This paper proposes an aesthetic-driven evolutionary algorithm for user-centered design. The evolutionary algorithm is based on a genetic algorithm (GA). It is developed to work as an art form generator that enhances user's productivity and creativity through reproduction, evaluation, and selection. Users can input their preferences and guide the generating direction to the system. A two-step fitness function is developed to evaluate morphology and aesthetics of the generated art forms. Fractals created by an iterated function system are used for representing art forms in our process. Algorithmic aesthetics are developed based on the aesthetic measure theory, surveys of human preferences, and popular long-lasting symbols. The algorithmic aesthetics is used for evaluating aesthetics of art forms together with subjective nonquantifiable aspects, and placed in the fitness function. The GA basically creates two-dimensional art forms. However, any two-dimensional image can be included through the property of a condensation set of fractals. The proposed GA can increase design productivity by about 80%. Examples of jewelry designs and physical prototypes created by the proposed system are included.

This paper presents an approach to optimal design of elastic flywheels using an Injection Island Genetic Algorithm (iiGA), summarizing a sequence of results reported in earlier publications. An iiGA in combination with a structural finite element code is used to search for shape variations and material placement to optimize the Specific Energy Density (SED, rotational energy per unit weight) of elastic flywheels while controlling the failure angular velocity. iiGAs seek solutions simultaneously at different levels of refinement of the problem representation (and correspondingly different definitions of the fitness function) in separate subpopulations (islands). Solutions are sought first at low levels of refinement with an axi-symmetric plane stress finite element code for high-speed exploration of the coarse design space. Next, individuals are injected into populations with a higher level of resolution that use an axi-symmetric three-dimensional finite element code to “fine-tune” the structures. A greatly simplified design space (containing two million possible solutions) was enumerated for comparison with various approaches that include: simple GAs, threshold accepting (TA), iiGAs and hybrid iiGAs. For all approaches compared for this simplified problem, all variations of the iiGA were found to be the most efficient. This paper will summarize results obtained studying a constrained optimization problem with a huge design space approached with parallel GAs that had various topological structures and several different types of iiGA, to compare efficiency. For this problem, all variations of the iiGA were found to be extremely efficient in terms of computational time required to final solution of similar fitness when compared to the parallel GAs.

This paper describes the results of a protocol study exploring problem–solution coevolution in a parametric design environment (PDE). The study involved eight participants who completed a defined architectural design task using Rhino and Grasshopper software: a typical PDE. The method of protocol analysis was employed to study the cognitive behaviors that occurred while these designers were working in the PDE. By analyzing the way in which the designers shifted between “problem” and “solution” spaces in the PDE, characteristics of the coevolutionary design process are identified and discussed. Results of this research include two potentially significant observations. First, the coevolution process occurs frequently within the design knowledge level (i.e., when using Rhino) and within the rule algorithm level (i.e., when using Grasshopper) of the parametric design process. Second, the designers’ coevolution process was focused on the design knowledge level at the beginning of the design session, while they focused more on the rule algorithm level toward the end of the design session. These results support an improved understanding of the design process that occurs in PDEs.

Performance assessment at early stages of buildings design is complicated by an inherent lack of information on the design and the uncertainty in how a building design may evolve to a final design. This pilot study reports on an initial quantification of such uncertainty associated with building energy performance and develops a method for informing decision makers of the risks in early design decisions under this uncertainty. Two case studies of building design decision situations under this uncertainty are explored along with using two different energy modeling tools: a reduced-order model and a high-order model. The intended contribution is to identify if a decision can be made with confidence in early design given a high level of uncertainty in the evolution of a design and what models can support decisions of this sort. Integration of the proposed decision support approach with a computer-aided design model is shown as well.

Feature technology is considered an essential tool for integrating design and manufacturing. Automatic feature recognition (AFR) has provided the greatest contribution to fully automated computer-aided process planning system development. The objective of this paper is to review approaches based on application of artificial neural networks for solving major AFR problems. The analysis presented in this paper shows which approaches are suitable for different individual applications and how far away we are from the formation of a general AFR algorithm.

