Mining Functional Model Graphs to Find Product Design Heuristics With Inclusive Design Illustration

2013 ◽  
Vol 13 (4) ◽  
Author(s):  
Shraddha Sangelkar ◽  
Daniel A. McAdams
Keyword(s):  
Data Mining ◽  
Functional Model ◽  
Inclusive Design ◽  
Generation Process ◽  
Graph Visualization ◽  
Concept Generation ◽  
Product Representation ◽  
Graph Data ◽  
Design Heuristics ◽  
Functional Models

Engineering design heuristics offer the potential to improve the design process and resultant designs. Currently, heuristics are empirically derived by experts. The goal of this paper is to automate the heuristics generation process. Functional modeling, a well-established product representation framework, is applied in this research to abstract the intended functionality of a product. Statistically significant heuristics, extracted from a database of functional models, serve as design suggestions or guidelines for concept generation. The heuristics can further be applied to automate portions of the concept generation process. Prior research efforts in automated concept generation rely heavily on the design repository. The repository needs to be appended for broader categories of design problems, and, at the same time, a tool for quick analysis of the expanded repository is required. An automated heuristic extraction process has the capability to efficiently mine the updated repositories and find new heuristics for design practice. A key objective of this research is to develop design heuristics applicable in the diverse and challenging domain of inclusive design. The research applies graph theory for mathematical representation of the functional model, graph visualization for comprehending graphs, and graph data mining to extract heuristics. The results show that the graphical representation of functional models along with graph visualization allows quick updates to the design repository. In addition, we show that graph data mining has the capability to efficiently search for new design heuristics from the updated repository.

Improving Students' Functional Modeling Skills: A Modeling Approach and a Scoring Rubric

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Robert L. Nagel ◽  
Matt R. Bohm ◽  
Julie S. Linsey ◽  
Marie K. Riggs
Keyword(s):  
Engineering Design ◽  
Functional Model ◽  
Concept Generation ◽  
Functional Modeling ◽  
Generate Functional ◽  
Algorithmic Approach ◽  
Functional Models ◽  
Grammar Rules ◽  
Students First ◽  
Scoring Rubric

An engineering design curriculum that introduces functional modeling methods is believed to enhance the ability to abstract complex systems, assist during the concept generation phase of design, and reduce design fixation. To that end, a variety of techniques for considering function during design have been proposed in the literature, yet there are a lack of validated approaches for teaching students to generate functional models and no reliable method for the assessment of functional models. This paper presents a study investigating students' ability to generate functional models during a homework assignment; the study includes three different treatment conditions: (1) students who receive only a lecture on functional modeling, (2) students who receive a lecture on functional modeling as well as a step-by-step example, and (3) students who receive a lecture, a step-by-step example, and an algorithmic approach with grammar rules. The experiment was conducted in a cornerstone, undergraduate engineering design course, and consequently, was the students' first exposure to functional modeling. To assess student generated functional models across all three conditions, an 18 question functional model scoring rubric was developed based on flow-based functional modeling standards. Use of the rubric to assess the student generated functional models resulted in high inter-rater agreement for total score. Results show that students receiving the step-by-step example perform as well as students receiving the step-by-step example and an algorithmic approach with grammar rules; both groups perform better than the lecture-only group.

Optimizing an Algorithm for Data Mining a Design Repository to Automate Functional Modeling

2020 ◽  
Author(s):  
Alex Mikes ◽  
Katherine Edmonds ◽  
Robert B. Stone ◽  
Bryony DuPont
Keyword(s):  
Data Mining ◽  
Functional Model ◽  
Prediction Algorithm ◽  
Optimum Size ◽  
Functional Modeling ◽  
Training Set ◽  
Specific Focus ◽  
Average Accuracy ◽  
Functional Models

Abstract The purpose of this research is to find the optimum values for threshold variables used in a data mining and prediction algorithm. We also minimize and stratify a training set to find the optimum size based on how well it represents the whole dataset. Our specific focus is automating functional models, but the method can be applied to any dataset with a similar structure. We iterate through different values for two of the threshold variables in this process and cross-validate to calculate the average accuracy and find the optimum values for each variable. We optimize the training set by reducing the size by 78% and stratifying the data, whereby we achieve an accuracy that is 96% as good as the whole training set and takes 50% less time. These optimum values can be used to better predict the functions and flows of any future product based on its constituent components, which can be used to generate a complete functional model.

scholarly journals Current Situation and Application of Graph Data Mining Technology

