A selective, multiple-criteria method is proposed for handling constraint violations in well-defined design problems. First, the types of design problems upon which the method may apply are presented and a graph topology is adopted for representing the problem decomposition in the level of design parameter interrelations (design parameter graphs). Next, the problem of constraint violations for the design parameters is discussed. It is shown that these violations can be resolved by modifying the values of the primary design parameters and recalculating the values of the violated parameters. Any “blind” attempt of modifying the values of the primary design parameters for resolving occurring violations may, eventually, create additional violations. The proposed method guides the designer toward those primary design parameters that present the least possibility of creating more violations when their values are modified. This is achieved by applying multiple criteria and by producing a final, sorted list of primary design parameters. The designer may then choose the first element of this list to handle efficiently the violations of the design parameters. Two examples are given on a design space where constraint violations occur. Through these examples, the capability of the proposed method in helping the designer to handle constraint violation is shown. The concluding remarks, except for summarizing the potential of the method, determine its boundaries and include a reference on relative work currently under investigation.

Requirement planning is one of the most critical tasks in the product development process. Despite its significant impact on the outcomes of the design process, engineering requirement planning is often conducted in an ad hoc manner without much structure. In particular, the requirement planning phase suffers from a lack of quantifiable measures for evaluating the quality of the generated requirements and also a lack of structure and formality in representing engineering requirements. The main objective of this research is to develop a formal Web Ontology Language ontology for standard representation of engineering requirements. The proposed ontology uses explicit semantics that makes the ontology amenable to automated reasoning. To demonstrate how the proposed ontology can support requirement analysis and evaluation in engineering design, three possible services enabled by the ontology are introduced in this paper. These services are information content measurement, specificity and completeness analysis, and requirement classification. The proposed ontology and its associated algorithms and tools are validated experimentally in this work.

The task of modeling, i.e., of creating a set of equations that can be used to predict the behavior of a physical object, is a key step in engineering analysis. This paper describes a computer system, MSG, for generating mathematical models to analyze physical systems involving heat transfer behavior. MSG is motivated by the need for modeling in an automated design process. The models are sets of equations which may include algebraic equations, ordinary differential equations and partial differential equations. MSG uses the strong domain theory to guide model construction in three sequential tasks: identify regions of interests on an object, determine relevant heat transfer and energy storage processes, and transform these processes into equations. The decisions in these tasks are guided by estimates of variation in temperature and material property, and the relative strengths of heat transfer processes.

This paper examines the role of design concepts in a modus operandi as opposed to a modus operatum, which is how their generation, as it unfolds over time, is perceived by someone involved in designing instead of with the hindsight of being finished. To this end, the interactive activation and competition model, commonly used to model the retrieval of information from stored knowledge of individual exemplars, is applied in the context of architectural design. Parsing the data of an architect's think-aloud protocol through this model at successive points in the design process results in a photo shoot of a design concept “under construction.” This allows for the start of appreciating and accounting for the highly elusive character of concepts during design.

We describe three approaches to identify novel product affordances: affordance of absence; insights from lead users, specifically do-it-yourselfers (DIYers); and natural-language searches. While these approaches were separately pursued, we show their connection to each other in this paper. We begin by describing the affordance of absence, inspired by insights on affordances arising from a lack of resources. For example, in the absence of specialized tools, more general tools are used to accomplish similar tasks. Such absence clarifies how other tools could be modified to add relevant features and identifies critical features of the absent tool. In addition, the temporary removal of physical features and objects enables user interaction in ways that may not emerge in their presence. Affordance of absence has the potential to more fully specify affordances for a given object and to help overcome functional fixedness. For the second approach, we describe insights from DIYers obtained from the “IKEA hackers” online community. We consider DIYers lead users for seeking out and exploiting product affordances, often transforming product functions dramatically. We also discuss their projects through the lens of affordance of absence. For the third approach, we outline our natural-language approach to affordance extraction, beginning with consumer product reviews provided for Canadian Tire, a major Canadian retailer. We describe efforts toward automatically identifying less common affordances, and the use of cue phrases to highlight insightful DIY transformations from the IKEA hackers community. Finally, we comment on the potential value of this work for product design in general.

