A case-based design functionality is a natural and intuitive addition to a design tool that can augment human capabilities and help designers remember and retrieve appropriate cases. SEED-Config, a design environment for conceptual building design, was developed to incorporate a case-based reasoning functionality to provide designers with initial potential solutions. The case representation in SEED-Config is the BENT information model, which records design knowledge, supports the hierarchical decomposition of design cases, offers multiple views, and encapsulates the outcome of the design in addition to the problem specification and the design solution. The case library was implemented in an object-oriented database management system to accumulate cases automatically and to provide efficient query facilities. The case retrieval aspect of SEED-Config offers three different methods to find the most useful cases stored in the case library: task-based, lineage-based, and customized. Case retrieval responds to the exploratory nature of the design process and supports versatile case retrieval by providing multiple paths to each case. The case adaptation aspect, which adjusts the selected case to the new problem to provide a complete solution, uses an adaptation method called derivational replay. The case-based design capabilities are completely integrated within the design environment from which the cases originate.

In this article, two methods to develop and optimize accompanying building spatial and structural designs are compared. The first, a coevolutionary method, applies deterministic procedures, inspired by realistic design processes, to cyclically add a suitable structural design to the input of a spatial design, evaluate and improve the structural design via the finite element method and topology optimization, adjust the spatial design according to the improved structural design, and modify the spatial design such that the initial spatial requirements are fulfilled. The second method uses a genetic algorithm that works on a population of accompanying building spatial and structural designs, using the finite element method for evaluation. If specific performance indicators and spatial requirements are used (i.e., total strain energy, spatial volume, and number of spaces), both methods provide optimized building designs; however, the coevolutionary method yields even better designs in a faster and more direct manner, whereas the genetic algorithm based method provides more design variants. Both methods show that collaborative design, for example, via design modification in one domain (here spatial) to optimize the design in another domain (here structural) can be as effective as monodisciplinary optimization; however, it may need adjustments to avoid the designs becoming progressively unrealistic. Designers are informed of the merits and disadvantages of design process simulation and design instance exploration, whereas scientists learn from a first fully operational and automated method for design process simulation, which is verified with a genetic algorithm and subject to future improvements and extensions in the community.

The problem addressed in this paper is that design decisions can have a propagation effect spanning multiple life-phases influencing life-cycle metrics such as cost, time, and quality. It introduces a computational framework of a “Knowledge of life-cycle Consequences (KC) approach” aimed at allowing designers to foresee and explore effectively unintended, solution specific life-cycle consequences (LCCs) during solution synthesis. The paper presents a phenomena model describing how LCCs are generated from two fundamentally different conditions: noninteracting and interacting synthesis decision commitments. Based on this understanding, the KC approach framework has been developed and implemented as a Knowledge-Intensive CAD (KICAD) tool named FORESEE. The framework consists of three frames: an artefact life modelling frame, an operational frame, and an LCC knowledge modelling frame. This paper focuses on the knowledge modelling frame, composed basically of synthesis elements, consequence inference knowledge, and consequence action knowledge. To evaluate the influence of design decision consequences on artefact life-phases, cost, time and quality performance measures are used within the frame. Using these metrics, the life-cycle implications of a decision can be instantly updated and fully appreciated. An evaluation of the approach was carried out by applying FORESEE to thermoplastic component design. The results provide a degree of evidence that the approach integrates the activity of component design synthesis with the activity of foreseeing artefact life issues including fluctuations in life-cycle metrics. This makes the approach fundamentally different from the conventional approach in which first a candidate design solution is generated and then, at a penalty of extra time, an analysis of the solution for conflicts with artefact life issues is carried out. The framework thus provides a significant step towards the realization of a “Design Synthesis for Multi-X” approach to component design, although further work is required to exploit practically its utilization.

The study of languages of shape is rich and interesting. One can develop formal grammars whose languages are non-realizable shape (nonsense objects), as well as grammars whose languages are classes of realizable shape. This paper develops several graph-based multi-dimensional languages that are complete in the physical solids. Thus, realizable shape is a language.

