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 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.

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.

This paper presents an approach to solving constraint satisfaction problems using Asynchronous Teams of autonomous agents (ATeams). The focus for the constraint satisfaction problem is derived from an effort to support spatial layout generation in a conceptual design framework. The constraint specification allows a high-level representation and manipulation of qualitative geometric information. We present a computational technique based on ATeams to instantiate solutions to the constraint satisfaction problem. The technique uses a search for a solution in numerical space. This permits us to handle both qualitative relationships and numerical constraints in a unified framework. We show that simple knowledge, about human spatial reasoning and about the nature of arithmetic operators can be hierarchically encapsulated and exploited efficiently in the search. An example illustrates the generality of the approach for conceptual design. We also present empirical studies that contrast the efficiency of ATeams with a search based on genetic algorithms. Based on these preliminary results, we argue that the ATeams approach elegantly handles arbitrary sets of constraints, is computationally efficient, and hence merits further investigation.

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.

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.

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.

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.

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.

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.

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 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.

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.

A flow line is a conventional manufacturing system where all jobs must be processed on all machines with the same operation sequence. Line buffers allow nonpermutation flowshop scheduling and job sequences to be changed on different machines. A mixed-integer linear programming model for nonpermutation flowshop scheduling and the buffer requirement along with manufacturing implication is proposed. Ant colony optimization based heuristic is evaluated against Taillard's (1993) well-known flowshop benchmark instances, with 20 to 500 jobs to be processed on 5 to 20 machines (stages). Computation experiments show that the proposed algorithm is incumbent to the state-of-the-art ant colony optimization for flowshop with higher job to machine ratios, using the makespan as the optimization criterion.

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.

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.

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.

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.

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.