy neural network (FNN) based approach to construct an individual-oriented car-following system. The feature of this system is firstly to incorporate a personal risk-taking factor in addition to other mechanical factors as the input parameters. Through the learning capability of artificial network, the complex membership functions between the input factors and the output (i.e., the appropriate car-following headway) can be efficiently established, and then the fuzzy logic rules can be properly constructed. The performance of the FNN system is finally assessed against the field data. The results are inspiring that the system is proven capable of providing highly accurate predictions of the required car-following headways from person to person at various speeds. The success of this study provides some clues of utilizing FNN techniques in exploring some individual-oriented machines.

Automatic generation of high-quality meshes is a base of CAD/CAE systems. The element extraction is a major mesh generation method for its capabilities to generate high-quality meshes around the domain boundary and to control local mesh densities. However, its widespread applications have been inhibited by the difficulties in generating satisfactory meshes in the interior of a domain or even in generating a complete mesh. The element extraction method's primary challenge is to define element extraction rules for achieving high-quality meshes in both the boundary and the interior of a geometric domain with complex shapes. This paper presents a self-learning element extraction system, FreeMesh-S, that can automatically acquire robust and high-quality element extraction rules. Two central components enable the FreeMesh-S: (1) three primitive structures of element extraction rules, which are constructed according to boundary patterns of any geometric boundary shapes; (2) a novel self-learning schema, which is used to automatically define and refine the relationships between the parameters included in the element extraction rules, by combining an Advantage Actor-Critic (A2C) reinforcement learning network and a Feedforward Neural Network (FNN). The A2C network learns the mesh generation process through random mesh element extraction actions using element quality as a reward signal and produces high-quality elements over time. The FNN takes the mesh generated from the A2C as samples to train itself for the fast generation of high-quality elements. FreeMesh-S is demonstrated by its application to two-dimensional quad mesh generation. The meshing performance of FreeMesh-S is compared with three existing popular approaches on ten pre-defined domain boundaries. The experimental results show that even with much less domain knowledge required to develop the algorithm, FreeMesh-S outperforms those three approaches in essential indices. FreeMesh-S significantly reduces the time and expertise needed to create high-quality mesh generation algorithms.

The user's gaze can provide important information for human–machine interaction, but the analysis of manual gaze data is extremely time-consuming, inhibiting wide adoption in usability studies. Existing methods for automated areas of interest (AOI) analysis cannot be applied to tangible products with a screen-based user interface (UI), which have become ubiquitous in everyday life. The objective of this paper is to present and evaluate a method to automatically map the user's gaze to dynamic AOIs on tangible screen-based UIs based on computer vision and deep learning. This paper presents an algorithm for automated Dynamic AOI Mapping (aDAM), which allows the automated mapping of gaze data recorded with mobile eye tracking to the predefined AOIs on tangible screen-based UIs. The evaluation of the algorithm is performed using two medical devices, which represent two extreme examples of tangible screen-based UIs. The different elements of aDAM are examined for accuracy and robustness, as well as the time saved compared to manual mapping. The break-even point for an analyst's effort for aDAM compared to manual analysis is found to be 8.9 min gaze data time. The accuracy and robustness of both the automated gaze mapping and the screen matching indicate that aDAM can be applied to a wide range of products. aDAM allows, for the first time, automated AOI analysis of tangible screen-based UIs with AOIs that dynamically change over time. The algorithm requires some additional initial input for the setup and training, but analyzed gaze data duration and effort is only determined by computation time and does not require any additional manual work thereafter. The efficiency of the approach has the potential for a broader adoption of mobile eye tracking in usability testing for the development of new products and may contribute to a more data-driven usability engineering process in the future.

An academic makerspace, home to tools and people dedicated to facilitating and inspiring a making culture, is characterized by openness, creativity, learning, design, and community. This nontraditional learning environment has found an immense increase in popularity and investment in the last decade. Further, makerspaces have been shown to be highly gendered, privileging men's and masculine understandings of making. The spike in popularity warrants deeper analysis, examining the value of these spaces for women and if learning is occurring in these spaces, specifically at higher education institutions. We implemented a phenomenologically based interviewing process to capture the making experiences of 20 women students, recruited through purposive and snowball sampling. By eliciting the narratives of women students, we captured how making, designing, and creating evolved through gendered experiences in the university makerspace. Each interview was transcribed and resulted in around 868 pages of single-spaced text transcriptions. The data were analyzed through multiple cycles of open and axial coding for common themes and patterns, where makerspaces create a culture of learning, facilitate students’ design journey, and form a laboratory for creativity. These themes forwarded the creation of a learning model that showcases how design and learning interact in the makerspace. This work demonstrates that women students are engaging learning and inspiration; developing confidence and resilience; and learning how to work with others and collaborate.