This paper defines, develops algorithms for, and illustrates the design use of a class of mathematical operations. These operations accept as inputs a system of linear constraint equations, Ax = b, an interval matrix of values for the coefficients A, and an interval vector of values for either x or b. They return a set of values for the other variable that is “sufficient” in this sense. Suppose that ◯ is an interval of input vectors, and  an interval matrix. Then, one Sufficient-Points operation returns a set of vectors ~ such that for each b in ~, the set of x values that can be produced by inserting all the values of  into Ax = b is a superset of the input vector x. These operations have been partly overlooked by the interval matrix mathematics community, but are mathematically interesting and useful in the design, for example, of circuits.

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.

Engineering design has proven to be a rich context for applying artificial intelligence (AI) methods, but a categorization of such methods applied in AI-based design research works seems to be lacking. This paper presents a focused literature review of AI-based methods mapped to the different stages of the engineering design process and describes how these methods assist the design process. We surveyed 108 AI-based engineering design papers from peer-reviewed journals and conference proceedings and mapped their contribution to five stages of the engineering design process. We categorized seven AI-based methods in our dataset. Our literature study indicated that most AI-based design research works are targeted at the conceptual and preliminary design stages. Given the open-ended, ambiguous nature of these early stages, these results are unexpected. We conjecture that this is likely a result of several factors, including the iterative nature of design tasks in these stages, the availability of open design data repositories, and the inclination to use AI for processing computationally intensive tasks, like those in these stages. Our study also indicated that these methods support designers by synthesizing and/or analyzing design data, concepts, and models in the design stages. This literature review aims to provide readers with an informative mapping of different AI tools to engineering design stages and to potentially motivate engineers, design researchers, and students to understand the current state-of-the-art and identify opportunities for applying AI applications in engineering design.

Automated support of design teams, consisting of both human and automated systems, requires an understanding of the role of trust in distributed design processes. By explicating trust, an individual designer's decisions become better understood and may be better supported. Each individual designer has his or her private goals in a cooperative design setting, in which requirement conflicts and resource competitions abound. However, there are group goals that also need to be reached. This paper presents an overview of research related to trust in the context of agents and design, a computational knowledge-level model of trust based on the seven beliefs distinguished by Castelfranchi and Falcone, and an example of the use of the trust model in a specific design process, namely, Website design from the perspective of a single designer. The results are discussed in the context of distributed design in open systems.

The paper proposes and evaluates a binary network architecture and complementary training algorithm designed for pattern classification, with applications in a variety of engineering problems. The advantages of the system are that it can always converge on zero error for a set of unambiguous training patterns (if required), converges rapidly, and circumvents the issue of how many hidden neurons to incorporate in a network. The main benefit of the proposed system over Hamming networks, its main counterpart, is that it can group patterns into different classes along any boundary. The system is shown to outperform 1) the Hamming network for a character recognition problem where the images are subject to both position change and noise; and 2) a radial-Gaussian network in a truck-type classification problem. A variant of the system, where hidden neuron thresholds are set to zero, is shown to further improve performance if a comprehensive set of noise-free input patterns are available for training.

This paper presents an expert system (called ADES, i.e. ATP Design Expert System) for the automatic design of Automatic Train Protection systems (ATP). An ATP system is a railway signalling system constituted by a set of logic circuits that control the safe movement of trains within a railway station.

AI techniques proved feasible to address the particular design problem discussed: ADES is able to rapidly design good control circuits to meet operational requirements by using a well-structured, explicitly represented, in depth knowledge of Automatic Train Protection. The use of AI techniques facilitates the maintenance and extension of ADES to face new or unplanned requirements.

Implementing both the expert system and its environment tools in the PROLOG language, by using meta-interpretation techniques, has led to the rapid prototyping of the overall system. Optimization techniques have also been developed to allow ADES to be efficiently executed.

The concept of a function is of great importance in design. This paper describes from theory how designers should reason about functions when designing. This paper introduces the link model, showing how functions and properties link the product and its use, to the perceived value of the product. The important and useful distinction between functions and properties is made along with the distinction between “wirk functions,” which is what the product does when operating, and “use functions,” which is what the product is used for. The paper makes a novel contribution beyond previous literature, showing that not only is a product's behavior or mode of action designed but also the use activity of the end user. Based on the theoretical perspective unfolded, the authors offer nine mind-sets for both design practitioners and researchers to consider when reasoning about functions.

