Agent models provide a generalized approach to the design of systems that autonomously monitor and affect an environment. Societies of agents that reason and communicate about an environment can achieve complex emergent behavior to facilitate and augment human activities. This paper introduces artificial agent technologies as a means by which the behavioral complexity of virtual worlds can be extended to provide the functionality needed to support collaboration in multidisciplinary design teams. Three key roles are identified that artificial agents can play to provide this functionality: support for multiple views of designed objects, support for the expression of relationships between designed objects, and compatibility with existing design tools. The implementation of a society of agents fulfilling these roles within a virtual world based, multidisciplinary design tool called DesignWorld is described. The increased behavioral complexity and functionality of DesignWorld's underlying virtual world is demonstrated using the results of multidisciplinary design experiments with DesignWorld.

The application of machine learning (ML) to solve practical problems is complex. Only recently, due to the increased promise of ML in solving real problems and the experienced difficulty of their use, has this issue started to attract attention. This difficulty arises from the complexity of learning problems and the large variety of available techniques. In order to understand this complexity and begin to overcome it, it is important to construct a characterization of learning situations. Building on previous work that dealt with the practical use of ML, a set of dimensions is developed, contrasted with another recent proposal, and illustrated with a project on the development of a decision-support system for marine propeller design. The general research opportunities that emerge from the development of the dimensions are discussed. Leading toward working systems, a simple model is presented for setting priorities in research and in selecting learning tasks within large projects. Central to the development of the concepts discussed in this paper is their use in future projects and the recording of their successes, limitations, and failures.

Recent research in the area of computer-aided engineering design has focused on the development of environments that provide effective integration of several domain specialties for complex multidisciplinary synthesis problems. The definition of communication requirements for co-operative interaction—and the subsequent establishment of a conceptual model for automating the process—are important considerations in the development of such environments. A communication model can also provide the basis for development of a knowledge engineering strategy by defining the organizational and representational requirements for domain knowledge in the automated system. This paper presents a conceptual model for communication in automated interactive design and demonstrates how the model can be employed as a knowledge engineering tool to facilitate the acquisition and organization of domain expertise. Both the process architecture and semantic modeling aspects of the communication problem are considered. An example is included which illustrates the use of the model in formulating an automated integrated engineering system in the domain of floor and equipment layout and design for industrial facilities.

Two studies tested whether introducing images to designers during the design process lead to more useful design solutions as evaluated by the end users’ willingness to use the final design. It was hypothesized based on theories in cognitive science and design that there were at least two paths from images to usefulness. One path concerns analogically transferring within-domain properties to the design solution. The other path concerns mentally simulating end-user characteristics and preferences and inclusion of the user in the resulting design. Study 1 supported that random images led to increased outcome usefulness, and supported both hypothesized paths, by using within-domain products and end-user images as input. Study 2 showed that the image categories competed for attention, and that the within-domain product stimuli attracted the most attention and was considered the most inspirational to the designers. The practical use of the technique may lead to only marginally original products perhaps limiting its applicability to incremental innovation.

System architecture is important for the design of complex mechatronic systems because it acts as an intermediator between conceptual design and detail design. An explicit and exact system modeling language is imperative for successful architecture design. However, some deficiencies remain, such as the lack of geometry elements, hybrid behavior description, and specific association semantics for existing architecture modeling languages. In this study, a Systems Modeling Language extension for mechatronic system architecture modeling called ArchME is proposed. The requirements for the mechatronic System Modeling Language are analyzed, and the metamodels are defined. Then, the modeling elements are determined. Finally, the profiles based on the systems modeling language are defined to support the modeling of function, behavior, structure, and their association. This enables system designers to model the system architecture and facilitates communication between different stakeholders. A case study is provided to demonstrate the modeling capability of ArchME.

In many cases, the use of product configurators provides a number of benefits for engineering-oriented companies, such as shorter lead times, the use of fewer man hours, and improved product quality. However, not all of these projects are successful. The task of defining what should be implemented into the configurator is much more complex than anticipated in many configurator projects. In the worst-case scenario, this implies that the configurator is never fully developed or it fails to support the users in a satisfactory manner. The difficulties in creating adequate specifications of what to implement in the knowledge base of a configurator have been explained in much of the configuration literature by the assumption that much of the relevant knowledge of the product experts is tacit. However, this paper shows that this kind of explanation can be misleading and may even be incorrect. This is accomplished by discussing the problems inherent in such explanations and through interviews with configuration project knowledge engineers. Therefore, according to this basis, this paper provides general recommendations concerning the use of the term tacit knowledge and outlines issues related to the acquisition of product knowledge in configuration projects.

As manufacturing systems become more sophisticated and complicated, effective managers know how they play a crucial role in managing an enterprise and managers know how to deal with their dynamics and uncertainty. In this article, a formalism based on the computer-integrated manufacturing open-system architecture (CIMOSA) reference model is presented to specify the business processes and enterprise activities at the knowledge level. The formalism uses an integration of multiple types of knowledge, including precise, muddy, and random symbolic and numerical knowledge to systematically represent enterprise behavior and functionality. To support the modelling process, a prototype is developed and an example for a maintenance activity is presented to demonstrate the effectiveness of the proposed method.