We present a multimodal spatial data access framework designed to serve the informational and computational requirements of architectural design assistance systems that are intended to provide intelligent spatial decision support and analytical capabilities. The framework focuses on multiperspective semantics, qualitative and artifactual spatial abstractions, and industrial conformance and interoperability within the context of the industry foundation classes. The framework provides qualitative and cognitively adequate representational mechanisms, and the formal interpretation of the structural form of indoor spaces that are not directly provided by conventional computer-aided design based or quantitative models of space. We illustrate the manner in which these representations directly provide the spatial abstractions that are needed to enable the implementation of intelligent analytical capabilities in design assistance tools. We introduce the framework, and also provide detailed use cases that illustrate the usability of the framework and the manner of its utilization within architectural design assistance systems.

A deeper understanding of creativity and design is essential for the development of tools to improve designers’ creative processes and drive future innovation. The objective of this research is to evaluate the effect of physical activity versus movement in a virtual environment on the creative output of industrial design students. This study contributes a novel assessment of whether the use of virtual reality can produce the same creative output within designers as physical activity has been shown to produce in prior studies. Eighteen industrial design students at the Georgia Institute of Technology completed nine design tasks across three conditions in a within-subjects experimental design. In each condition, participants independently experienced one of three interventions. Solutions were scored for novelty and feasibility, and self-reported mood data was correlated with performance. No significant differences were found in novelty or feasibility of solutions across the conditions. However, there are statistically significant correlations between mood, interventions, and peak performance to be discussed. The results show that participants who experienced movement in virtual reality prior to problem solving performed at an equal or higher level than physical walking for all design tasks and all designer moods. This serves as motivation for continuing to study how VR can provide an impact on a designer's creative output. Hypothesized creative performance with each mode is discussed using trends from four categories of mood, based on the combined mood characteristics of pleasantness (positive/negative) and activation (active/passive).

A data-driven approach for multiclass fault diagnosis of drive fed induction motor (IM) using stator current at steady-state condition is a complex pattern classification problem. The applied DWT-IDWT algorithm in this work is reinforced by a novel selection criterion for mother wavelet application and justifies the originality of the work. This investigation has exploited the built-in feature selection process of Random Forest (RF) classifier to resolve the most challenging issues in this area, including bearing and stator fault detection. RF has shown an outstanding performance without application of any feature selection technique because of its distributive feature model. The robustness of the results backed by the experimental verification shows an encouraging future of RF as a classifier in the area of intelligent fault diagnosis of IM.

A novel swarm intelligence approach for combinatorial optimization is proposed, which we call probability increment based swarm optimization (PIBSO). The population evolution mechanism of PIBSO is depicted. Each state in search space has a probability to be chosen. The rule of increasing the probabilities of states is established. Incremental factor is proposed to update probability of a state, and its value is determined by the fitness of the state. It lets the states with better fitness have higher probabilities. Usual roulette wheel selection is employed to select states. Population evolution is impelled by roulette wheel selection and state probability updating. The most distinctive feature of PIBSO is because roulette wheel selection and probability updating produce a trade-off between global and local search; when PIBSO is applied to solve the printed circuit board assembly optimization problem (PCBAOP), it performs superiorly over existing genetic algorithm and adaptive particle swarm optimization on length of tour and CPU running time, respectively. The reason for having such advantages is analyzed in detail. The success of PCBAOP application verifies the effectiveness and efficiency of PIBSO and shows that it is a good method for combinatorial optimization in engineering.

Constraint-based reasoning is often used to represent and find solutions to configuration problems. In the field of constraint satisfaction, the major focus has been on finding solutions to difficult problems. However, many real-life configuration problems, although not extremely complicated, have a huge number of solutions, few of which are acceptable from a practical standpoint. In this paper we present a value ordering heuristic for constraint solving that attempts to guide search toward solutions that are acceptable. More specifically, by considering weights that are assigned to values and sets of values, the heuristic can guide search toward solutions for which the total weight is within an acceptable interval. Experiments with random constraint satisfaction problems demonstrate that, when a problem has numerous solutions, the heuristic makes search extremely efficient even when there are relatively few solutions that fall within the interval of acceptable weights. In these cases, an algorithm that is very effective for finding a feasible solution to a given constraint satisfaction problem (the “maintained arc consistency” algorithm or MAC) does not find a solution in the same weight interval within a reasonable time when it is run without the heuristic.

In computer-aided architectural design, words are an underemployed source of information. Through a series of case studies, we deduced a design annotation data model. All entities in this model can be captured from the design draft, except one: the word relation. Therefore, a system was developed that generates word graphs using single words from the draft as input. The system searches for semantic relations between words and for new intermediate words that can connect two existing words. The system has filters that select only those graphs that are considered interesting by the designers. The envisioned applications of word graphs in the context of computer-aided architectural design are to contribute to the architect's design and to enhance the fluency of the design. These expectations are met, but must be considered in relation to the architect's drafting behavior.

