Joseph Donndelinger

Visual Steering Commands for Trade Space Exploration: User-Guided Sampling With Example

Recent advancements in computing power and speed provide opportunities to revolutionize trade space exploration, particularly for the design of complex systems such as automobiles, aircraft, and spacecraft. In this paper, we introduce three Visual Steering Commands to support trade space exploration and demonstrate their use within a powerful data visualization tool that allows designers to explore multidimensional trade spaces using glyph, 1-D and 2-D histogram, 2-D scatter, scatter matrix, and parallel coordinate plots; linked views; brushing; preference shading and Pareto frontier display. In particular, we define three user-guided samplers that enable designers to explore (1) the entire design space, (2) near a point of interest, or (3) within a region of high preference. We illustrate these three samplers with a vehicle configuration model that evaluates the technical feasibility of new vehicle concepts. Future research is also discussed.Copyright

Function Design Framework (FDF): Integrated Process and Function Modeling for Complex Systems

Functional models are representations of the energy, material and signal transformations that occur through the expected or normal operating condition of a product. As the complexity of products increases, there are often multiple dimensions to their operation in addition to their nominal operating state, e.g., crash protection systems in a car or laser leveling and stud finding combined in a single tool. Here system state is used to represent the different operational dimensions of a product, and a representation scheme that allows designers to fully explore system functionality of products with multiple system states is explored. Previous work in process and functional analysis is integrated to better represent complex systems with multi-dimensional system functionality. Process and functional modeling are integrated to produce a new function design framework supporting user-defined fidelity of hierarchical models for functional representation. An example modeling a complete automobile life cycle illustrates the development of integrated process and functional models within a complex system analysis.Copyright

A constraint-based approach to feasibility assessment in preliminary design

In this paper, we present the development and application of a technical feasibility model used in preliminary design to determine whether a set of desired product specifications obtained from marketing is feasible in the engineering domain. This model is developed by integrating the capabilities of a multiobjective design problem, a multicriteria design optimization tool, a Pareto frontier gap analyzer, metamodeling methods, and use of the Pareto frontier as a constraint for feasibility assessment. Although the tools are independent of the domain, their application is illustrated using two examples: a simple three-objective mathematical problem and a five-objective passenger vehicle design problem. The feasibility of the desired product specifications is determined with respect to the problems Pareto frontier, which is considered to be the necessary constraint to satisfy.

A study of convergence and mapping in preliminary vehicle design

In this paper, we investigate the issue of convergence in multi-objective optimisation problems developed for vehicle analyses when using a Multi-Objective Genetic Algorithm (MOGA) to determine the set of Pareto optimal automobile configurations. Additionally, given a Pareto set for a multi-objective problem, the mapping between the performance and design space is studied to determine new automobile design configurations for a given set of performance specifications. The advantage of this study is that the automobiles design information is obtained without having to repeat system analyses. The tools developed in this paper are applied both to a simple multi-objective optimisation problem to illustrate the methodology and to a preliminary vehicle design framework to develop a Technical Feasibility Model (TFM) for use in the early stages of automobile design.

Exploring Mass Trade-Offs in Preliminary Vehicle Design Using Pareto Sets

Our goal in this work is to develop analytical tools to support the definition of balanced and compatible sets of vehicle specifications in the early stages of vehicle development. In this paper, we discuss the development and application of a Technical Feasibility Model (TFM) that may be used in preliminary design to assess the technical feasibility and optimality of specified combinations of vehicle performance targets. For this paper, we have exercised the TFM specifically to explore the relationships between vehicle mass, vehicle performance measures, (such as acceleration, fuel efficiency, and interior roominess), and high-level vehicle design parameters (such as overall exterior dimensions, occupant positions, and selection of a powertrain). The TFM is developed by first applying a MultiObjective Genetic Algorithm to a multidisciplinary design framework to generate a set of Pareto-optimal design solutions, then applying response surface methods to generate a smooth mathematical representation of the Pareto set, and finally using geometric construction to analyze the position of a test point relative to the representation of the Pareto set. Results of this analysis include an assessment of the feasibility and optimality of the test point as well as a variety of projections from the test point to the representation of the Pareto set that may be used to identify opportunities for refining, relaxing, improving, or prioritizing performance specifications. The mapping between performance space and design space has been preserved, allowing for investigation of relationships between performance specifications and design variable settings.

