scholarly journals Bayesian Network Classifiers for Set-Based Collaborative Design

2010 ◽  
Author(s):  
David Shahan ◽  
Carolyn C. Seepersad
Keyword(s):  
Design Process ◽  
Collaborative Design ◽  
Probability Distributions ◽  
Design Problems ◽  
Fuzzy Membership Functions ◽  
Domain Specific ◽  
Bayesian Network Classifiers ◽  
Complex Design ◽  
Data Points ◽  
Aerial Vehicle

Complex design problems are typically decomposed into smaller design problems that are solved by domain-specific experts who must then coordinate their solutions into a satisfactory system-wide solution. In set-based collaborative design, collaborating engineers coordinate themselves by communicating multiple design alternatives at each step of the design process. The goal in set-based collaborative design is to spend additional resources exploring multiple options in the early stages of the design process, in exchange for less iteration in the latter stages, when iterative rework tends to be most expensive. Several methods have been proposed for representing sets of designs, including intervals, surrogate models, fuzzy membership functions, and probability distributions. In this paper, we introduce the use of Bayesian networks for capturing sets of promising designs, thereby classifying the design space into satisfactory and unsatisfactory regions. The method is compared to intervals in terms of its capacity to accurately classify satisfactory design regions as a function of the number of available data points. A simplified, multilevel design problem for an unmanned aerial vehicle is presented as the motivating example.

Bayesian Networks for Set-Based Collaborative Design

2009 ◽  
Author(s):  
David Shahan ◽  
Carolyn C. Seepersad
Keyword(s):  
Bayesian Networks ◽  
Design Process ◽  
Collaborative Design ◽  
Design Space ◽  
Expert Knowledge ◽  
Hill Climbing ◽  
Local Networks ◽  
Design Problems ◽  
Potential Benefits ◽  
Aerial Vehicle

A set-based approach to collaborative design is presented, in which Bayesian networks are used to represent promising regions of the design space. In collaborative design exploration, complex multilevel design problems are often decomposed into distributed subproblems that are linked by shared or coupled parameters. Collaborating designers often prefer conflicting values for these coupled parameters, resulting in incompatibilities that require substantial iteration to resolve, extending the design process lead time without guarantee of achieving a good design. In the proposed approach to collaborative design, each designer builds a locally developed Bayesian network that represents regions of interest in his design space. Then, these local networks are shared and combined with those of collaborating designers to promote more efficient local design space search that takes into account the interests of one’s collaborators. The proposed method has the potential to capture a designer’s preferences for arbitrarily shaped and potentially disconnected regions of the design space in order to identify compatible or conflicting preferences between collaborators and to facilitate a compromise if necessary. It also sets the stage for a flexible and concurrent design process with varying degrees of designer involvement that can support different designer strategies such as hill-climbing or region identification. The potential benefits are the capture of expert knowledge for future use as well as conflict identification and resolution. This paper presents an overview of the proposed method as well as an example implementation for the design of an unmanned aerial vehicle.

Sequential Sampling With Kernel-Based Bayesian Network Classifiers

2011 ◽  
Author(s):  
David Shahan ◽  
Carolyn C. Seepersad
Keyword(s):  
Bayesian Network ◽  
Collaborative Design ◽  
Design Space ◽  
Sequential Sampling ◽  
Space Filling ◽  
Training Set ◽  
Design Problems ◽  
Sampling Schemes ◽  
Domain Specific ◽  
Bayesian Network Classifiers

Complex design problems are typically decomposed into smaller design problems that are solved by domain-specific experts who must then coordinate their solutions into a satisfactory system-wide solution. In set-based collaborative design, collaborating engineers coordinate themselves by communicating multiple design alternatives at each step of the design process. Previous research has demonstrated that classifiers can be a communication medium for facilitating set-based collaborative design because of their ability to divide a design space into satisfactory and unsatisfactory regions. The proposed kernel-based Bayesian network (KBN) classifier uses a set of example designs of known acceptability, called the training set, to create a map of the satisfactory region of the design space. However, previous implementations used deterministic space-filling sampling sequences to choose the training set of designs. The shortcoming of deterministic space-filling sampling schemes is that they do not adapt to focus the samples on regions of interest to the design team (exploitation) or, alternatively, on regions in which little information is known (exploration). In this paper, we introduce the use of KBN classifiers as the basis for sequential sampling strategies that can be exploitive, exploratory, or any combination thereof.