From its inception the journal Artificial Intelligence for Engineering Design, Analysis and Manufacturing recognized that designing is the precursor to analysis and manufacturing by placing it at the front of the list of areas it covers. Designing distinguishes itself from other aspects of engineering by its goal of changing the world within which it operates: designers are change agents. This characteristic makes designing a difficult task even for humans let alone for machines, because most of our knowledge and the means to acquire it assume that the world is given to us and what we need to do is characterize it. The Journal provided one of two continuing publication outlets for artificial intelligence (AI) in engineering at the time. This created the opportunity to have a focal point for the publication of archival research in the area, research that had previously appeared in disparate locations. It also offered the potential to develop a coherent research area where future research could build on previously published 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.

Product development involves multiple phases. Design review (DR) is an essential activity formally conducted to ensure a smooth transition from one phase to another. Such a formal DR is usually a multicriteria decision problem, involving multiple disciplines. This paper proposes a systematic framework for DR using fuzzy set theory. This fuzzy approach to DR is considered particularly relevant for several reasons. First, information available at early design phases is often incomplete and imprecise. Second, the relationships between the product design parameters and the review criteria cannot usually be exactly expressed by mathematical functions due to the enormous complexity. Third, DR is frequently carried out using subjective expert judgments with some degree of uncertainty. The DR is defined as the reverse mapping between the design parameter domain and design requirement (review criterion) domain, as compared with Suh's theory of axiomatic design. Fuzzy sets are extensively introduced in the definitions of the domains and the mapping process to deal with imprecision, uncertainty, and incompleteness. A simple case study is used to demonstrate the resulting fuzzy set theory of axiomatic DR.

Decisions related to system architecture are difficult because of fuzziness and lack of information combined with often-conflicting objectives. We organized an industrial workshop with the objective of choosing 5 out of 800 architectures. The first step, the identification of selection criteria, proved to be the greatest challenge. As a result, designers selected system architectures that did not satisfy them without being able to explain why. It appeared that most of the difficulties faced by the designers came from the criteria used for architecture selection. This study aims to identify what made the selection criteria difficult to use. The audio recordings of the workshop were transcribed and analyzed in order to identify the obstacles related to the definition and the use of selection criteria. The analysis highlights two issues: the interdisciplinarity of system architecture makes criteria interdependent and the lack of information makes it impossible to define an exhaustive set of criteria. Finally, this study provides recommendations for selecting appropriate selection criteria and insights for future selection support tools dedicated to system architecture design.

Fixtures are used in almost all manufacturing operations. They take on many different forms, ranging from simple mechanical vises to modular fixtures to specialized fixtures. For cylindrical parts or simple prismatic parts, automatic design of fixtures is feasible. However, for parts of irregular shape such as castings or forged parts, the automation of fixture design is a complex problem. In this paper we address the problem of fixture layout for parts of complicated shape. Issues related to automatic design such as completeness, soundness, and computing efficiency are first discussed. A theoretical framework based on algebraic formulations is developed which models human reasoning employed for workpiece restraint. These formulations will ensure the completeness and soundness of the design methodology. They also provide a mechanism for constraint propagation in the design space. Finally, the generality of the framework is discussed.

The design of sheet metal components is perhaps one of the more challenging concurrent activities for design and manufacturing engineers. To aid this design process, a method is developed to encapsulate the constraints of sheet metal that make designing such components a tedious and iterative procedure. This project involves the implementation and testing of a geometric representation scheme for building feasible sheet metal components through the use of 17 grammar rules that capture manufacturing operations like cutting and bending. The implemented system has benefits both as a user interaction tool and as the basis for a computational design synthesis approach for designing sheet metal components. An example of a constructed sheet metal component is shown along with the method for invoking the sheet metal grammar to create this component.

Scientific data is often analyzed in the context of domain-specific problems, for example, failure diagnostics, predictive analysis, and computational estimation. These problems can be solved using approaches such as mathematical models or heuristic methods. In this paper we compare a heuristic approach based on mining stored data with a mathematical approach based on applying state-of-the-art formulae to solve an estimation problem. The goal is to estimate results of scientific experiments given their input conditions. We present a comparative study based on sample space, time complexity, and data storage with respect to a real application in materials science. Performance evaluation with real materials science data is also presented, taking into account accuracy and efficiency. We find that both approaches have their pros and cons in computational estimation. Similar arguments can be applied to other scientific problems such as failure diagnostics and predictive analysis. In the estimation problem in this paper, heuristic methods outperform mathematical models.

