Product/service systems (PSSs) are increasingly found in markets, and more resources are being invested in PSS design. Despite the substantial research into PSS design, the current literature exhibits an incomplete understanding of it as a cognitive activity. This article demonstrates that the methods used to analyze product designers’ cognitive behavior can be used to produce comparable and commensurable results when analyzing PSS designers. It also generates empirical grounding for the development of hypotheses based on a cognitive study of a PSS design session in a laboratory environment using protocol analysis. This study is a part of a larger project comparing PSS design with product design. The results, which are based on the function–behavior–structure coding scheme, show that PSS design, when coded using this scheme, can be quantitatively compared with product design. Five hypotheses were developed based on the results of the study of this design session concerning where and how designers expend their cognitive design effort. These hypotheses can be used to design experiments that test them and provide the grounding for a fuller understanding of PSS design.

In configuration design parts are selected and connected to meet customer specifications and engineering and physical constraints. Specifications include preferences (e.g., “prefer lower cost to higher performance, all things being equal”), bounds on various resources (e.g., “the computer should have four PCI slots”), and other information to customize a configuration. Constraints typically arise from exogenous concerns, such as the available parts, the way parts can interact, and the manufacturing plant.

The Designers' Workbench is a system developed by the Advanced Knowledge Technologies Consortium to support designers in large organizations, such as Rolls-Royce, to ensure that the design is consistent with the specification for the particular design as well as with the company's design rule book(s). In the principal application discussed here, the evolving design is described using a jet engine ontology. Design rules are expressed as constraints over the domain ontology. Currently, to capture the constraint information, a domain expert (design engineer) has to work with a knowledge engineer to identify the constraints, and it is then the task of the knowledge engineer to encode these into the Workbench's knowledge base. This is an error-prone and time-consuming task. It is highly desirable to relieve the knowledge engineer of this task, so we have developed a system, ConEditor+, that enables domain experts themselves to capture and maintain these constraints. Further, we hypothesize that to appropriately apply, maintain, and reuse constraints, it is necessary to understand the underlying assumptions and context in which each constraint is applicable. We refer to them as “application conditions,” and these form a part of the rationale associated with the constraint. We propose a methodology to capture the application conditions associated with a constraint and demonstrate that an explicit representation (machine interpretable format) of application conditions (rationales) together with the corresponding constraints and the domain ontology can be used by a machine to support maintenance of constraints. Support for the maintenance of constraints includes detecting inconsistencies, subsumption, redundancy, fusion between constraints, and suggesting appropriate refinements. The proposed methodology provides immediate benefits to the designers, and hence, should encourage them to input the application conditions (rationales).

A new methodology for solving resource leveling problems is introduced using Artificial Neural Networks (ANN). This paper describes a new efficient and robust approach which is unique to those utilized by traditional heuristic and optimization resource leveling techniques. The Resource Leveling Artificial Neural Network (RLANN) exploits advantages of both Hopfield networks and competition-based artificial neural networks. The universal scheme of the RLANN is applicable to construction project networks produced with Critical Path Method (CPM), in forms of either arrow or precedence diagrams. The scheme is comprised of two layers, an input and a competition layer, of artificial node matrices fully connected by links. Solving mechanisms inside the RLANN are based on an equation of motion and a competition strategy that control the level of daily resource usage. While the equation of motion governs activities to be shifted within schedule constraints, the competition process finds the best positions for the activities to achieve optimum results. The approach is simple and can be implemented on either a personal computer or a parallel processing device. The solutions produced are comparable to, or better than, those generated by other heuristic or optimization techniques. This paper describes the development of the RLANN, its solving mechanisms, and its uses in construction resource leveling problems. The comparison of the result of the RLANN to those of other traditional techniques is also included. The conclusions highlight the applicability of this model to other civil engineering problems.

