Industry 4.0 represents high-level methodologies for the development of new generation manufacturing metrology systems, which are more intelligent (smart), autonomous, flexible, high-productive, and self-adaptable. One of the systems capable of responding to these challenges is a cyber-physical manufacturing metrology system (CP2MS) with techniques of artificial intelligence (AI). In general, CP2MS systems generate Big data, horizontally by integration [coordinate measuring machines (CMMs)] and vertically by control. This paper presents a cyber-physical manufacturing metrology model (CP3M) for Industry 4.0 developed by applying AI techniques such as engineering ontology (EO), ant-colony optimization (ACO), and genetic algorithms (GAs). Particularly, the CP3M presents an intelligent approach of probe configuration and setup planning for inspection of prismatic measurement parts (PMPs) on a CMM. A set of possible PMP setups and probe configurations is reduced to optimal number using developed GA-based methodology. The major novelty is the development of a new CP3M capable of responding to the requirements of an Industry 4.0 concept such as intelligent, autonomous, and productive measuring systems. As such, they respond to one smart metrology requirement within the framework of Industry 4.0, referring to the optimal number of PMPs setups and for each setup defines the configurations of probes. The main contribution of the model is productivity increase of the measuring process through the reduction of the total measurement time, as well as the elimination of errors due to the human factor through intelligent planning of probe configuration and part setup. The experiment was successfully performed using a PMP specially designed and manufactured for the purpose.

Computational simulation of physical systems generally requires human experts to set up a simulation, run it, evaluate the quality of the simulation output, and repeatedly invoke the simulator with modified input until a satisfactory output quality is achieved. This reliance on human experts makes use of simulators by other programs difficult and unreliable, though invocation of simulators by other programs is critical for important tasks such as automated engineering design optimization. Presented is a framework for constructing intelligent controllers for computational simulators that can automatically detect a wide variety of problems that lead to low-quality simulation output, using a set of evaluation methods based on knowledge of physics and numerical analysis stored in a data/knowledgebase of models and simulations. An experimental implementation of this framework in an intelligent automated controller for a widely used computational fluid dynamics simulator is described.

A methodology for studying and understanding the process of design, and ultimately for developing a computational theory of design is presented. In particular, the role of formalization in such an investigation is set out. This is done by first presenting the background to and development of computational search as a widely adopted problem solving paradigm in artificial intelligence research. It is then suggested why computational search provides an inadequate characterization of the design process and an alternative, that design is an exploration process is proposed. By developing certain ideas first put forward by Simon the authors seek to explain why this view is taken and how it forms a central part of their Artificial Intelligence in Design research programme. It is hoped to (eventually) develop a computational theory of design. The radically incomplete nature of this work necessarily prevents the authors from answering the question posed by the title of the paper but the title does provide a good focus for their efforts.

In inspiration and fixation experiments, example designs are often provided along with the instructions for how participants should treat them. However, research has not reached a consensus about the influence of such instructions, leading to difficulties in understanding how the examples and the instructions each affect idea generation. We conducted an experiment in which 303 participants designed for the same design problem, while given different examples and instructions, which ranged from strongly encouraging copying the examples to strongly discouraging copying. Exposure to the examples affected the number and type of ideas generated, whereas exposure to the instructions did not. However, instructions did affect how participants incorporated features of the examples in their ideas. Encouraged groups incorporated many features of the examples, while also incorporating structural features more than conceptual ones. Surprisingly, the incorporation of features in discouraged groups was not different from that of groups given no instructions or even no stimulus. This indicates that concrete features may be easier to recognize and reproduce than abstract ones, and that encouraging instructions are more effective than discouraging ones, despite how strict or lenient those instructions are. The manipulation of different features also allowed us to observe how similar approaches to solving a design problem can compete for attention and how the calculation of feature repetition can be misleading depending on how common or obvious the features might be. These findings have implications for the interpretation of results from fixation studies, and for the development of design tools that present stimuli to assist idea generation.