A Proposal of Classification and Guideline of Selection for Design Modeling Methods

2007 ◽  
Author(s):  
Takashi Asanuma ◽  
Jumpei Kawashima ◽  
Yoshiki Ujiie ◽  
Yoshiyuki Matsuoka
Keyword(s):  
Design Process ◽  
Design Solution ◽  
Human Beings ◽  
Design Problems ◽  
Modeling Methods ◽  
Complex Design ◽  
The Social ◽  
Selection For ◽  
Design Objects ◽  
Exact Design

In recent years the demands of users and the social problems have been diverse. In design, the diverse demands of users and problems of the society have created increasingly complex design problems. Therefore, it is important to understand values and images of the design objects and analyze the relation among design objects, human beings and its environment to respond to the complicated design problems. A number of design modeling methods that realize above points have been proposed. Consequently, it is necessary for designers and engineers to derivate the exact design solution that responds to the complicated design problems. However, the framework of design modeling methods in design has not been established. Moreover, most of the current studies on the methods only respond to the problems in each aspect of design [1]. Therefore, designers and engineers apply the design modeling methods in each design process based on their knowledge and experiences. The guideline of selection for the application of design modeling methods has not been shown. Consequently, the guideline for selecting the design modeling methods is needed for designers and engineers to apply the methods appropriately in design.

scholarly journals Bayesian Network Classifiers for Set-Based Collaborative Design

2012 ◽  
Vol 134 (7) ◽  
Author(s):  
David W. Shahan ◽  
Carolyn Conner Seepersad
Keyword(s):  
Bayesian Network ◽  
Collaborative Design ◽  
Design Problem ◽  
Design Space ◽  
Expert Knowledge ◽  
Optimization Techniques ◽  
Domain Specific ◽  
Bayesian Network Classifiers ◽  
New Information ◽  
Engineering Design Problems

Complex engineering design problems are often decomposed into a set of interdependent, distributed subproblems that are solved by domain-specific experts. These experts must resolve couplings between the subproblems and negotiate satisfactory, system-wide solutions. Set-based approaches help resolve these couplings by systematically mapping satisfactory regions of the design space for each subproblem and then intersecting those maps to identify mutually satisfactory system-wide solutions. In this paper, Bayesian network classifiers are introduced for mapping sets of promising designs, thereby classifying the design space into satisfactory and unsatisfactory regions. The approach is applied to two example problems—a spring design problem and a simplified, multilevel design problem for an unmanned aerial vehicle (UAV). The method is demonstrated to offer several advantages over competing techniques, including the ability to represent arbitrarily shaped and potentially disconnected regions of the design space and the ability to be updated straightforwardly as new information about the satisfactory design space is discovered. Although not demonstrated in this paper, it is also possible to interface the classifier with automated search and optimization techniques and to combine expert knowledge with the results of quantitative simulations when constructing the classifiers.

A Model of Collaborative Design Decision Making Using Timed Petri-Net

2006 ◽  
Author(s):  
Zhiqiang Chen ◽  
Zahed Siddique
Keyword(s):  
Decision Making ◽  
Design Process ◽  
Petri Net ◽  
Collaborative Design ◽  
Process Model ◽  
Design Decision ◽  
Design Problems ◽  
Design Variables ◽  
And Performance ◽  
Improved Design

This paper presents a Petri-net process model that captures the dependency relationships of design decision making and information exchanges among multiple design problems in a distributed environment. The Model of Distributed Design (MDD) allows quantitative representation of a collaborative design process in which designers from multiple disciplines can effectively work together. The MDD is developed based on the Petri-net graph, which allows various performance analysis to be performed to evaluate and improve a collaborative design process. In this paper, the compromise Decision Support Problem (c-DSP) formulation is used to describe the design problems and the Petri-net is utilized to explicitly describe the propagation of shared design variables and the interactions. The applicability of the model is demonstrated through an example design problem that requires collaboration among four design disciplines. The design processes based on the example are modeled and then analyzed to obtain process features and performance evaluations. Based on the analysis results, an improved design process is given which shortens the design time.