Stylistic composition is a creative musical activity, in which students as well as renowned composers write according to the style of another composer or period. We describe and evaluate two computational models of stylistic composition, called Racchman-Oct2010 (random constrained chain of Markovian nodes, October 2010) and Racchmaninof-Oct2010 (Racchman with inheritance of form). The former is a constrained Markov model, and the latter embeds this model in an analogy-based design system. Racchmaninof-Oct2010 applies a pattern discovery algorithm called SIACT and a perceptually validated formula for rating pattern importance, to guide the generation of a new target design from an existing source design. A listening study is reported concerning human judgments of music excerpts that are, to varying degrees, in the style of mazurkas by Frédéric Chopin (1810–1849). The listening study acts as an evaluation of the two computational models and a third, benchmark system, called Experiments in Musical Intelligence. Judges' responses indicate that some aspects of musical style, such as phrasing and rhythm, are being modeled effectively by our algorithms. Judgments are also used to identify areas for future improvements. We discuss the broader implications of this work for the fields of engineering and design, where there is potential to make use of our models of hierarchical repetitive structure.

Customers can directly express their preferences on many options when ordering products today. Mass customization manufacturing thus has emerged as a new trend for its aiming to satisfy the needs of individual customers. This process of offering a wide product variety often induces an exponential growth in the volume of information and redundancy for data storage. Thus, a technique for managing product configuration is necessary, on the one hand, to provide customers faster configured and lower priced products, and on the other hand, to translate customers' needs into the product information needed for tendering and manufacturing. This paper presents a decision-making scheme through constructing a product family model (PFM) first, in which the relationship between product, modules, and components are defined. The PFM is then transformed into a product configuration network. A product configuration problem assuming that customers would like to have a minimum-cost and customized product can be easily solved by finding the shortest path in the corresponding product configuration network. Genetic algorithms (GAs), mathematical programming, and tree-searching methods such as uniform-cost search and iterative deepening A* are applied to obtain solutions to this problem. An empirical case is studied in this work as an example. Computational results show that the solution quality of GAs retains 93.89% for a complicated configuration problem. However, the running time of GAs outperforms the running time of other methods with a minimum speed factor of 25. This feature is very useful for a real-time system.

Communication is both the problem and the solution to misunderstanding. It is the human communicative ability to display understanding to resolve misunderstandings that plays an important part in the organization of the design inputs to a construction project. Ambiguity and uncertainty, as different forms of misunderstanding, are studied in this article, as they are manifest in the conversation at a design meeting. In this setting the coordination of both in situ design activities and the planning of design tasks takes place in real time, in conversation. Exhibited are several ways that design ambiguities and uncertainties can be seen in the interactional details of a multidisciplinary design team's conversation, to then report on how different design expertise featured in the raising of, and attempts at resolving, the misunderstandings that arose. In the course of this meeting, ambiguity and uncertainty were observed not as neat, discrete phenomena but were interwoven in the conversation. This characteristic poses difficulties in the disambiguation of the problem-solving response to each form of misunderstanding and further develops our understanding of design as it is communicated and conducted in social interaction. Finally, some implications from this study are put forward to inform the design of support for collaborative design.

One of the aspects of mass customization is to provide customers with products that are manufactured to their needs and requirements. To provide such support requires better integration of the customer into different stages of design and manufacturing. Expansion of the Internet provides an opportunity for such an integration, which will need to link design and manufacturing of the company with the customer. In current approaches, customers usually specify the options and get the price or simple pictures of the object. In this paper we present a framework in which customer options and size parameters are gathered using the Internet. It is used to automatically generate a 3-dimensional computer-aided design model of the product, estimate the price of the product, and generate assembly sequence information. The framework for mass customization of products necessitates information management among different segments of the company and the customer. The Internet-based system presented in this paper uses a graph grammar and templates to explicitly maintain correspondence among various types of product information from a module perspective. The system is demonstrated using a customizable coffeemaker product family.

A new method is proposed for efficient optimization under uncertainty that addresses the curse of dimensionality as it pertains to the evaluation of probabilistic objectives and constraints. A basis adaptation strategy previously introduced by the authors is integrated into a design optimization framework that construes the optimization cost function as the quantity of interest and computes stochastic adapted bases as functions of design space parameters. With these adapted bases, the stochastic integrations at each design point are evaluated as low-dimensional integrals (mostly one dimensional). The proposed approach is demonstrated on a well-placement problem where the uncertainty is in the form of a stochastic process describing the permeability of the subsurface. An analysis of the method is carried out to better understand the effect of design parameters on the smoothness of the adaptation isometry.

