Identifying failure paths and potentially hazardous scenarios resulting from component faults and interactions is a challenge in the early design process. The inherent complexity present in large engineered systems leads to nonobvious emergent behavior, which may result in unforeseen hazards. Current hazard analysis techniques focus on single hazards (fault trees), single faults (event trees), or lists of known hazards in the domain (hazard identification). Early in the design of a complex system, engineers may represent their system as a functional model. A function failure reasoning tool can then exhaustively simulate qualitative failure scenarios. Some scenarios can be identified as hazardous by hazard rules specified by the engineer, but the goal is to identify scenarios representing unknown hazards. The incidences of specific subgraphs in graph representations of known hazardous scenarios are used to train a classifier to distinguish hazard from nonhazard. The algorithm identifies the scenario most likely to be hazardous, and presents it to the engineer. After viewing the scenario and judging its safety, the engineer may have insight to produce additional hazard rules. The collaborative process of strategic presentation of scenarios by the computer and human judgment will identify previously unknown hazards. The feasibility of this methodology has been tested on a relatively simple functional model of an electrical power system with positive results. Related work applying function failure reasoning to a team of robotic rovers will provide data from a more complex system.

We are interested in investigating the different types of knowledge and mechanisms that cause design problem-solving to become routine. We use the model of Routine Parametric Design incorporated in the language DSPL (Brown & Chandrasekaran, 1989), where both the parameters to be given values and the design knowledge needed are known in advance.

The concept of discretization of a design space is used to make initial selection of prior designs using specification matching, and to direct redesign with evaluation and iteration. A general paradigm for routine design has been developed and implemented in a system called IDS (Initial Design Selection.) For a given design domain, certain characteristics are identified that allow all specifications and models (which represent prior designs) in that design domain to be described in terms of their values or intervals that these values may lie in. These characteristics are seen as dimensions of a design space discretized into a finite number of partitions. Each partition is represented by a model, thought of as occupying its center. Each such model is associated with a deep model, which contains sufficient information for the modeled device, process, or system to be realized. Despite the fact that the models inhabiting the space are shallow, the paradigm comprises a relatively rich mathematical structure. This paper describes in detail a computational methodology to implement this domain-independent paradigm. The IDS paradigm presents a convenient and structured framework for acquiring and representing domain knowledge. This paper also briefly discusses an enhanced version of the system, which attempts iterative redesign directed by the particular mismatch between a specification and an otherwise promising model. To date, this methodology has been applied in a variety of design domains, including mechanism design, hydraulic component selection, assembly methods, and non-destructive testing methods.

Function-based design approaches have been criticized for being too narrow to properly guide design. Specifically, they are said to be unable to cope with nonfunctional considerations, such as cost or maintenance issues without invoking other concepts, such as constraints. This paper investigates two alternative conceptualizations of the design process: the practical affordance-based design approach, as elaborated by Maier and Fadel, and the more theoretical use plan approach by Houkes and Vermaas. This paper compares function-, affordance-, and use plan-based design approaches. It highlights strengths and weaknesses of each approach and proposes a definition of the function of an artifact in terms of its affordances.

We describe an approach that uses causal and geometric reasoning to construct explanations for the purposes of the geometric features on the parts of a mechanical device. To identify the purpose of a feature, the device is simulated with and without the feature. The simulations are then translated into a “causal-process” representation, which allows qualitatively important differences to be identified. These differences reveal the behaviors caused and prevented by the feature and thus provide useful cues about the feature's purpose. A clear understanding of the feature's purpose, however, requires a detailed analysis of the causal connections between the caused and prevented behaviors. This presents a significant challenge because one has to understand how a behavior that normally takes place affects (or is affected by) another behavior that is normally absent. This article describes techniques for identifying such elusive relationships. These techniques employ a set of rules that can determine if one behavior enables or disables another that is spatially and temporally far away. They do so by geometrically examining the traces of the causal processes in the device's configuration space. Using the results of this analysis, our program can automatically generate text output describing how the feature performs its function.

In current commercial feature modeling systems, support for direct manipulation of features is not commonly available. This is partly due to the strong reliance of such systems on constraints, but also to the lack of speed of current constraint solvers. In this paper, an approach to the optimization of geometric constraint solving for direct manipulation of feature dimensions, orientation, and position is described. Details are provided on how this approach was successfully implemented in the Spiff feature modeling system.