PANDA, the Pumper Apparatus Novice Design Assistant, is a case-based design system developed to assist firefighters who wish to design their pumper engines. In contrast to other such systems, PANDA addresses the unique needs of a novice, non-specialist who performs design in a highly specialized domain, where the design is decomposable into elements which each fulfill their own identifiable function. In PANDA we study how to create an interactive case-based design assistant that can understand the needs and desires of a non-specialist designer and can translate them into formal specifications; that can provide assistance by using case-based design methodologies; that can deal with non-functional design criteria such as aesthetics and traditional practices; and that can guide the novice designer by discussing alternatives, tradeoffs, and adaptations. Our prototypical system verifies our methodological approach to supporting novice design activities.

Virtual three-dimensional (3-D) environments have become pervasive tools in a number of professional and recreational tasks. However, interacting with these environments can be challenging for users, especially as these environments increase in complexity and scale. In this paper, we argue that the design of 3-D interaction techniques is an ill-defined problem. This claim is elucidated through the context of data-rich and geometrically complex multiscale virtual 3-D environments, where unexpected factors can encumber intellection and navigation. We develop an abstract model to guide our discussion, which illustrates the cyclic relationship of understanding and navigating; a relationship that supports the iterative refinement of a consistent mental representation of the virtual environment. Finally, we highlight strategies to support the design of interactions in multiscale virtual environments, and propose general categories of research focus.

Product configuration is a key technology, which enables businesses to deliver and deploy individualized products. In many cases, finding the optimal configuration solution for the user is a creative process that requires them to decide trade-offs between conflicting goals (multicriteria optimization problem). These problems are best supported by an interactive dialog that is managed by a dedicated software program (the configurator) that provides decision support. We illustrate this using a real example (configuration of a business software system). This productively used application makes the user aware of which choices are available in a given situation, provides assistance in resolving inconsistent choices and defaults, and generates explanations if desired. One of the key configurator components used to manage this is a truth maintenance system. We describe how this component is used and two novel extensions to it: methods for declarative handling of defaults (of varying strength) and the declarative handling of incompleteness. Finally, we summarize our experiences made during the implementation of this application and the pros and cons of declarative versus procedural approaches.

Speech- and gesture-based interfaces for computer-aided design (CAD) modeling must employ vocabulary suitable for target professional groups. We conducted an experiment with 40 participants from architecture and engineering backgrounds to elicit their speech preferences for four CAD manipulation tasks: Scale, Rotate, Copy, and Move. We compiled speech command terms used by participants and analyzed verbalizations based on three analytic themes: the exactness of descriptions, the granularity of descriptions, and the use of CAD legacy terms. We found that participants from both groups used precise and vague expressions in their verbalizations and used a median of three parameters in their verbalizations. Architects used CAD legacy terms more than Engineers in the tasks Scale and Rotate. Based on these findings, we give recommendations for the design of speech- and gesture-based interface for conceptual CAD modeling.

In the current work, the effects of design (groove depth and groove width) and operational (temperature and velocity) parameters on aerodynamic performance parameters (coefficient of drag and coefficient of lift) of an isolated passenger car tire have been investigated. The study is conducted by using neural network-based Monte-Carlo analysis on computational fluid dynamics (CFD). The computer experiments are designed to obtain the causal relationship between tire design, operational, and aerodynamic performance parameters. The Reynolds-averaged Navier–Stokes equations-based Realizable K-ε model has been employed to analyze the variations in flow patterns around an isolated tire. The design parameters are varied over wide range and full factorial design, while considering temperature and velocity is completely explored to draw conclusive results. The multi-layer perceptron type neural network with the back-propagation algorithm is trained to map any non-linearity in causal relationships. The sensitivity analysis is performed to find the relationship between control variables and performance indicators. The importance of control variable is determined by both sensitivity and significance analyses and the paired interaction analysis is performed between selected control variables to find the interactive behavior of corresponding variables. The design parameter of groove width with 6.8% and 41% reduction in drag and lift coefficient, respectively, and conventionally overlooked operational parameter of velocity with 4% and 35% impact on drag and lift coefficient, respectively, are found to be the most significant variables. The air trapped between the longitudinal grooves and the road is found to follow the beam theory. The interaction of the groove depth and width is found to be significant with respect to coefficient of lift based on the air beam concept. The interaction of groove width and velocity is found to be significant with respect to both coefficients of lifts and drag.

