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.

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.

This paper presents an approach to the automatic generation of electromechanical engineering designs. We apply messy genetic algorithm (GA) optimization techniques to the evolution of assemblies composed of LegoTM structures. Each design is represented as a labeled assembly graph and is evaluated based on a set of behavior and structural equations. The initial populations are generated at random, and design candidates for subsequent generations are produced by user-specified selection techniques. Crossovers are applied by using cut and splice operators at the random points of the chromosomes; random mutations are applied to modify the graph with a certain low probability. This cycle continues until a suitable design is found. The research contributions in this work include the development of a new GA encoding scheme for mechanical assemblies (Legos), as well as the creation of selection criteria for this domain. Our eventual goal is to introduce a simulation of electromechanical devices into our evaluation functions. We believe that this research creates a foundation for future work and it will apply GA techniques to the evolution of more complex and realistic electromechanical structures.

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.

This study deals with both a decision model for making decisions under epistemic uncertainty and how to use it for selecting optimal materials under the same uncertainty. In particular, the proposed decision model employs a set of possibilistic objective functions defined by fuzzy numbers to handle a set of conflicting criteria. In addition, the model can calculate the compliance of a piece of decision-relevant (imprecise) information with a given objective function. Moreover, the model is capable to aggregate the calculated compliances for the sake of ranking a given set of alternatives against the set of conflicting criteria. The problem of selecting materials for making the body of a vehicle is considered as an example. In this problem, the indices for selecting the materials are unknown because the specifications regarding the vehicle body are not given. In addition, the data relevant to material properties entails a great deal of imprecision. The presented decision model can quantify the above-mentioned epistemic uncertainty in a lucid manner and generate a list of optimal materials.

The research on new types of design knowledge bases and conceptual solution catalogues is an advanced subject of design methodology. Using the separate function as an example, this paper establishes design catalogues for principle conceptual design to realize the function, and proposes a general computer-oriented action principle design process. Upon studying the relationship of knowledge in the process, this paper represents functions of action principles in function feature models and provides computer-oriented feature models for action principle design.

This paper discusses the integration of knowledge bases and shape grammars for the generation of building models, covering interaction, system, and implementation. Knowledge-based and generative systems are combined to construct a method for characterizing existing buildings, in particular, their interior layouts based on exterior features and certain other parameters such as location and real dimensions. The knowledge-based model contains information about spatial use, organization, elements, and contextual information, with the shape grammar principally containing style rules. Buildings are analyzed and layouts are generated through communication and interaction between these two systems. The benefit of using an interactive system is that the complementary properties of the two schemes are employed to strengthen the overall process. Ontologies capture knowledge relating to architectural design principles, building anatomy, structure, and systems. Shape grammar rules embody change through geometric manipulation and transformation. Existing buildings are analyzed using this approach, and three-dimensional models are automatically generated. Two particular building types, the vernacular rowhouse and high-rise apartment building, both from Baltimore, Maryland, are presented to illustrate the process and for comparing the utilized methodologies.

The paper introduces and discusses the notion of decomposition of a configuration problem within the framework of a structured logical approach. The paper describes under which conditions a given configuration problem can be decomposed into a set of noninteracting subproblems and how to exploit such a decomposition, both for improving the performance of the configurator and for supporting interactive configuration. Different kinds of decomposition are considered, but all of them exploit, as much as possible, the explicit representation of the partonomic relations in the language, a KL-One like representation formalism augmented with constraints for expressing complex interrole relations. The paper introduces a notion of boundness among constraints, which is used for formally specifying different types of decomposition. One decomposition strategy aims at singling out the components and subcomponents that are directly related to the constraints put by the user's requirements; the configurator exploits such decomposition by first configuring that portion of the product and then configuring the parts that are not related to the user's requirements. Another decomposition strategy verifies whether the set of constraints for the product to be configured can be split into a set of noninteracting problems. In such a case the configurator solves the configuration problem by splitting the whole search space into a set of smaller search spaces. Different combinations of these two decomposition techniques are considered, and the impact of the decomposition strategies on the performance of the configurator is evaluated via a set of experiments using the configuration of computer systems as a test bed. The results of the experiments show a significant reduction of the computational effort (both in terms of number of backtrackings and in CPU time) when decomposition strategies are used.

Digital libraries of case studies of analogical design have been popular since their advent in the early 1990s. We consider four benefits of digital libraries of case studies of analogical design in the context of biologically inspired design. First, a digital library affords documentation. The 83 case studies in our work come from 8 years of extended, collaborative design projects in an interdisciplinary class on biologically inspired design. Second, a digital library provides on-demand access to the case studies. We describe a web-based library of case studies of biologically inspired design called the Design Study Library (DSL). Third, a compilation of case studies supports analyses of broader patterns and trends. As an example, an analysis of DSL's case studies found that environmental sustainability was a major factor in about a third of the case studies and an explicit design goal in about a fourth. Fourth, a digital library of case studies can support analogical learning. Preliminary results from an exploratory study indicate that DSL may support novice learning about the processes of biologically inspired design.