I was among those who proposed problem solving methods (PSMs) in the late 1970s and early 1980s as a knowledge-level description of strategies useful in building knowledge-based systems. This paper summarizes the evolution of my ideas in the last two decades. I start with a review of the original ideas. From an artificial intelligence (AI) point of view, it is not PSMs as such, which are essentially high-level design strategies for computation, that are interesting, but PSMs associated with tasks that have a relation to AI and cognition. They are also interesting with respect to cognitive architecture proposals such as Soar and ACT-R: PSMs are observed regularities in the use of knowledge that an exclusive focus on the architecture level might miss, the latter providing no vocabulary to talk about these regularities. PSMs in the original conception are closely connected to a specific view of knowledge: symbolic expressions represented in a repository and retrieved as needed. I join critics of this view, and maintain with them that most often knowledge is not retrieved from a base as much as constructed as needed. This criticism, however, raises the question of what is in memory that is not knowledge as traditionally conceived in AI, but can support the construction of knowledge in predicate–symbolic form. My recent proposal about cognition and multimodality offers a possible answer. In this view, much of memory consists of perceptual and kinesthetic images, which can be recalled during deliberation and from which internal perception can generate linguistic–symbolic knowledge. For example, from a mental image of a configuration of objects, numerous sentences can be constructed describing spatial relations between the objects. My work on diagrammatic reasoning is an implemented example of how this might work. These internal perceptions on imagistic representations are a new kind of PSM.

The hierarchical construction of solid models with current computer-aided design systems provide little support in creating and editing free-form surfaces commonly encountered in industrial design. In this work, we propose a new design exploration method that enables sketch-based editing of free-form surface geometries where specific modifications can be applied at different levels of detail. This multilevel detail approach allows the designer to work from existing models and make alterations at coarse and fine representations of the geometry, thereby providing increased conceptual flexibility during modeling. At the heart of our approach lies a multiscale representation of the geometry obtained through a spectral analysis on the discrete free-form surface. This representation is accompanied by a sketch-based surface editing algorithm that enables edits to be made at different levels. The seamless transfer of modifications across different levels of detail facilitates a fluid exploration of the geometry by eliminating the need for a manual specification of the shape hierarchy. We demonstrate our method with several design examples.

Generative product design systems used in the context of mass customization are required to generate diverse solutions quickly and reliably without necessitating modification or tuning during use. When such systems are employed to allow for the mass customization of product form, they must be able to handle mass production and engineering constraints that can be time-consuming to evaluate and difficult to fulfill. These issues are related to how the constraints are handled in the generative design system. This article evaluates two promising sequential constraint-handling techniques and the often used weighted sum technique with regard to convergence time, convergence rate, and diversity of the design solutions. The application used for this purpose was a design system aimed at generating a table with an advanced form: a Voronoi diagram based structure. The design problem was constrained in terms of production as well as stability, requiring a time-consuming finite element evaluation. Regarding convergence time and rate, one of the sequential constraint-handling techniques performed significantly better than the weighted sum technique. Nevertheless, the weighted sum technique presented respectable results and therefore remains a relevant technique. Regarding diversity, none of the techniques could generate diverse solutions in a single search run. In contrast, the solutions from different searches were always diverse. Solution diversity is thus gained at the cost of more runs, but no evaluation of the diversity of the solutions is needed. This result is important, because a diversity evaluation function would otherwise have to be developed for every new type of design. Efficient handling of complex constraints is an important step toward mass customization of nontrivial product forms.

This paper presents the development of an optimization methodology for selecting the lowest monetary cost combinations of building technologies to meet set operational energy reduction targets. The new optimization algorithm introduced in this paper departs from the notion that optimal design choices over a large set of design parameters and properties can be driven by energy targets. We assume that design parameters are determined by many concurrent considerations fighting over the attention span of the design team. Our approach starts from a design outcome and asks the question, which set of discrete technologies are the right mix to reach an energy target in the cost optimal way? Such an approach has to face the challenge that the properties of market-available building technologies have a discrete nature that makes their optimal selection a combinatorial problem. The optimization algorithm searches the discrete combinatoric space by maximizing the following objective function: calculated energy savings divided by premium cost, where cost is defined as the additional cost over a baseline solution. The algorithm is codified into a custom MATLAB script and when compared to prescriptive methodologies is shown to be more cost effective and generically applicable given a palette of building technology alternatives and their corresponding cost data.

The application of data analytics to product usage data has the potential to enhance engineering and decision-making in product planning. To achieve this effectively for cyber-physical systems (CPS), it is necessary to possess specialized expertise in technical products, innovation processes, and data analytics. An understanding of the process from domain knowledge to data analysis is of critical importance for the successful completion of projects, even for those without expertise in these areas. In this paper, we set out the foundation for a toolbox for data analytics, which will enable the creation of domain-specific pipelines for product planning. The toolbox includes a morphological box that covers the necessary pipeline components, based on a thorough analysis of literature and practitioner surveys. This comprehensive overview is unique. The toolbox based on it promises to support and enable domain experts and citizen data scientists, enhancing efficiency in product design, speeding up time to market, and shortening innovation cycles.