Understanding product functions is a key aspect of the work undertaken by engineers involved in complex system design. The support offered to these engineers by existing modeling tools such as the function tree and the function structure is limited because they are not intuitive and do not scale well to deal with real-world engineering problems. A research collaboration between two universities and a major power system company in the aerospace domain has allowed the authors to further develop a method for function analysis known as function analysis diagram that was already in use by line engineers. The capability to generate and edit these diagrams was implemented in the Decision Rationale editor, a software tool for capturing design rationale. This article presents the intended benefits of the method and justifies them using an engineering case study. The results of the research have shown that the function analysis diagram method has a simple notation, permits the modeling of product functions together with structure, allows the generation of rich and accurate descriptions of product functionality, is useful to work with variant and adaptive design tasks, and can coexist with other functional modeling methods.

This paper develops a general framework for knowledge-based computer tools that promote simultaneous engineering in mechanical design. Design compatibility analysis (DCA) serves as the underlying concept for these knowledge-based systems. DCA focuses on the compatibility between the design requirements (specification) and the proposed design, evaluates the design based on the compatibility knowledge of experts, gives justifications for the evaluation, and suggest improvements. DCA accommodates a product's various life-cycle issues (e.g. functionality, manufacturability, reliability) with a unified focus, i.e. compatibility, and thus helps designers to incorporate these issues at the early stages of design (simultaneous engineering). The resulting framework not only serve as the basis for various design expert systems but will also enhance our understanding of the life-cycle design issues. We illustrate the proposed method with two examples: system design of power generation plants and design for assembly (DFA) of mechanical products.

Solid models are the critical data elements in modern computer-aided design environments, because they describe the shape and form of manufactured artifacts. Their growing ubiquity has created new problems in how to effectively manage the many models that are now stored in the digital libraries for large design and manufacturing enterprises. Existing techniques from the engineering literature and industrial practice, such as group technology, rely on human-supervised encodings and classification; techniques from the multimedia database and computer graphics/vision communities often ignore the manufacturing attributes that are most significant in the classification of models. This paper presents our approach to comparing the manufacturing similarity assessments of solid models of mechanical parts based on machining features. Our technical approach is threefold: perform machining feature extraction, construct a model dependency graph (MDG) from the set of machining features, and partition the models in a database using a measure of similarity based on the MDGs. We introduce two heuristic search techniques for comparing MDGs and present empirical experiments to validate our approach using our testbed, the National Design Repository.

The application of Bayesian networks for monitoring and diagnosis of a multistage manufacturing process is described. Bayesian network “part models” were designed to represent individual parts in-process. These were combined to form a “process model,” a Bayesian network model of the entire manufacturing process. An efficient procedure is designed for managing the “process network.” Simulated data is used to test the validity of diagnosis made from this method. In addition, a critical analysis of this method is given, including computation speed concerns, accuracy of results, and ease of implementation. Finally, a discussion on future research in the area is given.

The biological domain has the potential to offer a rich source of analogies to solve engineering design problems. However, due to the complexity embedded in biological systems, adding to the lack of structured, detailed, and searchable knowledge bases, engineering designers find it hard to access the knowledge in the biological domain, which therefore poses challenges in understanding the biological concepts in order to apply these concepts to engineering design problems. In order to assist the engineering designers in problem-solving, we report, in this paper, a web-based tool called Idea-Inspire 4.0 that supports analogical design using two broad features. First, the tool provides access to a number of biological systems using a searchable knowledge base. Second, it explains each one of these biological systems using a multi-modal representation: that is, using function decomposition model, text, function model, image, video, and audio. In this paper, we report two experiments that test how well the multi-modal representation in Idea-Inspire 4.0 supports understanding and application of biological concepts in engineering design problems. In one experiment, we use Bloom's method to test “analysis” and “synthesis” levels of understanding of a biological system. In the next experiment, we provide an engineering design problem along with a biological-analogous system and examine the novelty and requirement-satisfaction (two major indicators of creativity) of resulting design solutions. In both the experiments, the biological system (analogue) was provided using Idea-Inspire 4.0 as well as using a conventional text-image representation so that the efficacy of Idea-Inspire 4.0 is tested using a benchmark.