In a construction project, resource leveling techniques are necessary as a primary schedule-improvement tool to reduce overall project cost by decreasing day-to-day fluctuation in resource usage and resource idleness. There are, however, some limitations in traditional resource leveling techniques. Conventional heuristic approaches cannot guarantee a near-optimum solution for every construction project; a given heuristic may perform well on one project and poorly on another. The existing optimization approaches, such as linear programming and enumeration methods, are best applicable only to small size problems. Recently, there has been success in the use of Artificial Neural Networks (ANNs) for solving some optimization problems. The paper discusses how state-of-the-art ANNs can be a functional alternative to traditional resource leveling techniques. It then investigates the application of different ANN models (such as backpropagation networks, Hopfield networks, Boltzmann machines, and competition ANNs) to resource leveling problems. Because the development of ANNs involves not only science but also experience, the paper presents various intuitive yet effective ways of mapping resource leveling problems on different applicable ANN architectures. To demonstrate the application of ANNs to resource leveling, a simple ANN model is developed using a Hopfield network. The conclusion highlights the usefulness and the limitations of ANNs when applied to resource leveling problems.

Sometime in the mid-1980s, in an urban restaurant some-where in the United States, I was sitting next to a Japanese engineer who was talking to a U.S. engineer. The following is the record of their conversation that I made with my Sony Walkman.

This paper addresses an important application of machine learning (ML) in design. One of the major bottlenecks in the process of engineering analysis by using the finite-element method—a design of the finite-element mesh—was a subject of improvement. Defining an appropriate geometric mesh model that ensures low approximation errors and avoids unnecessary computational overhead is a very difficult and time-consuming task based mainly on the user's experience. A knowledge base for finite-element mesh design has been constructed using the ML techniques. Ten mesh models have been used as a source of training examples. The mesh dataset was probably the first real-world relational dataset and became one of the most widely used training set for experimenting with inductive logic programming (ILP) systems. After several experiments with different ML systems in the last few years, the ILP system CLAUDIEN was chosen to construct the rules for determining the appropriate mesh resolution values. The ILP has been found to be an effective approach to the problem of mesh design. An evaluation of the resulting knowledge base shows that the mesh design patterns are captured well by the induced rules and represent a solid basis for practical application. The aim of this paper is not only to present the real-life ML application to design, but also to describe and discuss a relation of the work being done to the topic of this special issue: the proposed “dimensions” of ML in design.

Universal access on equal terms to audiovisual content is a key point for the full inclusion of people with disabilities in activities of daily life. As a real challenge for the current Information Society, it has been detected but not achieved in an efficient way, due to the fact that current access solutions are mainly based in the traditional television standard and other not automated high-cost solutions. The arrival of new technologies within the hybrid television environment together with the application of different artificial intelligence techniques over the content will assure the deployment of innovative solutions for enhancing the user experience for all. In this paper, a set of different tools for image enhancement based on the combination between deep learning and computer vision algorithms will be presented. These tools will provide automatic descriptive information of the media content based on face detection for magnification and character identification. The fusion of this information will be finally used to provide a customizable description of the visual information with the aim of improving the accessibility level of the content, allowing an efficient and reduced cost solution for all.

Constraint-based reasoning is a problem-solving approach based on deductive reasoning. In this approach, a problem is modeled in terms of hypotheses and conclusion constraints, and it is solved via constraint satisfaction. The ability to handle linear and nonlinear algebraic constraints is essential for successful application of constraint-based reasoning in engineering. Due to the scarcity of algebraic techniques for satisfying nonlinear constraints, little attention has been paid to the use of constraint-based reasoning for solving nonlinear problems. This paper examines the use of the Grobner Bases method for satisfying nonlinear constraints in the context of constraint-based reasoning. After a brief introduction to the Grobner Bases method and its role in constraint-based reasoning, two examples are presented. The first example illustrates the use of Grobner bases, in the context of constraint-based reasoning, for reasoning about the behavior of beams. The second example illustrates the geometry configuration of truss structures via constraint-based reasoning.

This Special Issue was motivated in part by the success of the 2001 American Association for Artificial Intelligence's Special Interest Group on Manufacturing (SIGMAN) workshop. SIGMAN has organized a series of these workshops that highlight significant advances in academic research and industrial practice. New manufacturing concepts such as agile/lean manufacturing and virtual manufacturing place increasing emphasis on the need for more intelligent manufacturing systems. It is no longer a question of whether artificial intelligence (AI) technologies will have an impact on manufacturing but one of better understanding and exploiting the broad potential of AI in this domain.