This paper presents the use of data clustering methods applied to the analysis results of a design-stage, functional failure reasoning tool. A system simulation using qualitative descriptions of component behaviors and a functional reasoning tool are used to identify the functional impact of a large set of potential single and multiple fault scenarios. The impact of each scenario is collected as the set of categorical function “health” states for each component-level function in the system. This data represents the space of potential system states. The clustering and statistical tools presented in this paper are used to identify patterns in this system state space. These patterns reflect the underlying emergent failure behavior of the system. Specifically, two data analysis tools are presented and compared. First, a modified k-means clustering algorithm is used with a distance metric of functional effect similarity. Second, a statistical approach known as latent class analysis is used to find an underlying probability model of potential system failure states. These tools are used to reason about how the system responds to complex fault scenarios and assists in identifying potential design changes for fault mitigation. As computational power increases, the ability to reason with large sets of data becomes as critical as the analysis methods used to collect that data. The goal of this work is to provide complex system designers with a means of using early design simulation data to identify and mitigate potential emergent failure behavior.

One of the products of engineering, besides constructed artifacts, is design documentation. To understand how design participants use documentation, designers and typical documentation users were interviewed and protocols were taken of them both creating and using design documentation. The protocols were taken from realistic projects of preliminary design for heating, ventilation, and air conditioning systems (HVAC). The studies of document creation and use revealed three important issues: (1) design participants not only look up design facts; they frequently access documents to obtain information about the rationale for design decisions; (2) the design rationale that they see often is missing from the documents; and (3) design requirements change frequently over a project life cycle so that design documents are often inconsistent and out of date. Recognizing these documentation issues in design practice, a new approach was developed in which documents are no longer static records, but rather interactive design models supporting a case. The feasibility of the approach was demonstrated by constructing a running system and testing it with designers on realistic problems. The costs and benefits of creating and using documentation of design rationale also were analyzed. In particular, the active documents approach was evaluated for a routine, preliminary design in domains where community practice is widely shared and largely standardized. The approach depends on the feasibility of creating a parametric design model for the design domain.

Over the past decade “design assistance,” that is, where the computer is viewed as an Intelligent Design Assistant (IDA) (MacCallum et al., 1987), has emerged in knowledge based design support and has formed the basic research strategy for the CAD Centre, University of Strathclyde, since the mid-1980s. Within this philosophy, an IDA would act as a colleague to a designer, providing guidance, learning from past design experiences, carrying out semi- and fully-automated tasks, explaining its reasoning and in essence complementing the designer's own natural skills, and thus leaving the ultimate decision-making, control, and responsibility with the designer (Fig. 1).

An interactive system, referred to as MECXPERT {Mechanism Expert}, has been designed with the expressed purpose of assisting nonexpert design engineers in creating mechanisms for fulfilling specific motion-conversion and/or power-transmission requirements. The particular knowledge representation scheme chosen for this application comprises a hybrid formulation of a rule-based production system with a frame-based approach. The underlying control strategy is based on a series of special-purpose, domain-specific operators whose function is to move from one problem space to another through various stages or ‘states’ that comprise the mechanism design process.

The primary focus of this paper centers on the representation of knowledge and its control within an expert system for creative mechanism design. An overview summarizing the reasons for developing such an expert system is provided, and the formulation of a problem is discussed through an example taken from the design of a variable-stroke internal-combustion engine mechanism.

