Design evolution of engineering systems using bond graphs and genetic programming. (February 2016)

• Record Type: Journal Article
• Authors: Samarakoon, Buddhika L.; Gamage, Lalith B.; de Silva, Clarence W.
• Is Part Of: Mechatronics. Volume 33(2016)
• Page Start: 71
• Page End: 83
• Publication Date: 2016-02
• Subjects: Genetic programming -- Automated design optimization -- Modeling -- Mechatronics; Computer integrated manufacturing systems -- Periodicals; Flexible manufacturing systems -- Periodicals; Mechatronics -- Periodicals; Productique -- Périodiques; Fabrication, Systèmes flexibles de -- Périodiques; Mécatronique -- Périodiques; Computer integrated manufacturing systems; Flexible manufacturing systems; Mechatronics; Periodicals; 629.89
• DOI: 10.1016/j.mechatronics.2015.09.009
• Languages: English
• ISSNs: 0957-4158

Abstract

Abstract: This paper presents a scheme of evolutionary design optimization, which integrates modeling with bond graphs and optimization using genetic programming for multi-domain engineering systems, particularly mechatronic systems. The performance of the developed system is studied using both experimentation and simulation. During the evolutionary optimization, in addition to the desired response error, system complexity is also taken into account. For the experimental study, the method is implemented in an industrial fish processing machine at the Industrial Automation Laboratory of the University of British Columbia, and the obtained results for suggested design modifications are studied and tested. The drawbacks of the fitness calculation methodologies that are presented in literature are identified and improved fitness functions are developed for evolutionary design in the present work. While previous work has investigated the integration of bond graphs and genetic programming for designing an engineering system, the present work specifically addresses the application of the developed method for the design improvement of an industrial machine. The proposed method is applicable particularly to existing engineering systems, first because the initial model can be tested by comparing its simulated results with the corresponding results from the actual physical system, and second because the design improvements as suggested by the evolutionary design framework, which is developed in the present work, may be implemented and tested against the behavior of the corresponding model.