Verifying experiment for automated design of mechatronic systems using Bond-Graph modelling and simulation and genetic programming

2008 ◽  
Vol 32 (3) ◽  
pp. 173
Author(s):  
M. Afzaal Malik ◽  
Saheeb A. Kayani
Keyword(s):  
Genetic Programming ◽  
Automated Design ◽  
Bond Graph ◽  
Modelling And Simulation ◽  
Mechatronic Systems

Some Key Issues in Using Bond Graphs and Genetic Programming for Mechatronic System Design

2001 ◽  
Author(s):  
R. C. Rosenberg ◽  
E. D. Goodman ◽  
Kisung Seo
Keyword(s):  
Genetic Programming ◽  
System Design ◽  
Design Space ◽  
Bond Graph ◽  
Mechatronic System ◽  
Bond Graphs ◽  
Mechatronic Systems ◽  
Energy Behavior ◽  
Key Issues ◽  
Bond Graph Modeling

Abstract Mechatronic system design differs from design of single-domain systems, such as electronic circuits, mechanisms, and fluid power systems, in part because of the need to integrate the several distinct domain characteristics in predicting system behavior. The goal of our work is to develop an automated procedure that can explore mechatronic design space in a topologically open-ended manner, yet still find appropriate configurations efficiently enough to be useful. Our approach combines bond graphs for model representation with genetic programming for generating suitable design candidates as a means of exploring the design space. Bond graphs allow us to capture the common energy behavior underlying the several physical domains of mechatronic systems in a uniform notation. Genetic programming is an effective way to generate design candidates in an open-ended, but statistically structured, manner. Our initial goal is to identify the key issues in merging the bond graph modeling tool with genetic programming for searching. The first design problem we chose is that of finding a model that has a specified set of eigenvalues. The problem can be studied using a restricted set of bond graph elements to represent suitable topologies. We present the initial results of our studies and identify key issues in advancing the approach toward becoming an effective and efficient open-ended design tool for mechatronic systems.

Demonstrating the Generation of Bond Graphs From 3D Assemblies

2020 ◽  
Author(s):  
Corey J. Alicchio ◽  
Justin S. Vitiello ◽  
Pradeep Radhakrishnan
Keyword(s):  
Bond Graph ◽  
Dynamic Responses ◽  
Bond Graphs ◽  
Mechatronic Systems ◽  
3D Cad ◽  
Related Information ◽  
Grammar Rules ◽  
Gear Trains ◽  
System Graph ◽  
Future Work

Abstract The bond graph method provides a generic and simple way to compute differential equations and dynamic responses for complex mechatronic systems. This paper will illustrate the process of automatically generating bond graphs from 3D CAD assemblies of gear-trains. Using appropriate CAD application programming interfaces (APIs), information on parts and mates within an existing assembly is extracted. The extracted information is stored as an identity graph, which also stores all geometry and mass related information of every part. Grammar rules are then used to transform the identity graph to a system graph, which is then converted to bond graph using an existing bond graph generation program. The paper will discuss the process, challenges and planned future work.

A System Framework With Online Monitoring and Evaluation for Design Evolution of Engineering Systems

2010 ◽  
Vol 10 (3) ◽  
Author(s):  
L. B. Gamage ◽  
C. W. de Silva
Keyword(s):  
Genetic Programming ◽  
Domain Knowledge ◽  
Design Space ◽  
Graph Model ◽  
Bond Graph ◽  
Design Solution ◽  
Engineering Systems ◽  
Design Evolution ◽  
Bond Graph Modeling ◽  
Machine Health Monitoring

This paper presents a methodology for the design evolution of engineering systems, with a mechatronic emphasis. The developed approach specifically integrates machine health monitoring and an expert system and carries out the design evolution of a multidomain dynamic system using bond graph modeling and genetic programming. The evolution of a bond graph model of a mechatronic system through genetic programming enables the exploration of the design space, thereby generating a global optimum design solution in an automated manner. Domain knowledge and expertise are used to control the design exploration and to restrict it only to a meaningful design space. As an illustrative example, the developed methodology is applied to redesign the electrohydraulic manipulator of an existing industrial fish processing machine.

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