scholarly journals DECOMPOSITION AND RECOMPOSITION STRATEGIES OF PROFESSIONAL ENGINEERING DESIGN TEAMS

2021 ◽  
Vol 1 ◽  
pp. 871-880
Author(s):  
Julie Milovanovic ◽  
John Gero ◽  
Kurt Becker
Keyword(s):  
Design Process ◽  
Design Theory ◽  
Design Teams ◽  
Theoretical Understanding ◽  
Design Problems ◽  
Engineering Teams ◽  
Complex Design ◽  
Decomposition Strategies ◽  
Design Proposal ◽  
Empirical Foundation

AbstractDesigners faced with complex design problems use decomposition strategies to tackle manageable sub-problems. Recomposition strategies aims at synthesizing sub-solutions into a unique design proposal. Design theory describes the design process as a combination of decomposition and recomposition strategies. In this paper, we explore dynamic patterns of decomposition and recomposition strategies of design teams. Data were collected from 9 teams of professional engineers. Using protocol analysis, we examined the dominance of decomposition and recomposition strategies over time and the correlations between each strategy and design processes such as analysis, synthesis, evaluation. We expected decomposition strategies to peak early in the design process and decay overtime. Instead, teams maintain decomposition and recomposition strategies consistently during the design process. We observed fast iteration of both strategies over a one hour-long design session. The research presented provides an empirical foundation to model the behaviour of professional engineering teams, and first insights to refine theoretical understanding of the use decomposition and recomposition strategies in design practice.

A Set-Based Method for Multilevel Design of Materials and Structures With Manufacturing Uncertainty

2016 ◽  
Author(s):  
Clinton Morris ◽  
Carolyn C. Seepersad ◽  
Michael R. Haberman
Keyword(s):  
High Performance ◽  
Design Space ◽  
Probability Distributions ◽  
Joint Probability ◽  
Mechanical Stiffness ◽  
Design Problems ◽  
Paper Manufacturing ◽  
Bayesian Network Classifiers ◽  
Multi Level ◽  
Joint Probability Distributions

Recent research has indicated that embedding small volume fractions of negative stiffness (NS) inclusions within a host material can create composites with macroscopic mechanical stiffness and loss properties that exceed conventional composites. To design these composites, a multi-level, set-based approach that employs Bayesian network classifiers was developed to identify sets of satisfactory designs at each level of the multilevel design space. In this paper, manufacturing uncertainties are incorporated to further refine the design space mappings created by the set-based approach. Manufacturing uncertainty refers to the random deviations in dimensions and other properties that often arise when fabricating a specimen. Joint probability distributions are used to model this manufacturing uncertainty. The joint probability distributions are formulated as kernel density estimates that can be based on manufacturing data. The joint probability distributions are incorporated within the set-based approach to identify sets of designs that not only yield satisfactory performance but also offer robustness to manufacturing uncertainty. The approach is demonstrated in the context of hierarchical composite materials, but it can be applied to other multi-level design problems to efficiently yield sets of robustly manufacturable, high performance designs.

Causing a Stir

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Camilo POTOCNJAK-OXMAN
Keyword(s):  
Design Process ◽  
Collaborative Design ◽  
Entrepreneurial Activity ◽  
Design Team ◽  
Design Processes ◽  
Early Stages

Stir was a crowd-voted grants platform aimed at supporting creative youth in the early stages of an entrepreneurial journey. Developed through an in-depth, collaborative design process, between 2015 and 2018 it received close to two hundred projects and distributed over fifty grants to emerging creatives and became one of the most impactful programs aimed at increasing entrepreneurial activity in Canberra, Australia. The following case study will provide an overview of the methodology and process used by the design team in conceiving and developing this platform, highlighting how the community’s interests and competencies were embedded in the project itself. The case provides insights for people leading collaborative design processes, with specific emphasis on some of the characteristics on programs targeting creative youth

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