2017 ◽  
Vol 10 (3) ◽  
pp. 1-10
Author(s):  
Mengke Zhang ◽  
Pingping Wei ◽  
Suzhi Zhang ◽  
Jiaxing Xu
Keyword(s):  
Data Mining ◽  
Current Situation ◽  
Mining Technology ◽  
Graph Data

Functional Models With Inherent Behavior: Towards a Framework for Safety Analysis Early in the Design of Complex Systems

2016 ◽  
Author(s):  
Matthew G. McIntire ◽  
Elham Keshavarzi ◽  
Irem Y. Tumer ◽  
Christopher Hoyle
Keyword(s):  
Design Process ◽  
Functional Model ◽  
Behavioral Model ◽  
Safety Analysis ◽  
State Machine ◽  
System Component ◽  
State Transitions ◽  
Failure Behavior ◽  
State Machines ◽  
Functional Models

This paper represents a step toward a more complete frame-work of safety analysis early in the design process, specifically during functional modeling. This would be especially useful when designing in a new domain, where many functions have yet to be solved, or for a problem where the functional architecture space is large. In order to effectively analyze the inherent safety of a design only described by its functions and flows, we require some way to simulate it. As an already-available function failure reasoning tool, Function Failure Identification and Propagation (FFIP) utilizes two distinct system models: a behavioral model, and a functional model. The behavioral model simulates system component behavior, and FFIP maps specific component behaviors to functions in the functional model. We have created a new function-failure reasoning method which generalizes failure behavior directly to functions, by which the engineer can create functional models to simulate the functional failure propagations a system may experience early in the design process without a separate behavioral model. We give each basis-defined function-flow element a pre-defined behavior consisting of nominal and failure operational modes, and the resultant effect each mode has on its functions connected flows. Flows are represented by a two-variable object reminiscent of a bond from bond graphs: the state of each flow is represented by an effort variable and a flow-rate variable. The functional model may be thought of as a bond graph where each functional element is a state machine. Users can quickly describe functional models with consistent behavior by constructing their models as Python NetworkX graph objects, so that they may quickly model multiple functional architectures of their proposed system. We are implementing the method in Python to be used in conjunction with other function-failure analysis tools. We also introduce a new method for the inclusion of time in a state machine model, so that dynamic systems may be modeled as fast-evaluating state machines. State machines have no inherent representation of time, while physics-based models simulate along repetitive time steps. We use a more middle-ground pseudo time approach. State transitions may impose a time delay once all of their connected flow conditions are met. Once the entire system model has reached steady state in a timeless sense, the clock is advanced all at once to the first time at which a reported delay is ended. Simulation then resumes in the timeless sense. We seek to demonstrate this modeling method on an electrical power system functional model used in previous FFIP studies, in order to compare the failure scenario results of an exhaustive fault combination experiment with similar results using the FFIP method.

scholarly journals How do designers generate new ideas? Design heuristics across two disciplines

2015 ◽  
Vol 1 ◽  
Author(s):  
Seda Yilmaz ◽  
Shanna R. Daly ◽  
Colleen M. Seifert ◽  
Richard Gonzalez
Keyword(s):  
Cognitive Strategies ◽  
Concept Generation ◽  
Design Concepts ◽  
Design Heuristics ◽  
Frequent Use ◽  
New Ideas ◽  
Industrial Designers ◽  
Protocol Study ◽  
High Degree ◽  
Design Heuristic

Research supports the central role cognitive strategies can play in successful concept generation by individual designers. Design heuristics have been shown to facilitate the creation of new design concepts in the early, conceptual stage of the design process, as well as throughout the development of ideas. However, we know relatively little about their use in differing disciplines. This study examined evidence of design heuristic use in a protocol study with 12 mechanical engineers and 12 industrial designers who worked individually to develop multiple concepts. The open-ended design problem was for a novel product, and the designers’ sketches and comments were recorded as they worked on the problem for 25 min and in a retrospective interview. The results showed frequent use of design heuristics in both disciplines and a significant relationship to the rated creativity of the concepts. Though industrial designers used more heuristics in their concepts, there was a high degree of similarity in heuristic use. Some differences between design disciplines were observed in the choice of design heuristics, where industrial designers showed a greater emphasis on user experience, environmental contexts, and added features. These findings demonstrate the prevalence of design heuristics in individual concept generation and their effectiveness in generating creative concepts, across two design domains.