The goal of machine learning for artifact synthesis is the acquisition of the relationships among form, function, and behavior properties that can be used to determine more directly form attributes that satisfy design requirements. The proposed approach to synthesis knowledge acquisition and use (SKAU) described in this paper, called NETSYN, creates a function to estimate the probability of each possible value of each design property being used in a given design context. NETSYN uses a connectionist learning approach to acquire and represent this probability estimation function and exhibits good performance when tested on an artificial design problem. This paper presents the NETSYN approach for SKAU, a preliminary test of its capability, and a discussion of issues that need to be addressed in future work.

The expertise of designers consists, primarily, of information about the relationship between goals or performance criteria and the attributes of the desired artifact that will result in performances that will satisfy these criteria. The designer like experts in other fields is typically better at applying the knowledge that constitutes his expertise than he is at articulating this knowledge. Generation and simulation models are discussed as a means of generating a set of designs for which the set of attributes defining these designs and the performance of these designs in terms of the criteria considered are explicitly defined. Pareto optimization is discussed as a means of structuring these designs on the basis of their performance. The induction algorithm ID3 is used as a means of inferring general statements about the nature of solutions which exhibit Pareto optimal performance in terms of a set of performance criteria. The rules inferred in building design domain are compared with those extracted using a heuristic based learning system.

Recent knowledge-based expert systems for structural engineering design have focused on design-independent knowledge (abstract reasoning rules for designing), and while great strides have been made in that area, there is still a significant need to develop systems to take advantage of the wealth of knowledge contained in every substantial structural design. On the other hand, previous database-oriented design efforts have focused primarily on knowledge-poor databases of solutions, in which the traditional engineering handbook of solutions has simply been replaced by digital data. The challenge is to find a way to capture and apply the kind of case-based, design-dependent knowledge that structural engineers have traditionally used. The long-term results will be better structural designs and better structural designers. This paper discusses the character of the design-dependent knowledge in a structural engineering context, describes two initial applications of case-based reasoning to component design, and presents a general paradigm for a knowledge-based design system integrating rule-based and case-based reasoning.

The architecture and implementation of a mechanical designer's assistant shell called DEJAVU is presented. The architecture is based on an integration of design and CAD with some of the more well known concepts in case-based reasoning (CBR). DEJAVU provides a flexible and cognitively intuitive approach for acquiring and utilizing design knowledge. It is a domain independent mechanical design shell that can incrementally acquire design knowledge in the domain of the user. DEJAVU provides a design environment that can learn from the designer(s) until it can begin to perform design tasks autonomously or semi-autonomously. The main components of DEJAVU are a knowledge base of design plans, an evaluation module in the form of a design plan system, and a blackboard-based adaptation module. The existance of these components are derived from the utilization of a CBR architecture. DEJAVU is the first step in developing a robust designer's assistant shell for mechanical design problems. One of the major contributions of DEJAVU is the development of a clean architecture for the utilization of case-based reasoning in a mechanical designer's assistant shell. In addition, the components of the architecture have been developed, tailored or modified from a general CBR context into a more synergistic relationship with mechanical design.

A voxel-based shape representation when integrated with an evolutionary algorithm offers a number of potential advantages for shape optimization. Topology need not be predefined, geometric constraints are easily imposed and, with adequate resolution, any shape can be approximated to arbitrary accuracy. However, lack of boundary smoothness, length of chromosome, and inclusion of small holes in the final shape have been stated as problems with this representation. This paper describes two experiments performed in an attempt to address some of these problems. First, a design problem with only a small computational cost of evaluating candidate shapes was used as a testbed for designing genetic operators for this shape representation. Second, these operators were refined for a design problem using a more costly finite element evaluation. It was concluded that the voxel representation can, with careful design of genetic operators, be useful in shape optimization.