This paper presents a multipurpose chair grammar and its implementation in the design tool ChairDNA. This tool is oriented for the exploration of design alternatives in the early concept phase of the chair design process. This work addresses two shortcomings within the research area of shape grammars (SGs), namely, the lack of implementation of SGs applied to design domains, and the lack of practical applications in real-life design scenarios. To address these problems, a methodology is proposed for the implementation of a SG (more specifically, a set grammar) into a tool, comprising the translation of the grammar into user-interface elements oriented for design practitioners. By using the proposed tool, the user can add/delete chair components and edit shape parameters, while visualizing the effects on a three-dimensional digital model presented in a variety of CAD applications. Compared with other SG implementations, ChairDNA uses an approach that keeps under control the combinatorial explosion of rule applications, which simplifies the use of the tool by designers that do not have experience in SGs. The generative potential of the tool is demonstrated by generating chairs of different types, and its usability and utility in aiding the designer are evaluated by design students and design practitioners.

Concurrent Engineering needs a series of measures (or measurement criteria) that are distinct to each process, and a set of metrics to check (and validate) the outcome when two or more of the life-cycle processes are overlapped or required to be executed in parallel. Because product realization involves concurrent processes that occur across multiple disciplines and organizations, appropriate measures and the methods of qualifying metrics are essential. Inevitably, such concurrent processes generate design conflicts among multiple life-cycle concerns. Individual assurances of satisfying life-cycle design criterion (one at a time) do not capture the most important aspect of Concurrent Engineering—the system perspective—meaning achieving a well-balanced trade-off among the different life-cycle design measures. While satisfying life-cycle design measures in a serial manner only those, which are not in conflict, are occasionally met. The paper first describes a set of life-cycle measures and metrics and explains how those could be used for gaining operational excellence. Second, this paper provides an insight into the mechanisms (such as knowledge-based systems, rule-based simulation, and rule-based optimization) to ensure an effective trade-off across different life-cycle measures, customer requirements, and their inclusion into a product design, development, and delivery (PD3) process.

Maintenance plays a critical role in reducing operating cost and maximizing reliability of a complex engineering system. This paper proposes a novel maintenance-focused, system-level design framework that attempts to capture the interactions between maintenance strategies and system-level design parameters overlooked in current modeling approaches. The goal of this maintenance-focused approach is to help designers better understand the interconnectedness of system architecture, choice of maintenance strategy, and uncertainties in a design. Application of the proposed design framework is demonstrated through a case example of a power plant condenser system. Results show that using an integrated approach can reveal the many nonobvious interactions between subsystems, and produce system designs that have lower life-cycle cost compared to traditional sequential design approaches.

A general constraint-based formulation for the analysis and partial synthesis of two-dimensional truss structures is presented. This formulation is general in that it handles statically determinate and statically indeterminate trusses with pin and roller supports, and concentrated joint loads. The formulation is constraint-based in that the physical behavior of truss components is declaratively represented using constraints.

The analysis and partial synthesis of a truss structure manifest themselves in proving the satisfiability of the constraints associated with the structural components. An artificial intelligence approach called constraint logic programming is used for representing and satisfying constraints. A constraint logic programming language, called CLP(R), is used for implementing the formulation. The implemented program consists of sixteen rules. These rules are used for both the analysis and partial synthesis of truss structures. Several truss analysis and synthesis examples using the formulation are presented.

This paper addresses manufacturing research involving contributions in computer science, control theory and material processing science. The research objective has been to develop intelligent, †self-directed‡ control systems which enable in situ control path generation based on †product-processing‡ feedback. A †product-directed‡ control philosophy which emphasizes product quality is described together with a generic architecture for representing process knowledge. Specific details are presented involving the development and application of a self-directed material processing system for autoclave curing of composites together with recent research results, production challenges and future research in †self-improving‡ material processing systems.

Genetic algorithm (GA) and singular value decomposition (SVD) are deployed for the optimal design of both Gaussian membership functions of antecedents and the vector of linear coefficients of consequents, respectively, of adaptive neurofuzzy inference systems (ANFIS) networks that are used for modeling of the explosive cutting process of plates by shaped charges. The aim of such modeling is to show how the depth of penetration varies with the variation of important parameters, namely, the apex angle, standoff, liner thickness, and mass of charge. It is demonstrated that SVD can be effectively used to optimally find the vector of linear coefficients of conclusion parts in ANFIS models and their Gaussian membership functions in premise parts are determined by a GA.