Graph Data Mining in Recommender Systems

2021 ◽  
pp. 491-496
Author(s):  
Hongxu Chen ◽  
Yicong Li ◽  
Haoran Yang
Keyword(s):  
Data Mining ◽  
Recommender Systems ◽  
Graph Data

A Knowledge Extraction and Design Support System for Supporting Industrial and Product Design

2017 ◽  
Vol 4 (2) ◽  
pp. 1-18
Author(s):  
W. B. Lee ◽  
W. M. Wang ◽  
C. F. Cheung ◽  
Z. H. Wu
Keyword(s):  
Product Design ◽  
Support System ◽  
Knowledge Extraction ◽  
Design Stage ◽  
Generation Process ◽  
Concept Generation ◽  
Design Support ◽  
Design Concepts ◽  
Design Support System ◽  
Design Ideas

Industrial and product design involves a lot of unstructured information for the generation of innovative product design ideas. However, the generation of innovative design concepts is not only time consuming but also heavily relies on the experience of product designers. Most existing systems focus mainly on the technical aspects of realizing product designs, which are inadequate to support concept generation process at the pre-design stage. In this paper, a knowledge extraction and design support system (KEDSS) is presented. The system aims at extracting key design concepts and depicting the trends of these concepts from the massive amount of unstructured design information in the open domain. A summary report, a related concept list, and concept trend graphs are produced based on the inputs of the designers' design ideas. A series of experiments have been conducted to measure the performance of the system. Moreover, the system has been successfully trial implemented as part of a public service platform for modern industrial design of injection molding machinery and equipment.

scholarly journals More faithfulness graph embedding

2015 ◽  
Vol 4 (2) ◽  
pp. 336
Author(s):  
Alaa Najim
Keyword(s):  
Dimensionality Reduction ◽  
Graph Embedding ◽  
New Method ◽  
Graph Representation ◽  
Data Sets ◽  
Graph Visualization ◽  
Graph Data ◽  
Original Space ◽  
Data Points ◽  
Effectiveness And Efficiency

<p><span lang="EN-GB">Using dimensionality reduction idea to visualize graph data sets can preserve the properties of the original space and reveal the underlying information shared among data points. Continuity Trustworthy Graph Embedding (CTGE) is new method we have introduced in this paper to improve the faithfulness of the graph visualization. We will use CTGE in graph field to find new understandable representation to be more easy to analyze and study. Several experiments on real graph data sets are applied to test the effectiveness and efficiency of the proposed method, which showed CTGE generates highly faithfulness graph representation when compared its representation with other methods.</span></p>

Modality and representation in analogy

2008 ◽  
Vol 22 (2) ◽  
pp. 85-100 ◽  
Author(s):  
J.S. Linsey ◽  
K.L. Wood ◽  
A.B. Markman
Keyword(s):  
Prior Knowledge ◽  
English Language ◽  
Functional Model ◽  
Cognitive Mechanisms ◽  
Support Design ◽  
Design Problems ◽  
Functional Models ◽  
Current Design ◽  
Design By Analogy ◽  
General Semantic

AbstractDesign by analogy is a powerful part of the design process across the wide variety of modalities used by designers such as linguistic descriptions, sketches, and diagrams. We need tools to support people's ability to find and use analogies. A deeper understanding of the cognitive mechanisms underlying design and analogy is a crucial step in developing these tools. This paper presents an experiment that explores the effects of representation within the modality of sketching, the effects of functional models, and the retrieval and use of analogies. We find that the level of abstraction for the representation of prior knowledge and the representation of a current design problem both affect people's ability to retrieve and use analogous solutions. A general semantic description in memory facilitates retrieval of that prior knowledge. The ability to find and use an analogy is also facilitated by having an appropriate functional model of the problem. These studies result in a number of important implications for the development of tools to support design by analogy. Foremost among these implications is the ability to provide multiple representations of design problems by which designers may reason across, where the verb construct in the English language is a preferred mode for these representations.

Research of Data Graph Mining Based on Telecommunication Customers

2013 ◽  
Vol 443 ◽  
pp. 402-406 ◽  
Author(s):  
Shang Gao ◽  
Mei Mei Li
Keyword(s):  
Data Mining ◽  
Graph Mining ◽  
Pattern Mining ◽  
Rapid Development ◽  
Frequent Pattern Mining ◽  
Frequent Pattern ◽  
Practical Significance ◽  
Research Progress ◽  
Graph Data ◽  
Data Graph

With the rapid development of the number of mobile phone users has accumulated a large number of graph data, graph data mining has gradually become a hot area of research. Traditional data such as clustering, classification, frequent pattern mining gradually extended to the field of graph data mining research. Introduced at this stage graph data mining technology research progress, summarizes the characteristics of the graphical data mining, practical significance, the main problem, and scenarios to discuss and forecast chart data, especially research on uncertain graph data become trends and hot spots.

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