This paper describes a generic approach to guiding designers when making decisions during the early stages of design. The objective of the research is to enable designers to foresee unintended life-cycle consequences during mechanical component design. Engineering design is a process of evolving solutions to a design problem through the commitment of decisions. As a designer commits a new design decision, a more concrete design solution is generated. Decisions made can have intended and unintended consequences on the performance of the life phase activities that follow, such as manufacturing, assembly, and disposal. Many existing tools only consider the impact of the design solution on later life-cycle phases when the solution is almost complete. This makes changes expensive and difficult. This paper presents a novel approach to how consequences encountered in down stream life-cycle phases can be brought to the designer's attention early in generation of component form. For this purpose, a knowledge model has been derived from a phenomena model. The phenomena model describes how life-cycle consequences are generated during component synthesis. An insight into the representation of the resultant knowledge model is discussed through examples. The implementation of a prototype Knowledge Intensive CAD tool, entitled FORESEE, aimed at supporting life-oriented, feature-based component synthesis and exploration, is also described. The results of the evaluation of FORESEE with a range of designers show that by using the system designers are motivated to explore alternative design solutions and are able to make more informed design decisions. This highlights that the knowledge structure provides a base for extending feature-based component design to a ‘Design Synthesis for Multi-X’ approach.

This paper presents an ontological framework for situated design teams in which the team is both the subject and the object of designing. Team designing is modeled using the set of processes provided by the situated function–behavior–structure framework. This is a formal basis for understanding the drivers for change in the product to be designed and in the design team. We specifically focus on changes in a team's structure that emerge from interactions among individual team members and subteams.

Despite alefforts for a sustainable production system, many companies are still struggling to implement environmental aspects in their daily product development processes. Among the evaluation and improvement methods, life cycle assessment (LCA) is one of the most popular tools to achieve this goal. Up to date, LCA has been applied to many products, services, and industrial systems to evaluate their environmental impact aspects. However, there is a wide range of indicators available to be applied for LCA, and choosing an inappropriate indicator may lead the product designer to achieve wrong and weak results. Therefore, this paper proposes to overcome this difficulty by developing a method that can be used as a knowledge transfer to product designers and LCA practitioners in order to help them to make the most appropriate choice of LCA indicators. This method should have some characteristics, such as (a) to be adaptable to a given context and (b) to be dynamic, scalable, and easy to learn. The purpose of this paper is to present the Evaluation Method for Choosing Indicator (EMCI) developed to facilitate the learning process of LCA methods and to quickly select their most appropriate indicators. To validate the EMCI method, a case study on a French textile industry has been implemented. The focus was to evaluate how LCA indicators and methods were chosen to be integrated into the suitable eco-design LCA tool.

Knowledge-based configurators are supporting configuration tasks for complex products such as telecommunication systems, computers, or financial services. Product configurations have to fulfill the requirements articulated by the user and the constraints contained in the configuration knowledge base. If the user requirements are inconsistent with the constraints in the configuration knowledge base, users have to be supported in finding out a way from the no solution could be found dilemma. In this paper we introduce a new algorithm (PersDiag) that allows the determination of personalized diagnoses for inconsistent user requirements in knowledge-based configuration scenarios. We present the results of an empirical study that show the advantages of our approach in terms of prediction quality and efficiency.

We aim to examine the potential of using analogies in design education and to compare the roles of analogies in explaining versus inspiring in engineering design. We review existing research in analogical thinking, with a focus on scientific discourse and education. Then we explore the role of analogies in design education in making concepts more relatable by asking six participants in a graduate-level design course to generate analogies for course topics. We describe criteria developed to evaluate the analogies and present these evaluations. We then asked participants to perform divergent thinking tests, but we found no significant correlation between these and analogy scores. The participants were also asked to reflect on what constitutes an effective analog, describe their process of identifying analogies, and provide their definitions of analogies. We describe possible links between these comments and the ratings of their analogies. We then draw on results in using analogies in pedagogy to inform and reflect on obstacles we encountered in the use of analogies to inspire. Specifically, we related them to our experience with biomimetic or biologically inspired design, where we used a natural-language search approach to identify relevant analogies. Three aspects discussed are familiarity of source analogies, boundaries of parallels between source analogies and target concepts, and concreteness of source analogies. Finally, we discuss possible pedagogical benefits of eliciting analogies on course topics from students, namely, using the elicited analogies as tools for improved student engagement as well as more prompt instructor feedback.