Design fixation is a factor that negatively influences the generation of novel design concepts (Jansson & Smith, 1991). When designers fixate, they tend to reproduce example features or features from their initial ideas. In order to mitigate design fixation, it is crucial to identify the factors that influence the extent of design fixation. This paper investigates two such factors: the modality of examples and the familiarity of designers with the example features. To investigate this, an experiment is conducted with mechanical engineering students who were asked to generate ideas to solve a peanut sheller design problem. The students generated ideas in five different experimental conditions: control, where no example was given; the first example given in a sketch form; the first example given as a nonfunctional prototype; a second example in sketch form; and the second example in a working prototype form. The first example was a nonfeasible solution, but it contained several features familiar to the participants. The second example was a feasible solution, but it contained less familiar features. In order to understand the extent of fixation triggered by the examples, three metrics were utilized to compare across the experimental conditions: the quantity of nonredundant ideas generated by the participants, the presence of example features in their solutions, and their fixation to the example's energy source. The results showed that in the case of the familiar example, the example modality did play an important role in the extent of design fixation. Across the examples, it was found that the first example containing several familiar features caused more fixation than the second one. Overall, this paper shows that the modality in which the example was communicated and the presence of familiar features in an example influenced the fixation caused by those examples.

Biomimetic design applies biological analogies to solve design problems and has been known to produce innovative solutions. However, when designers are asked to perform biomimetic design, they often have difficulty recognizing analogies between design problems and biological phenomena. Therefore, this research aims to investigate designer behaviors that either hinder or promote the use of analogies in biomimetic design. A verbal protocol study was conducted on 30 engineering students working in small teams while participating in biomimetic design sessions. A coding scheme was developed to analyze cognitive processes involved in biomimetic design. We observed that teams were less likely to apply overall biological analogies if they tended to recall existing solutions that could be easily associated with specific superficial or functional characteristics of biological phenomena. We also found that the tendency to evaluate ideas, which reflects critical thinking, correlates with the likelihood of identifying overall biological analogies. Insights from this paper may contribute toward developing generalized methods to facilitate biomimetic design.

Data-enabled design (DED) is a promising new methodology for designing with users from within their own context in an iterative and hands-on fashion. However, the agile and flexible qualities of the methodology do not directly translate to every context. In this article, we reflect on the design process of an intelligent ecosystem, called ORBIT, and a proposed evaluative study planned with it. This was part of a DED project in collaboration with a medical hospital to study the post-operative behavior in the (remote) context of bariatric patients. The design and preparation of this project and the process towards an eventual study rejection from the medical ethical committee (METC) provide rich insights into (1) what it means to conduct DED research in a clinical context, and (2) where the boundaries of the method might lie in this specific application area. We highlight insights from carefully designing the substantial infrastructure for the study, and how different aspects of DED translated less easily to the clinical context. We analyze the proposed study setup through the lenses of several modifications we made to DED and further reflect on how to expand and scale up the methodology and adapt the process for the clinical context.

We present a case study showing a human-competitive design of an evolved antenna that was deployed on a NASA spacecraft in 2006. We were fortunate to develop our antennas in parallel with another group using traditional design methodologies. This allowed us to demonstrate that our techniques were human-competitive because our automatically designed antenna could be directly compared to a human-designed antenna. The antennas described below were evolved to meet a challenging set of mission requirements, most notably the combination of wide beamwidth for a circularly polarized wave and wide bandwidth. Two evolutionary algorithms were used in the development process: one used a genetic algorithm style representation that did not allow branching in the antenna arms; the second used a genetic programming style tree-structured representation that allowed branching in the antenna arms. The highest performance antennas from both algorithms were fabricated and tested, and both yielded very similar performance. Both antennas were comparable in performance to a hand-designed antenna produced by the antenna contractor for the mission, and so we consider them examples of human-competitive performance by evolutionary algorithms. Our design was approved for flight, and three copies of it were successfully flown on NASA's Space Technology 5 mission between March 22 and June 30, 2006. These evolved antennas represent the first evolved hardware in space and the first evolved antennas to be deployed.

The tool switching problem (ToSP) is well known in the domain of flexible manufacturing systems. Given a reconfigurable machine, the ToSP amounts to scheduling a collection of jobs on this machine (each of them requiring a different set of tools to be completed), as well as the tools to be loaded/unloaded at each step to process these jobs, such that the total number of tool switches is minimized. Different exact and heuristic methods have been defined to deal with this problem. In this work, we focus on memetic approaches to this problem. To this end, we have considered a number of variants of three different local search techniques (hill climbing, tabu search, and simulated annealing), and embedded them in a permutational evolutionary algorithm. It is shown that the memetic algorithm endowed with steepest ascent hill climbing search yields the best results, performing synergistically better than its stand-alone constituents, and providing better results than the rest of the algorithms (including those returned by an effective ad hoc beam search heuristic defined in the literature for this problem).

