Design has always been seen as an inherently human activity and hard to automate. It requires a lot of traits that are seldom attributable to machines or algorithms. Consequently, the act of designing is also hard to assess. In particular in an educational context, the assessment of progress of design tasks performed by individuals or teams is difficult, and often only the outcome of the task is assessed or graded. There is a need to better understand, and potentially quantify, design progress. Natural Language Processing (NLP) is one way of doing so. With the advancement in NLP research, some of its models are adopted into the field of design to quantify a design class performance. To quantify and visualize design progress, the NLP models are often deployed to analyze written documentation collected from the class participants at fixed time intervals through the span of a course. This paper will explore several ways of using NLP in assessing design progress, analyze its advantages and shortcomings, and present a case study to demonstrate its application. The paper concludes with some guidelines and recommendations for future development.

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.

Few existing Computer Aided Design (CAD) systems provide assistance to designers in developing geometric concepts at the early design stages. Instead they require a high level of precision and detail suited to detail design. To support the early geometric design, a CAD system should provide utilities for the rapid capture and iterative development of vague geometric models. This paper presents a pilot system that is being developed based on such a vision. The system has adopted minimum commitment modelling and incremental refinement as the guiding principles. The representation of geometric configuration is based on a parametric and constraint-based geometric design model, and provides a uniform representation of the approximate and precise size and location parameters. A constraint-based mechanism has been developed for processing geometric information. The use of the system in assisting the development of a geometric configuration is also demonstrated. Finally, features and limitations of the system as well as relations to relevant works are discussed, and based on this a number of key research directions are established.

This article discusses the issues that arise in the design and implementation of expert systems. These issues include: task selection; the stages of development of expert system projects; knowledge acquisition; languages and tools; development and run-time environments; and organizational and institutional issues. The article closes with some speculation about the future development of expert systems.

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.

Methods for efficient variance-based global sensitivity analysis of complex high-dimensional problems are presented and compared. Variance decomposition methods rank inputs according to Sobol indices that can be computationally expensive to evaluate. Main and interaction effect Sobol indices can be computed analytically in the Kennedy and O'Hagan framework with Gaussian processes. These methods use the high-dimensional model representation concept for variance decomposition that presents a unique model representation when inputs are uncorrelated. However, when the inputs are correlated, multiple model representations may be possible leading to ambiguous sensitivity ranking with Sobol indices. In this work, we present the effect of input correlation on sensitivity analysis and discuss the methods presented by Li and Rabitz in the context of Kennedy and O'Hagan's framework with Gaussian processes. Results are demonstrated on simulated and real problems for correlated and uncorrelated inputs and demonstrate the utility of variance decomposition methods for sensitivity analysis.

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.

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.

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.

The main objective of this paper is to develop a novel approach for linear kitchen layout design which utilizes information from existing layouts via machine learning algorithms. With the growing popularity of large-scale virtual 3D environments for architectural visualization and the game industry, the manual interior design of virtual scenes becomes prohibitively expensive in terms of time and resources. In our approach, the machine learning model automatically generates layout suggestions. The proposed procedural kitchen generation (PKG) model is a pipeline of six Machine Learning (ML) classifiers that are trained and tested on a kitchen layout dataset created by interior designers. The performances of the model are evaluated for the following classifiers: Random forest, Decision tree, AdaBoost, Naive Bayes, MLP, SVM, and L2 Logistic regression. Random forest, as the best performing classifier is used in the final PKG model, and integrated into Unity Engine for automatic 3D kitchen generation and presentation. The PKG model is evaluated in the quantitative and perceptual study, showing better performance than the prior rule-based method. The perceptual study results demonstrate that our tool can be used to speed up designer's work, improve communication with clients, and educate interior design students.

This paper aims to situate functional abstraction in light of systems thinking. While function does not extensively appear in systems thinking literature, the literature does identify function as part of systems thinking that enables us to recognize and connect that function has a role in building a systems thinking approach for students. A systems thinking approach is valuable for students since it helps them view a system holistically. In this research, we measure how well students are able to abstract function. We asked students to generate functions for two different products and examined how students taught functional modeling and function enumeration compare to students who are only taught function enumeration. The student responses were examined using a rubric that we developed and validated for assessing function. This rubric may be used to classify functions by correctness (correct, partially correct, and incorrect) and categories (high level, interface, low level, and ambiguous). On questions where students were not explicitly asked to write a high-level function or low-level function, and so on, students who were taught functional modeling were able to better demonstrate systems thinking in their responses (low-level and interface functions) than those students who were only taught function enumeration.

In engineering design, the end goal is the creation of an artifact, product, system, or process that fulfills some functional requirements at some desired level of performance. As such, knowledge of functionality is essential in a wide variety of tasks in engineering activities, including modeling, generation, modification, visualization, explanation, evaluation, diagnosis, and repair of these artifacts and processes. A formal representation of functionality is essential for supporting any of these activities on computers. The goal of Parts 1 and 2 of this Special Issue is to bring together the state of knowledge of representing functionality in engineering applications from both the engineering and the artificial intelligence (AI) research communities.