Tangible interaction is an emerging field of human–computer interaction that links the digital and the physical worlds by embedding computation in physical artifacts and environments. This paper shares our experience teaching tangible interaction over the past 4 years in an interdisciplinary, project-based laboratory course at Tufts University. Although the course is offered through the Computer Science Department, it reflects the multidisciplinary nature of the field, merging product engineering practices with a design studio approach. With a diverse mix of students, this approach has fostered creativity and hands-on learning. Throughout the course students have created innovative interfaces that not only capture fundamental concepts of tangible interaction but also contribute novel techniques for supporting collaborative design. We discuss examples of student-created interfaces and illustrate the relationship between the methods employed in the course and the artifacts created. We also share our recommendations for implementing such a course in institutions with constraints similar to ours including a limited budget and minimal laboratory space.

Direct planning of assembly sequences is either very complicated computationally, or requires an ‘expert’ user. This paper presents a new approach to the automatic generation of assembly plans which involves two phases. An ‘exploded’ layout of the assembled product is first found from its topological and geometric description by a graph-based procedure. Then, a knowledge-based technique is used for planning of selected assembly sequences by utilizing theoretical as well as heuristic knowledge of mechanical components and assembly processes. Besides serving the assembly planning phase, automatic generation of exploded-views could also be a most desirable feature of any computer-aided design system. An example demonstrates the application of the method to an actual product.

This paper introduces new research that seeks to develop low-cost, high quality, mass customizable building assembly systems that provide visually rich design variations for housing or other small structures. The building systems are intended to be tailored for particular cultures and communities by incorporating vernacular decorative design into the assembly design. Two complementary research areas are brought together in this work: shape grammars and digital fabrication. The visual, aesthetic aspects of the research are explored through shape grammars. The physical design and manufacturing aspects are explored through advanced digital design and fabrication technologies, and in particular, build on recent work on monomaterial assemblies with interlocking components that can be fabricated with computer numerical control machines and assembled easily by hand. The long-term objective of this research is the development of formal, visual–physical grammars with rules that generate complete computer-assisted design/computer-assisted manufacturing data for fabrication of full-scale components for assembly design variations. This paper reports on the first phase of this research: pilot studies for prototype assembly systems that incorporate vernacular languages from different parts of the world. The results of these studies are very promising, and demonstrate a spectrum of strategies for embedding visual properties in structural systems. Important next steps in this research are outlined. If successful, this work will lead to new solutions for low-cost, easily manufactured housing, which is especially critical in developing countries and for postdisaster environments. These new housing solutions will not only provide shelter but also support important cultural values through the integration of familiar visual design features. Beyond the specific context of housing and building assemblies, the research has the potential to impact the design and manufacture of designed artifacts on many scales and in many domains, especially in domains where visual aesthetics need to be considered jointly with physical, structural, or material requirements, and where design customization and variation is important.

At its root level, style is actually an esthetic agreement between people. The question is, how can esthetic agreements be modeled and measured in artificial intelligence? This paper offers a formal theory called EVE′ and applies it to a novel test bed of dynamic drawings that combine features of music and sketching. The theory provides mathematical measures of expectations, violations, and explanations, which are argued to be the atomic components of the esthetic experience. The approach employs Bayesian methods to extend information measures proposed in other research. In particular, it is shown that information theory is useful at an entropic level to measure expectations (E) of signals and violations (V) of expectations, but that Bayesian theory is needed at a semantic level to measure explanations (E′) of meaning for the signals. The entropic and semantic measures are then combined in further measures of tension and pleasure at an esthetic level that is actually style.

We demonstrate the use of genetic programming in the automatic invention of quantum computing circuits that solve problems of potential theoretical and practical significance. We outline a developmental genetic programming scheme for such applications; in this scheme the evolved programs, when executed, build quantum circuits and the resulting quantum circuits are then tested for “fitness” using a quantum computer simulator. Using the PushGP genetic programming system and the QGAME quantum computer simulator we demonstrate the invention of a new, better than classical quantum circuit for the two-oracle AND/OR problem.

Many tools are being developed to assist designers in retrieving analogies. One critical question these designers face is how these analogues should be represented in order to minimize design fixation and maximize idea generation. To address this question, an experiment is presented that compares various representations' influence on creativity and design fixation. This experiment presents an effective example (analogue) as computer-aided design (CAD), sketch, or photograph representations. We found that all representations induced fixation, and the degree of fixation did not vary significantly. We also found that CAD representations encourage engineering designers to identify and copy the key effective features of the example. CAD and photo representations also produced a higher quality of design concepts. Results from this experiment offer insights into how these various representations may be used in examples during idea generation; CAD representations appear to offer the greatest advantages during the idea generation process. The results from this experiment also indicate that analogical databases of effective design examples should include CAD and photolike images of the analogue rather than sketches.