This paper explores using Shannon's entropy of information to measure linkographs of 12 design sessions that involved six architects in two different experimental conditions. The aim is to find a quantitative tool to interpret the linkographs. This study examines if the differences in the design processes and the design outcomes can be reflected in the entropic interpretations. The results show that the overall entropy of one design condition is slightly higher than the other. Further, there are indications that the change of entropy might reflect design outcomes.

The need to model and to reason about design alternatives throughout the design process demands robust representation schemes of function, behavior, and structure. Function describes the physical effect imposed on an energy or material flow by a design entity without regard for the working principles or physical solutions used to accomplish this effect. Behaviors are the physical events associated with a physical artifact (or hypothesized concept) over time (or simulated time) as perceived by an observer. Structure, the most tangible concept, partitions an artifact into meaningful constituents such as features, Wirk elements, and interfaces in addition to the widely used assemblies and components. The focus of this work is on defining a model for function-based representations that can be used across various design methodologies and for a variety of design tasks throughout all stages of the design process. In particular, the mapping between function and structure is explored and, to a lesser extent, its impact on behavior is noted. Clearly, the issues of a function-based representation's composition and mappings directly impact certain computational synthesis methods that rely on (digitally) archived product design knowledge. Moreover, functions have already been related to not only form, but also information of user actions, performance parameters in the form of equations, and failure mode data. It is essential to understand the composition and mappings of functions and their relation to design activities because this information is part of the foundation for function-based methods, and consequently dictates the performance of those methods. Toward this end, the important findings of this work include a formalism for two aspects of function-based representations (composition and mappings), the supported design activities of the model for function-based representations, and examples of how computational design methods benefit from this formalism.

Probabilistic and stochastic algorithms have been used to solve many hard optimization problems since they can provide solutions to problems where often standard algorithms have failed. These algorithms basically search through a space of potential solutions using randomness as a major factor to make decisions. In this research, the knapsack problem (optimization problem) is solved using a genetic algorithm approach. Subsequently, comparisons are made with a greedy method and a heuristic algorithm. The knapsack problem is recognized to be NP-hard. Genetic algorithms are among search procedures based on natural selection and natural genetics. They randomly create an initial population of individuals. Then, they use genetic operators to yield new offspring. In this research, a genetic algorithm is used to solve the 0/1 knapsack problem. Special consideration is given to the penalty function where constant and self-adaptive penalty functions are adopted.

This paper examines the need for explicit representations of the design space, in response to Woodbury and Burrow. Specifically, their proposal for a particular search strategy, by means of which one can reuse past experiences explicitly represented by previously traversed paths, is examined. This is done by exploring issues with respect to design search and representation in general, while relating these to specific issues raised by Woodbury and Burrow. The paper concludes by suggesting that their arguments essentially point to devising an appropriate “programming language” for design.

This paper presents our research on developing an ontology-based framework that can represent morphological characteristics related to assembly joints. Joints within the physical structure of an assembly are inevitable because of the limitations of component geometries and the associated, required engineering properties. Consequently, a framework is needed that can capture and propagate assembly design and joint information in a robust assembly model throughout the entire product development processes. The framework and model are based on an understanding of the morphological characteristics of an assembly and its different physical effects. The morphological characteristics are consequences of the principal physical processes and of the design intentions. Therefore, the morphological characteristics should be carefully represented while considering the geometry and topology of assembly joints. In this research, assembly joint topology is defined by a mereotopology, which is a region-based theory for the parts and associated concepts. This formal ontology can differentiate often ambiguous assembly and joining relations. Furthermore, the mereotopological definitions for assembly joints are implemented in Semantic Web Rule Language (SWRL) rules and Web Ontology Language triples. This process provides universality to the mereotopological definitions. Two geometrically and topologically similar joint pairs are presented to describe how the assembly joints can be defined in mereotopology and be transformed into SWRL rules. Web3D is also employed to support network-enabled sharing of assembly geometry. Finally, the proposed modeling framework is demonstrated using a real fixture assembly. This case study demonstrates the usability of the proposed framework for network-based design collaboration.

This paper presents the requirements and needs for establishing a benchmarking protocol that considers representation characteristics, supported cognitive criteria, and enabled reasoning activities for the systematic comparison of function modeling representations. Problem types are defined as reverse engineering, familiar products, novel products, and single-component systems. As different modeling approaches share elements, a comparison of modeling approaches on multiple levels was also undertaken. It is recommended that researchers and developers of function modeling representations collaborate to define a canonically acceptable set of benchmark tests and evaluations so that clear benefits and weaknesses for the disparate collection of approaches can be compared. This paper is written as a call to action for the research community to begin establishing a benchmarking standard protocol for function modeling comparison purposes. This protocol should be refined with input from developers of the competing approaches in an academically open environment. At the same time, the benchmarking criteria identified should also serve as a guide for validating a modeling approach or analyzing its failure.