Common Functionality Across Engineering Domains Through Transfer Functions and Bond Graphs

Author(s):  
Briana M. Lucero ◽  
Matthew J. Adams

Functional Modeling allows a direct, and sometimes abstract, method for depicting a product. Through this method, product architecture, concept generation and physical modeling can be used to obtain repeatable and more meaningful results. The Functional Basis approach of engineering design, as taught to engineering design students, provides the vocabulary to produce a uniform approach to function structures with functions (verbs) and flows (nouns). This paper suggests that the flows, particularly the “signal” flows, can be correlated to additional domains domain through transfer functions common in controls engineering. Controls engineering employs transfer functions to mathematically represent the physical or digital functions of a system or product using block diagrams to show the individual steps. The research herein suggests the correlations between the mathematical representations of transfer functions and the functional basis of engineering design through the actions performed upon “signal” flows. Specifically, the methodologies employed by controls engineering can relate to engineering design by 1) Schematic similarities, 2) Quantifiable performance metric inputs/outputs, 3) Mathematical representations of the flows, and 4) isomorphic matching of the schematics. Controls systems use block diagrams to represent the sequential steps of the system, These block diagrams parallel the functions structures of engineering design. Performance metrics between the two domains can be complimentary when decomposed down to non-dimensional engineering units. Mathematical Functions of the actions in a controls systems can resemble the functional basis functions through the use if bond graphs by identifying characteristic behavior of the functions on the flows. Isomorphic matching using the schematic diagrams can be used to find analogies based upon similar functionality and target performance metrics. When these four similarities are performed, parallels between the engineering domain and the controls engineering can be establish. Examples of cross-domain matching via transfer functions and controls systems are provided as contextualization for the concepts proposed. Pathways forward for this preliminary research are additionally suggested.

Author(s):  
Briana M. Lucero ◽  
Matthew J. Adams ◽  
Cameron J. Turner

AbstractFunctional modeling is an effective method of depicting products in the design process. Using this approach, product architecture, concept generation, and physical modeling all contribute to the design process to generate a result full of quality and functionality. The functional basis approach provides taxonomy of uniform vocabulary to produce function structures with consistent functions (verbs) and flows (nouns). Material and energy flows dominate function structures in the mechanical engineering domain with only a small percentage including signal flows. Research suggests that the signal flow gap is due to the requirement of “carrier” flows of either material or energy to transport the signals between functions. This research suggests that incorporating controls engineering methodologies may increase the number of signal flows in function structures. We show correlations between the functional modeling and controls engineering in four facets: schematic similarities, performance matching through flows, mathematical function creation using bond graphs, and isomorphic matching of the aforementioned characteristics allows for analogical solutions. Controls systems use block diagrams to represent the sequential steps of the system. These block diagrams parallel the function structures of engineering design. Performance metrics between the two domains can be complimentary when decomposed down to nondimensional engineering units. Mathematical functions of the actions in controls systems can resemble the functional basis functions with bond graphs by identifying characteristic behavior of the functions on the flows. Isomorphic matching, using the schematic diagrams, produces analogies based upon similar functionality and target performance metrics. These four similarities bridge the mechanical and electrical domains via the controls domain. We provide concepts and contextualization for the methodology using domain-agnostic examples. We conclude with suggestion of pathways forward for this preliminary research.


Author(s):  
Briana Lucero ◽  
Peter Ngo ◽  
Julie Linsey ◽  
Cameron J. Turner

Computational tools for aiding design-by-analogy have so far focused on function- and keyword-based retrieval of analogues. Given the critical role of performance and benchmarking in design, there is a need for performance metrics-driven analogy retrieval that is currently unmet. Towards meeting this need, a study has been done to investigate and propose frameworks for organizing the myriad technical performance metrics in engineering design, such as measures of efficiency. Such organizational frameworks are needed for the implementation of a computational tool which can retrieve relevant analogies using performance metrics. The study, which takes a deductive approach, defines a hierarchical taxonomy of performance metrics akin to the functional basis vocabulary of function and flow terms. Its derivation follows from bond graphs, control theory, and Design for X guidelines.


2018 ◽  
Vol 140 (9) ◽  
Author(s):  
Ashish M. Chaudhari ◽  
Zhenghui Sha ◽  
Jitesh H. Panchal

Crowdsourcing is the practice of getting ideas and solving problems using a large number of people on the Internet. It is gaining popularity for activities in the engineering design process ranging from concept generation to design evaluation. The outcomes of crowdsourcing contests depend on the decisions and actions of participants, which in turn depend on the nature of the problem and the contest. For effective use of crowdsourcing within engineering design, it is necessary to understand how the outcomes of crowdsourcing contests are affected by sponsor-related, contest-related, problem-related, and individual-related factors. To address this need, we employ existing game-theoretic models, empirical studies, and field data in a synergistic way using the theory of causal inference. The results suggest that participants' decisions to participate are negatively influenced by higher task complexity and lower reputation of sponsors. However, they are positively influenced by the number of prizes and higher allocation to prizes at higher levels. That is, an amount of money on any following prize generates higher participation than the same amount of money on the first prize. The contributions of the paper are: (a) a causal graph that encodes relationships among factors affecting crowdsourcing contests, derived from game-theoretic models and empirical studies, and (b) a quantification of the causal effects of these factors on the outcomes of GrabCAD, Cambridge, MA contests. The implications of these results on the design of future design crowdsourcing contests are discussed.