In an increasingly competitive economic environment, innovation has become an invaluable asset to the organization and for effective knowledge management (KM). Nowadays, organizations are knowledge based and their success and survival depend on creativity, diversity, and innovation. A knowledge map is a vital tool for better KM and innovation. To this effect, the innovation processes on KMin education system through knowledge transfer activities will facilitate the shift from teaching as knowledge transmission to teaching as learning facilitation. In this context, we present a new approach based on, the one hand, the critical knowledge mapping being based on an extraction work of the expert knowledge and on the other hand, the description of the conceptual framework design which allows one to exploit and integrate knowledge capitalized and external knowledge by the open innovation process. In addition, this practice makes it possible to examine how KM, in particular, the knowledge mapping, can be used to establish the flow of the internal and external information in order to increase the efficiency of creativity and invention.

We developed an efficient semisupervised feedforward neural network clustering model with one epoch training and data dimensionality reduction ability to solve the problems of low training speed, accuracy, and high memory complexity of clustering. During training, a codebook of nonrandom weights is learned through input data directly. A standard weight vector is extracted from the codebook, and the exclusive threshold of each input instance is calculated based on the standard weight vector. The input instances are clustered based on their exclusive thresholds. The model assigns a class label to each input instance through the training set. The class label of each unlabeled input instance is predicted by considering a linear activation function and the exclusive threshold. Finally, the number of clusters and the density of each cluster are updated. The accuracy of the proposed model was measured through the number of clusters and the quantity of correctly classified nodes, which was 99.85%, 100%, and 99.91% of the Breast Cancer, Iris, and Spam data sets from the University of California at Irvine Machine Learning Repository, respectively, and the superior F measure results between 98.29% and 100% accuracies for the breast cancer data set from the University of Malaya Medical Center to predict the survival time.

Architecture, engineering, and construction team members, while collaborating on building projects, rely on past experiences and content through the use of project design archives (whether in paper or digital format). Underutilization of potential knowledge in the decision-making process of data, information, and knowledge reuse is limited by access to these archives, because of sheer size, decontextualized content, and inconvenient access and presentation. This paper presents an integrated solution called CoMem–iRoom that leverages two technologies Corporate Memory (CoMem) and interactive Room (iRoom) developed at Stanford. CoMem–iRoom addresses critical limitations (content, context, visualization, and interactivity) constraining the process of collaborative exploration toward knowledge reuse and decision making.

The assessment of damage of structural concrete elements relies on the engineering judgment of the person in charge of the inspection and evaluation. This paper describes a rule-based prototype expert system developed to assist an engineer engaged in the task of assessing postearthquake damage to structural concrete elements in the task of providing guidance for inspection as well as criteria for evaluation and courses of action to take afterwards. The expert system, called DASE, identifies the most likely failure modes that may be developed by columns or beams of a damaged building, determines the severity of damage and, suggests immediate actions to take afterwards, such as rehabilitation procedures or tests, to maintain an acceptable local safety level. Floor damage classification and restoration guidelines can also be provided, assuming that all of the structural components in a building's floor have been inspected. The Analytic Hierarchy Process has been implemented as the overall framework to determine the severity of damage, since it allows judgments and personal values to be represented in a systematic and rational manner. DASE provides graphics that customize the user interface and an explanation facility. The knowledge acquisition process consisted primarily of the analysis of documented knowledge found through literature research.

This paper describes a generic ontology-based approach that eases the requirement analysis (RA) work. The approach enables the user to derive method-specific RA tools for different applications. The derivation process is based on a unified framework that contains a software methodology ontology and a knowledge acquisition ontology. The former contains a library of software RA methods and a set of modeling support entities, which use the library to construct methodological knowledge. The latter contains a library of knowledge acquisition techniques and a set of acquisition support entities, which work on the library, guided by the generated methodological knowledge, to extract domain knowledge. The generated methodological knowledge, coupled with the domain knowledge, forms the RA tools. We have demonstrated the use of the approach by deriving an RA tool to assist the system analyst to acquire and formalize a requirement specification for a network management system. This approach facilitates the integrating, sharing, and reuse of software methodologies and knowledge acquisition techniques and alleviates the problems associated with the correct generation of requirement specification for different domains.

This paper proposes a method to compare the performances of different methods for robust design optimization of computationally demanding models. Its intended usage is to help the engineer to choose the optimization approach when faced with a robust optimization problem. This paper demonstrates the usage of the method to find the most appropriate robust design optimization method to solve an engineering problem. Five robust design optimization methods, including a novel method, are compared in the demonstration of the comparison method. Four of the five compared methods involve surrogate models to reduce the computational cost of performing robust design optimization. The five methods are used to optimize several mathematical functions that should be similar to the engineering problem. The methods are then used to optimize the engineering problem to confirm that the most suitable optimization method was identified. The performance metrics used are the mean value and standard deviation of the robust optimum as well as an index that combines the required number of simulations of the original model with the accuracy of the obtained solution. These measures represent the accuracy, robustness, and efficiency of the compared methods. The results of the comparison show that sequential robust optimization is the method with the best balance between accuracy and number of function evaluations. This is confirmed by the optimizations of the engineering problem. The comparison also shows that the novel method is better than its predecessor is.