This paper is concerned with presenting guidelines to aide in the selection of the appropriate network architecture for back-propagation neural networks used as approximators. In particular, its goal is to indicate under what circumstances neural networks should have two hidden layers and under what circumstances they should have one hidden layer. Networks with one and with two hidden layers were used to approximate numerous test functions. Guidelines were developed from the results of these investigations.

We present a methodology of learning fuzzy rules using an iterative genetic algorithm (GA). The approach incorporates a scheme of partitioning the entire solution space into individual subspaces. It then employs a mechanism to progressively relax or tighten the constraint. The relaxation or tightening of constraint guides the GA to the subspace for further iteration. The system referred to as the iterative GA learning module is useful for learning an efficient fuzzy control algorithm based on a predefined linguistic terms set. The overall approach was applied to learn a fuzzy algorithm for a water bath temperature control. The simulation results demonstrate the effectiveness of the approach in automating an industrial process.

This paper proposes and evaluates a neural network-based method for simulating manufacturing processes that exhibit both noncontinuous and stochastic behavior processes more conventionally modeled, using discrete-event simulation algorithms. The incentive for developing the technique is its potential for rapid execution of a simulation through parallel processing, and facilitation of the development and improvement of models particularly where there is limited theory describing the dependence between component processes. A brief introduction is provided to a radial-Gaussian neural network architecture and training process, the system adopted for the work presented in this paper. A description of the basic approach proposed for applying this technology to simulation is then described. This involves the use of a modularized neural network approach to model construction and the prediction of the occurrence of events using information retained from several previous states of the simulation. A class of earth-moving systems, comprising a push-dozer and a fleet of scrapers, is used as the basis for assessing the viability and performance of the proposed approach. A series of experiments show the neural network to be capable of both capturing the characteristic behavior and making an accurate prediction of production rates of scraper-based earth-moving systems. The paper concludes with an indication of some areas for further development and evaluation of the technique.

Artificial Intelligence has been very active in developing high-level symbolic reasoning paradigms that have resulted in practical expert systems. However, with a few exceptions, it has paid little attention to the automation of spatial reasoning. On the other hand, spatial reasoning has attracted the interest of several researchers in Robotics. One of the important problems that have been investigated is motion planning, and very significant results have been obtained. This paper describes an implemented system for designing pipe layouts automatically using motion planning techniques. It introduces a new approach to pipe layout design automation in which pipe routes are treated as trajectories left behind by rigid objects (‘robots’). We have implemented this approach in a basic Pipe Router that is described in detail in this paper. We have extended this router in order to make it capable of treating a variety of other constraints which are typical of practical pipe layout design problems. These constraints relate to the process carried out in the pipes, to the design of their mechanical support, and to the constructability and the ease of operation and maintenance of the designed pipe systems.

Linkage-type mechanisms have numerous applications in industry especially for automation Unfortunately, they are less popular due to lack of proper design tools. This paper describes our efforts to remove the technological barrier in mechanisms design automation. Although the ideas presented apply to automation of mechanisms design in general, the paper discusses the development of an expert system for a particular sub-set of mechanisms called Dwell mechanisms.

Many essential and desirable motion characteristics of mechanisms are so implicit that they are difficult to control by analytical methods. By systematically and extensively studying the entire motion characteristics of hundreds of linkages, a comprehensive classification system and heuristics were developed. This qualitative classification scheme led to a finite set of linkage models that cover the entire design space in the sense that any possible design falls under one or more of the models. Our system, called Dwell-Expert, incorporates this design expertise to select the best linkage model for a given set of design specifications and to compare that model against alternatives. The new design methodology and its implementation in AGNESS (A Generalized Network-based Expert System Shell) are explained. A design example is also presented. Our system can reduce even an experienced designer’s initial-design time from a day or more to a minute or less, assuming specifications have already been formulated. Such results motivate extension of this design methodology to other areas of mechanical design and engineering design in general.

This position paper proposes the P5BL initiative and vision, that is, Problem-, Project-, Product-, Process-, People-Based Learning. A definition is proposed for the P5BL teaching and learning approach. The discussion identifies key pedagogical issues and innovative roles of computing for each of the five critical Ps in a multisite, cross-disciplinary, project-centered, and team-oriented Architecture/Engineering/Construction environment.

Configuration is a complex task generally involving varying measures of constraint satisfaction, optimization, and the management of soft constraints. Although many successful systems have been developed, these are often difficult to maintain and to generalize in rapidly changing domains. In this paper, we consider building intelligent knowledge-based systems with maintainability well to the fore in our requirements for such systems. We introduce two case studies: the initial proof of concept, which was in the domain of computer configuration, and a further field-tested study, the configuration of compressors. Central to our approach is the use of the proof planning technique, and the clean separation of different kinds of knowledge: factual, heuristic, and strategic.

This article is an attempt to improve the efficiency of procedures for compositional synthesis of design solutions using building blocks. These procedures have found use in a wide range of applications, and are one of the most substantial outcomes of research into automated synthesis of design solutions. Due to their combinatorial nature, these procedures are highly inefficient in solving problems, especially when the database of building blocks for synthesis or the problem size is large. Previous literature often focuses on improving only the algorithm part of a procedure, although it is both its algorithm and database which together determine the overall efficiency of the procedure. This article reports the construction and analysis of an improved algorithm, based on bidirectional search, for efficient compositional synthesis of design solutions using a set of building blocks.