This paper proposes an intelligent model-based optimization methodology for optimizing the production cost and material removal rate subjected to surface quality constraint in turning operation of hardened AISI D2. Unlike traditional approaches, this paper deals with finding optimum cutting parameters considering the real condition of the cutting tool. Tool flank wear is predicted by the model obtained using genetic programming. On the basis of the predicted flank wear value, the surface roughness of work piece is estimated by neural networks. Applying the particle swarm optimization algorithm, the optimum machining parameters are determined. The simulation and experimental results show that machining with proposed intelligent optimization methodology has higher efficiency than conventional techniques with constant optimized cutting parameters.

Earlier research on product family design (PFD) often highlights isolated and successful empirical studies with a limited attempt to explore the modeling and design support issues surrounding this economically important class of engineering design problems. This paper proposes a graph rewriting system to organize product family data according to the underpinning logic and to model product derivation mechanisms for PFD. It represents the structural and behavioral aspects of product families as family graphs and related graph operations, respectively. The derivation of product variants becomes a graph rewriting process, in which family graphs are transformed to variant graphs by applying appropriate graph rewriting rules. The system is developed in the language of programmed graph rewriting systems or PROGRES, which supports the specification of hierarchical graph schema and parametric rewriting rules. A meta model is defined for family graphs to factor out those entities common to all product families. A generic model is defined to describe all specific entities relevant to particular families. An instance model describes all product variants for individual customer orders. A prototype of a graph-based PFD system for office chairs is also developed. The system can provide an interactive environment for customers to make choices among product offerings. It also facilitates design automation of product families and enhances interactions and negotiations among sales, design, and manufacturing.

Engineering conceptual design can be defined as that phase of the product development process during which the designer takes a specification for a product to be designed and generates many broad solutions to it. This paper presents a constraint-based approach to supporting interactive conceptual design. The approach is based on an expressive and general technique for modeling: the design knowledge that a designer can exploit during a design project; the life-cycle environment that the final product faces; the design specification that defines the set of requirements the product must satisfy; and the structure of the various schemes that are developed by the designer. A computational reasoning environment based on constraint filtering is proposed as the basis of an interactive design support tool. Using such a tool, human designers can be assisted in interactively developing and evaluating a set of schemes that satisfy the various constraints imposed on the design.

Optimized lightweight manufacturing of parts is crucial for automotive and aeronautical industries in order to stay competitive and to reduce costs and fuel consumption. Hence, aluminum becomes an unquestionable material choice regarding these challenges. Nevertheless, using only virgin aluminum is not satisfactory because its extraction requires high use of energy and effort, and its manufacturing has high environmental impact. For these reasons, the use of recycled aluminum alloys is recommended considering their properties meet the expected technical and environmental added values. This requires complete reengineering of the classical life cycle of aluminum-based products and the collaboration practices in the global supply chain. The results from several interdependent disciplines all need to be taken into account for a global product/process optimization. Toward achieving this, a method for sustainability assessment integration into product life cycle management and a platform for life cycle simulation integrating environmental concerns are proposed in this paper. The platform may be used as a decision support system in the early product design phase by simulating the life cycle of a product (from material selection to production and recycling phases) and calculating its impact on the environment.

Complex engineered systems design is a collaborative activity. To design a system, experts from the relevant disciplines must work together to create the best overall system from their individual components. This situation is analogous to a multiagent system in which agents solve individual parts of a larger problem in a coordinated way. Current multiagent models of design teams, however, do not capture this distributed aspect of design teams – instead either representing designers as agents which control all variables, measuring organizational outcomes instead of design outcomes, or representing different aspects of distributed design, such as negotiation. This paper presents a new model which captures the distributed nature of complex systems design by decomposing the ability to control design variables to individual computational designers acting on a problem with shared constraints. These designers are represented as a multiagent learning system which is shown to perform similarly to a centralized optimization algorithm on the same domain. When used as a model, this multiagent system is shown to perform better when the level of designer exploration is not decayed but is instead controlled based on the increase of design knowledge, suggesting that designers in multidisciplinary teams should not simply reduce the scope of design exploration over time, but should adapt based on changes in their collective knowledge of the design space. This multiagent system is further shown to produce better-performing designs when computational designers design collaboratively as opposed to independently, confirming the importance of collaboration in complex systems design.