Author(s):  
Vance D. Browne

Abstract The process by which new products are brought to market — the product realization process, or PRP — can be introduced in engineering design education. In industry, the PRP has been evolving to concurrent engineering and product teams. The PRP includes components such as concept generation, analysis, manufacturing process development and customer interaction. Also, it involves the sequencing of the components and their connections which includes teamwork, project planning, meetings, reports and presentations. A capstone senior engineering project, along with classroom lectures and presentations can be structured to provide knowledge and experience to the students in many of the PRP components and the connections. This paper will give an overview of the PRP and a project/lecture structure at the author’s university. The instructor recently joined the academic ranks after years in industry with responsibility for directing product development and R&D and for leading product development teams.


Author(s):  
Julie M. Hirtz ◽  
Robert B. Stone ◽  
Daniel A. McAdams ◽  
Simon Szykman ◽  
Kristin L. Wood

Abstract All products and artifacts are designed for a purpose. There is some intended reason behind their existence: the product or artifact function. Functional modeling provides an abstract, yet direct, method for understanding and representing an overall product or artifact function. Function modeling also provides a strategy for problem decomposition, physical modeling, product architecting, concept generation, and team organization. A formal function representation is needed to support function modeling, and a standardized set function-related terminology is necessary to achieve repeatable and meaningful results from such a representation. We refer to this representation as a functional basis; in this paper, we seek to reconcile and integrate two independent research efforts into a significantly evolved functional basis. These efforts include research from the National Institute of Standards and Technology (NIST) and two U.S. universities, and their industrial partners. The overall approach for integrating the functional representations is developed, in addition to the final results. The integration process is discussed relative to differences, similarities, insights into the representations, and product validation. Based on the results, a more versatile and comprehensive design vocabulary is obtained. This vocabulary will greatly enhance and expand the frontiers of research in design repositories, product architecture, design synthesis, and general product modeling.


Author(s):  
Neeraj Sonalkar ◽  
Kathryn Jablokow ◽  
Jonathan Edelman ◽  
Ade Mabogunje ◽  
Larry Leifer

This paper investigates the relationship between interaction behaviors and the cognitive characteristics of participating individuals in engineering design teams engaged in concept generation. Individual characteristics were measured using the Kirton Adaption-Innovation inventory (KAI), which assesses an individual’s cognitive preference for structure in seeking and responding to change. Team interactions were measured using the Interaction Dynamics Notation (IDN), which allows interaction behaviors to be quantitatively analyzed. A correlation analysis revealed statistically significant correlations between individual characteristics and specific interaction behaviors, and ideation utterances. An interaction sequence analysis of the team data also revealed specific interaction sequences associated with greater probabilities of idea occurrence within the team. These findings serve as a first step towards building a cognitive-behavioral model of engineering design team performance.


Author(s):  
Jacquelyn K.S. Nagel ◽  
Robert L. Nagel ◽  
Robert B. Stone ◽  
Daniel A. McAdams

AbstractThe natural world provides numerous cases for inspiration in engineering design. Biological organisms, phenomena, and strategies, which we refer to as biological systems, provide a rich set of analogies. These systems provide insight into sustainable and adaptable design and offer engineers billions of years of valuable experience, which can be used to inspire engineering innovation. This research presents a general method for functionally representing biological systems through systematic design techniques, leading to the conceptualization of biologically inspired engineering designs. Functional representation and abstraction techniques are used to translate biological systems into an engineering context. The goal is to make the biological information accessible to engineering designers who possess varying levels of biological knowledge but have a common understanding of engineering design. Creative or novel engineering designs may then be discovered through connections made between biology and engineering. To assist with making connections between the two domains concept generation techniques that use biological information, engineering knowledge, and automatic concept generation software are employed. Two concept generation approaches are presented that use a biological model to discover corresponding engineering components that mimic the biological system and use a repository of engineering and biological information to discover which biological components inspire functional solutions to fulfill engineering requirements. Discussion includes general guidelines for modeling biological systems at varying levels of fidelity, advantages, limitations, and applications of this research. The modeling methodology and the first approach for concept generation are illustrated by a continuous example of lichen.


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