In this report the architecture of an intelligent design machine capable of performing routine design in different design domains is described. This machine is crafted to operate as a part of a larger system driven by a human designer. Notable features are its use of best-first search strategies for problem-solving control and its ability to adjust problem-solving control strategies, perform automated redesign following specification changes, and resolve constraint violations using domain knowledge. The claims made for this machine are analyzed and it is argued that these claims are founded on established principles of design for intelligent systems. An implementation of this architecture in a rule-based system named Proteus is discussed and its operation is examined using as an example the domain of register-transfer-level computer design.

Although current efforts have indicated the need for design verification and evaluation systems, little has been done to develop knowledge representation systems for implementing this class of expert systems. In this paper, we describe CANDLE, a knowledge representation system built to solve design verification and evaluation problems. We define the class of design verification and evaluation systems and the functionality required of knowledge representation systems used to develop these systems. We present an example design verification and evaluation problem, illustrating how this problem imposes these functionality requirements on the knowledge representation system used. In the remainder of the paper we discuss CANDLE, a knowledge representation system created to develop design verification and evaluation systems. We show that CANDLE meets the knowledge representation requirements for this class of systems. We also present some of CANDLE'S functionality that exceeds the defined knowledge representation requirements and provides a knowledge engineer with the capability to expand the functionality of a design verification and evaluation system.

The application of truth maintenance techniques to the component selection phase of the engineering design process is described. The aims of the research include the selection of components that most closely match a set of requirements which include fluid characteristics, economic constraints, and company policies or preferences. In addition, we consider the storage and retrieval of requirement sets and corresponding component selections. The benefits of maintaining temporal knowledge are twofold: to enhance performance through reuse when new requirement sets are similar to previous sets, and to re-assess selections in the light of changes over time, for example, to the set of available components, to company policy, or, perhaps, to national safety regulations.

We propose a temporal truth maintenance system (TTMS) to support this management of selection knowledge over time.

One of the biggest problems in pneumatic system design is over design. Thus, the results are excessive in costs of the initial investment and it requires too much energy. This article describes the development of expert system based pneumatic design system, PNEUDES (PNEUmatic Design Expert System), prototype that enables the user the optimal design of pneumatic system. Once the design requirements such as cylinder type and work load, etc. are input to the system, optimal cylinder specifications with standardized order-based size, valves types, and necessary accessories are all determined. Also the configuration information such as the connectivity among components and cylinder image data are supplied to the user. It can also help the novice of pneumatic design. The rule-based reasoning approach is used as a reasoning strategy with Intelligent Rule Element shell.

In order to realize the online allocation of collaborative processing resource of smart workshop in the context of cloud manufacturing, a multi-objective optimization model of workshop collaborative resources (MOM-WCR) was proposed. Considering the optimization objectives of processing time, processing cost, product qualification rate, and resource utilization, MOM-WCR was constructed. Based on the time sequence of workshop processing tasks, the workshop collaborative manufacturing resource was integrated in MOM-WCR. Fuzzy analytic hierarchy process (FAHP) was adopted to simplified the multi-objective problem into the single-objective problem. Then, the improved firefly algorithm which integrated the particle swarm algorithm (IFA-PSA) was used to solve MOM-WCR. Finally, a group of connecting rod processing experiments were used to verify the model proposed in this paper. The results show that the model is feasible in the application of workshop-level resource allocation in the context of cloud manufacturing, and the improved firefly algorithm shows good performance in solving the multi-objective resource allocation problem.

Learning engineering is a new subarea of knowledge engineering dealing with the methodological aspects of using learning systems in knowledge acquisition. In this paper, the justification for the development of Learning Engineering is provided, and its major subdomains and research directions are briefly discussed.

Metaphors are powerful tools for design, enabling designers to encapsulate sets of properties and relations as short verbal descriptions. This paper aims to clarify how simple spatial configurations may emerge from concise metaphoric descriptions at the conceptual design phase. To this aim, we propose a framework for a metaphor-based design process. As a basis for the framework, we introduce the concept of “complementary visual potential” – a property which ties the spatial configuration of the objects in the composition with their metaphoric roles. The framework is developed by studying the practice of metaphor-based spatial configuration design in Japanese rock gardens. Accordingly, it is implemented and tested in this context by attempting to generate alternative designs for an existing rock composition in the famous garden of Ryōan-ji. This is followed by a discussion of its possible implications and potential for generalization to other areas of design.