In preliminary design, the details of a structure are insufficient to warrant the use of numeric tools traditionally used in structural analysis. However, an accurate prediction of the behavior of a structure and its components in the preliminary design phase can have a significant effect on the final design process in reducing the number of alternative solutions, avoiding the costly design revisions, and improving the quality of design. Presently, there are few tools available for preliminary analysis of structures. This study represents an initial effort towards the development of a tool that can be used in the conceptual design stage to qualitatively evaluate the behavior of a structure.

This paper describes a prototype system, QStruc, for qualitative structural analysis, which combines first principles in structural engineering and experiential knowledge of structural behavior. The purposes of QStruc are: (1) to generate qualitative models from the schematics of a structure; and (2) to infer the qualitative response of the structure in terms of deflected shape, moments, and reactions. The qualitative analysis strategy employs: (1) a greedy depth-first approach that tries to expand the derived response as much as possible from known parameter values; (2) a causal ordering mechanism, which enables the system to identify the solution path for the qualitative analysis; (3) qualitative calculus, which enables the qualitative evaluation of the physical quantities of the causal model that describes the behavior of the structure; and (4) Quantity Lattice (Simmons, 1986) which enables the system to reason about partial ordering among physical quantities and to reduce some of the ambiguous conclusions caused by the impreciseness of the information. Examples are provided to illustrate the effectiveness and limitations of the prototype system.

The ability to successfully conduct radical innovations is mandatory for mature industrial companies that want to remain competitive in the global market. This ability relies on several ingredients, namely: (1) the structuring of the innovation process; (2) managerial principles; (3) methodological tools; (4) the presence of a culture of innovation. This paper reports about the impact of applying the User eXperience-Fuzzy Front End (UX-FFE) model, which brings together the systemic innovation process with the social, economical, and methodological aspects on the outcomes of the innovation process. Firstly, it appears that the operational performance of the upstream innovation process relies on the quality of the social context, intrinsic to the group of co-creators, corresponding to the reported perceived experience. Secondly, the UX-FFE model application, therefore, allows optimizing the upstream innovation process performance. Indeed, we argue that the evaluation of the co-creators perceived experience brings new opportunities to optimize the operational performance of the upstream innovation process. The first part of this paper presents deeper a theoretical model, named UX-FFE, which combines a UX approach with an upstream innovation process (FFE). The main interest of this UX-FFE model is that it allows evaluating the social aspect of the upstream innovation process, which may be detrimental to the success of radical innovation projects in mature companies. The second part presents the results of previous experiments that validated the model. The results allow the design of an instrument dedicated to the evaluation of the user experience of co-creators in the ideation stage. Finally, the third part reports about the experimentation of the UX-FFE in a mature company. Results present the impact of the co-creators' experience on the performance of radical innovation projects.

This paper reports a systematic literature review with the aim of determining the role of stimuli and other factors, such as timing, the designers’ background, expertise, and experience, in the idea generation phase of conceptual design related to engineering and industrial design and architecture. “Stimulus” is a general expression for a source of information characterized by several features, including the source (internal or external), analogical distance (near or far), and form (textual, visual, or other). Several recent studies have been conducted on this topic involving neurophysiological measurements with significant results. This comprehensive review will help to determine if the neurophysiological results are consistent with those from protocol studies. This allows for determining how the features of stimuli affect – among the related factors – designers’ performance in idea generation. The literature search was carried out using the Snowball and PRISMA methods. A total of 72 contributions were selected from studies adopting protocol analysis or neurophysiological measurements. This study presents a framework to support the selection of stimuli most likely to maximize performance, based on the designer's background and expertise in the different idea generation metrics. The main findings of the framework suggest that visual stimuli enhance the creative performance of designers, regardless of their background, while textual stimuli foster the variety and quality of ideas, but only in engineering and industrial designers. Comparing the findings, the resulting framework reveals aspects of stimuli that require further investigation. These can be considered valuable insights for new directions for design research.

In engineering and architecture, different approaches have been developed that share the use of graph transformation to automate design processes or to search for design solutions by means of computational design synthesis. In order to give an overview of these approaches, we provide a review of articles published in the last decade. Forty-eight articles were reviewed to determine similarities and differences of these approaches. Research fields in method development for the representation of design problems and the processing of graph transformations, as well as the application of graph transformations in engineering, architecture, and shape grammars were identified. Different approaches for the documentation of the vocabulary and the rules were examined. Finally, different approaches for rule applications were analyzed. Based on found limitations, future research directions are suggested.