Creating targeted initial populations for genetic product searches in heterogeneous markets

Genetic searches often use randomly generated initial populations to maximize diversity and enable a thorough sampling of the design space. While many of these initial configurations perform poorly, the trade-off between population diversity and solution quality is typically acceptable for small-scale problems. Navigating complex design spaces, however, often requires computationally intelligent approaches that improve solution quality. This article draws on research advances in market-based product design and heuristic optimization to strategically construct ‘targeted’ initial populations. Targeted initial designs are created using respondent-level part-worths estimated from discrete choice models. These designs are then integrated into a traditional genetic search. Two case study problems of differing complexity are presented to illustrate the benefits of this approach. In both problems, targeted populations lead to computational savings and product configurations with improved market share of preferences. Future research efforts to tailor this approach and extend it towards multiple objectives are also discussed.

A Study of Convergence and Mapping in Multiobjective Optimization Problems

In this paper, we investigate the issue of convergence in multiobjective optimization problems when using a MultiObjective Genetic Algorithm (MOGA) to determine the set of Pareto optimal solutions. Additionally, given a Pareto set for a multi-objective problem, the mapping between the performance and design space is studied to determine design variable configurations for a given set of performance specifications. The advantage of this study is that the design variable information is obtained without having to repeat system analyses. The tools developed in this paper have been applied to develop a Technical Feasibility Model (TFM) used by General Motors as well as a simple multiobjective optimization problem in this paper. The multi-objective problem is primarily used to illustrate the developed methodology.

EXPLORING DIFFERENCES IN PREFERENCE HETEROGENEITY REPRESENTATION AND THEIR INFLUENCE IN PRODUCT FAMILY DESIGN

When using conjoint studies for market-based design, two model types can be fit to represent the heterogeneity present in a target market, discrete or continuous. In this paper, data from a choice-based conjoint study with 2275 respondents is analyzed for a 19-attribute combinatorial design problem with over 1 billion possible product configurations. Customer preferences are inferred from the choice task data using both representations of heterogeneity. The hierarchical Bayes mixed logit model exemplifies the continuous representation of heterogeneity, while the latent class multinomial logit model corresponds to the discrete representation. Product line solutions are generated by each of these model forms and are then explored to determine why differences are observed in both product solutions and market share estimates. These results reveal some potential limitations of the Latent Class model in the masking of preference heterogeneity. Finally, the ramifications of these results on the market-based design process are discussed.© 2011 ASME

Choice Model Specification in Market-Based Engineering Design

The application of market demand models in engineering design is now a well-established practice. One could consider the archetypical application to be a random utility model used in conjunction with a parametric design representation to optimize the design of a single product with respect to a risk-adjusted measure of profit. Much of the work in this area over the past decade has been focused on various extensions of this archetypical framework, such as problem decomposition and product family design. A wide variety of market demand models have been applied, including models derived from classic economic methods and random utility models spanning from multinomial logit through generalized extreme value to mixed logit. While there has been some discussion of the properties of the various choices of market demand models used in prior work, the most recent work in this area suggests that the consequences of market demand model specification in engineering design problems are both more significant than once realized and not yet fully understood. In this paper, we explore the consequences of market demand model specification specifically in the context of engineering design through both a review of prior work and an illustrative example problem featuring a market demand model parameterized in terms of reservation price. These results demonstrate that choices in market demand model specification — especially those relating to representation of customer heterogeneity — can lead to substantially different conclusions in a discrete product configuration design problem.Copyright

Feasibility Assessment in Preliminary Design Using Pareto Sets

In this paper, we present the development and application of a Technical Feasibility Model (TFM) used in preliminary design to determine whether or not a set of desired product specifications is technically feasible, and the optimality of those specifications with respect to the Pareto frontier. The TFM is developed by integrating the capabilities of a multidisciplinary design framework, a multi-objective design optimization tool, a Pareto set gap analyzer, metamodeling methods, and mathematical methods for feasibility assessment. This tool is then applied to a three objective example problem and to a five objective passenger vehicle design problem by analyzing benchmarking data from 78 late model sedans.