Mass customization has been identified as a competitive strategy by an increasing number of companies. Family-based product design is an efficient and effective means to realize sufficient product variety, while satisfying a range of customer demands in support for mass customization. This paper presents a knowledge decision support approach to product family design evaluation and selection for mass customization process. Here, product family design is viewed as a selection problem with the following stages: product family (design alternatives) generation, product family design evaluation, and selection for customization. The fundamental issues underlying product family design for mass customization are discussed. Then, a knowledge support framework and its relevant technologies are developed for module-based product family design for mass customization. A systematic fuzzy clustering and ranking model is proposed and discussed in detail. This model supports the imprecision inherent in decision making with fuzzy customers' preference relations and uses fuzzy analysis techniques for evaluation and selection. A neural network technique is also adopted to adjust the membership function to enhance the model. The focus of this paper is on the development of a knowledge-intensive support scheme and a comprehensive systematic fuzzy clustering and ranking methodology for product family design evaluation and selection. A case study and the scenario of knowledge support for power supply family evaluation, selection, and customization are provided for illustration.

Configurators have been generally accepted as important tools to elicit customers' needs and find the matches between customers' requirements and company's offerings. With product configurators, product design is reduced to a series of selections of attribute values. However, it has been acknowledged that customers are not patient enough to configure a long list of attributes. Therefore, making every round of configuring process productive and hence reducing the number of inputs from customers are of substantial interest to academic and industry alike. In this paper, we present an efficient product configuration approach by incorporating Shapley value, which is a concept used in game theory, to estimate the usefulness of each attribute in the configurator design. This new method iteratively selects the most relevant attribute that can contribute most in terms of information content from the remaining pool of unspecified attributes. As a result from product providers' perspective, each round of configuration can best narrow down the choices with given amount of time. The selection of the next round query is based on the customer's decision on the previous rounds. The interactive process thus runs in an adaptive manner that different customers will have different query sequences. The probability ranking principle is also exploited to give product recommendation to truncate the configuration process so that customers will not be burdened with trivial selection of attributes. Analytical results and numerical examples are also used to exemplify and demonstrate the viability of the method.

A grammatical approach to product design is demonstrated. In particular, shape grammars are shown to be especially useful for products that are differentiated primarily on the basis of form yet driven by function; they allow products to be designed as a sequence of well-defined steps. However, it is not always clear how to choose the sequence of rules that should be applied to generate the final shape. In this paper we demonstrate that at each stage during the process, partial designs of the final product can be used to provide feedback to the designer based on specific design objectives and thus suggest possible rule choices. We take advantage of the shape grammar for the generation of coffeemakers introduced by Agarwal and Cagan, and associate with the grammar rules expressions that model manufacturing costs. With each application of a shape grammar rule, an understanding of the overall cost of manufacturing the product is incrementally improved. Thus, at each stage of the design process the designer has an indication of what the overall cost of the product will be and how the selection of one grammar rule over another influences the final cost. Once the complete product is generated, an appraisal of its manufacturing cost is given to the designer. This evaluation methodology helps the designer understand the implications of decisions made early on in the design process. We have also verified the accuracy of this approach through the costs of some commercially available coffeemakers, generated by this method, which are comparable to the costs for those designs listed in the literature.

This paper describes and evaluates approaches to model and solve technical product configuration problems using different artificial intelligence methodologies. By means of a typical example, the benefits and limitations of different artificial intelligence methods are discussed and a flexible software architecture for integrating different solvers in a product configurator is proposed.