Engineering design involves the evaluation and satisfaction of a wide variety of constraints. The ability to represent and process these constraints in a computer is important for the verification of the output produced by computer-aided design programs. Constraints need not only check designs but can also be used to derive design solution s that satisfy constraints. The paper discusses how to represent the dual nature of constraints so that design consistency is maintained as the design evolves.

Assumptions and rules of thumb are used frequently in design to propose initial solutions. We represent the logic behind the derivation of these assumptions as heuristic procedures and maintain the dependencies between these assumptions and their consequents as an aid to the management of design consistency. We also propose a simple scheme, involving the partitioning of the design modules, to effect design changes when constraint violations occur. An example from structural design illustrates the methodology.

Expert systems employing current methodologies suffer from two major problems: they are brittle and their development is time-consuming and tedious. Learning, the key to intelligent human behavior and expertise, has the potential of alleviating these difficulties. The paper reviews a number of machine learning techniques and provides a framework for their classification. The description of each technique is followed by an example taken from the domain of structural design. The applicability of machine learning techniques to expert systems is discussed, including some prototype applications and their shortcomings. Three promising research directions are outlined as a partial solution for the shortcomings.

An integrated design and manufacturing approach allows economic decisions to be made that reflect an entire system design as a whole. To achieve this objective, we have developed and utilized integrated cost and engineering models within a focused design perspective. A framework for the integrated design of an aircraft system with a combined performance and economic perspective is described in this article. This framework is based on the concept of Design Justification using a Design-for-Economics approach. We have developed a knowledge-based system that can be used to evaluate aircraft structural concept material and process selections. The framework consists of the knowledge-based system, integrated with numerical analysis tools including an aircraft performance/sizing code and a life-cycle cost analysis code. Production cost estimates are applied for evaluation of process trades at the subcomponent level of design. Life-cycle cost estimates are used for evaluation of process trades at the system level. Results of a case study are presented for several advanced wing structural concepts for a future supersonic commercial transport aircraft. Cost versus performance studies indicate that a high-speed civil transport aircraft with a hybrid wing structural concept, though more expensive to manufacture than some homogeneous concepts, can have lower direct operating costs due to a lower take-off gross weight and less mission fuel required.

The shape grammar formalism has been discussed theoretically extensively. Recently there has been increased activity in implementing shape grammar interpreters, yet there is a lack of implementations that support parametric rules and emergence. Here the structure of a general parametric shape grammar interpreter is discussed in detail. The interpreter is based on graph grammars. It supports emergence, parametric rules, and numerous types of geometric objects. The shape grammar engine, an agent-based rule selection system and several implementations based on the engine are discussed.

The use of modularity in the design of a new product or the adoption of a product platform, as the base to define new solutions within a product family, offers the company a chance to meet diverse customer needs at low cost because of economies of scale in all phases of the product's life cycle. At present, the concept of modularity in product design is becoming widely used in many industries such as automobiles and consumer electronics. However, if modularity and mass customization have attracted the interest of industries and researchers, the greatest efforts have been focused on the theoretical aspect whereas the related design support technologies have been only partially implemented. In this context, our intent is to develop highly reusable models, which are able to reconfigure themselves on the basis of new functional requirements. The proposed approach is based on the definition of what we call self-configuring components and multiple-level functions. To describe the approach, a practical example related to the design of modules for woodworking machines is reported.

When designing a product, the earlier the potential risks can be identified, the more costs can be saved, as it is easier to modify a design in its early stages. Several methods exist to analyze the risk in a system, but all require a mature design. However, by applying the concept of “common interfaces” to a functional model and utilizing a historical knowledge base, it is possible to analyze chains of failures during the conceptual phase of product design. This paper presents a method based on these common interfaces to be used in conjunction with other methods such as risk in early design to allow a more complete risk analysis during the conceptual design phase. Finally, application of this method is demonstrated in a design setting by applying it to a thermal control subsystem.