Collaborative engineering design involves various stakeholders with different perspectives. The design process is relatively complex and difficult to handle. Various conflicts always happen among the design tasks and affect the design team performance. Therefore, to represent the collaborative design process and capture the evolution of design perspectives in a structured way, it is critical to manage the design conflicts and improve the collaborative design productivity. This article provides a generic collaborative design process model based on a sociotechnical design framework. This model has a topological format and adopts process analysis techniques from Petri Nets. By addressing both the technical and social aspects of collaborative design activities, it provides a mechanism to identify the interdependencies among design tasks and perspectives of different stakeholders. Based on this design process model, a methodology of detecting and handling the design conflicts is developed to support collaborative design coordination.

The status of research methodology employed by studies on the application of AI techniques to solving problems in engineering design, analysis, and manufacturing is poor. There may be many reasons for this status, including: unfortunate heritage from AI, poor educational system, and researchers’ sloppiness. Understanding this status is a prerequisite for improvement. The study of research methodology can promote such understanding, but, most importantly, it can assist in improving the situation. Concepts from the philosophy of science are introduced, and models of world views of science are built on them. These world views are combined with research heuristics or research perspectives and criteria for evaluating research to create a layered model of research methodology. This layered model can serve to organize and facilitate a better understanding of future studies of research methodologies. Many of the issues involved in the study of AI and AIEDAM research methodology using this layered model are discussed.

Function logic methods have been successfully used in Value Analysis (VA) and Value Engineering (VE) for several decades. This functional approach attempts to provide a common language for specialists in multiple domains. This paper describes an extension of function logic that assists in systematic identification of design functions, allocations, and their interrelations. Our approach identifies a three-level function/allocation/component information structure to represent the state of the design. We illustrate new types of links that exist between functions and the effect of these on the representation of the interrelated functions. These linkages provide new pathways for design information and function evaluation through allocation arithmetic and supported functions. A computational model of the conceptual design process is proposed based on the extended function logic design representation. An outline of the inputs, outputs and operations on form and function variables is given as a step prior to the synthesis process. We illustrate, by example, the process of translating functional representations across specialist domains. Finally, a computer-based aid to developing functional models is described.

Large-scale engineering projects typically involve many different types of professionals who must interact and communicate with one another. This interaction produces conflicts that need to be resolved. A framework is presented in which the rationale used in a collaborative design environment for designing an artifact is also used for conflict mitigation. The framework contains mechanisms for checking interactions and prompting hypotheses about the reasons for the interactions. These hypotheses, once verified by the designers, improve conflict resolution by assisting them in coordinating and negotiating conflicts. This, in turn, enhances communication during the design process and consequently increases productivity in the engineering industry.

The systems engineering V (SE-V) is an established process model to guide the development of complex engineering projects (INCOSE, 2011). The SE-V process involves decomposition and integration of system elements through a sequence of tasks that produce both a system design and its testing specifications, followed by successive levels of build, integration, and test activities. This paper presents a method to improve SE-V implementation by mapping multilevel data into design structure matrix (DSM) models. DSM is a representation methodology for identifying interactions between either components or tasks associated with a complex engineering project (Eppinger & Browning, 2012). Multilevel refers to SE-V data on complex interactions that are germane either at multiple levels of analysis (e.g., component versus subsystem) conducted either within a single phase or across multiple time phases (e.g., early or late in the SE-V process). This method extends conventional DSM representation schema by incorporating multilevel test coverage data as vectors into the off-diagonal cells. These vectors provide a richer description of potential interactions between product architecture and SE-V integration test tasks than conventional domain mapping matrices. We illustrate this method with data from a complex engineering project in the offshore oil industry. Data analysis identifies potential for unanticipated outcomes based on incomplete coverage of SE-V interactions during integration tests. In addition, assessment of multilevel features using maximum and minimum function queries isolates all the interfaces that are associated with either early or late revelations of integration risks based on the planned suite of SE-V integration tests.