Automated synthesis of mechanical vibration absorbers using genetic programming

Hu, Jianjun; Goodman, Erik D.; Li, Shaobo; Rosenberg, Ronald

doi:10.1017/s0890060408000140

Cited by 11 publications

(6 citation statements)

References 22 publications

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“…The BG-GP methodology was also widely employed in designing vibration absorbers. Using the BG-GP methodology [Hu et al, 2008] have obtained results that are comparable with those from conventional design methodologies, for vibration absorbers. Nonetheless, much of the BG-GP work presented in the literature have not been tested with real world industrial machines.…”

Section: Bond Graph and Genetic Programmingmentioning

confidence: 75%

Design evolution of engineering systems using bond graphs and genetic programming

2016

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Engineering design is a complex task, which typically involves multiple physical domains. It can benefit from a modeling tool that can represent different domains in a unified manner. For complex designs, optimization by traditional techniques (such as gradient-based methods) may not be appropriate. Evolutionary methodologies may be used in design optimization of complex engineering systems. This research is based on a framework for evolutionary design system consisting of a machine health monitoring system, model-based evolutionary design optimization system, and a design expert system. The design weaknesses and faults of an existing engineering system are identified using a machine health monitoring system. Then under supervision of the design expert system, an optimal design is evolved using genetic programming. This thesis primarily addresses the modeling and evolutionary aspects and their integration. The thesis develops the integrated system consisting of bond-graph modeling and genetic programming. The performance of the developed system is studied using both experimentation and simulation. The drawbacks of the fitness calculation methodologies that are presented in literature are identified and improved fitness functions are developed in the present work. A methodology to automatically obtain the state-space model of a system represented using bond graphs is also developed. While previous researchers have investigated the integration of bond graphs and genetic programming in design, they have not applied the method in a real engineering system. The present work specifically addresses the application of the developed method for design improvement for an industrial machine. For this purpose a linear bond graph model of the industrial fish processing machine is developed and the parameter values are identified using genetic programming. The design of the actual system is modified according to the evolved bond graph model and the results are validated using the data from the actual engineering iii system. The proposed method is applicable particularly to actual systems, first because the initial model can be tested by comparing its simulated results with the corresponding results from the actual system, and second because the design improvements as suggested by the evolutionary design framework may be implemented and tested against the behaviour of the corresponding model.

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Section: Bond Graph and Genetic Programmingmentioning

confidence: 75%

Design evolution of engineering systems using bond graphs and genetic programming

2016

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“…The aim of GP is to find out the member or offspring that best solves the problem (Koza et al ., 2008). Some successful applications of GP have been reported in product design (Lee and Tang, 2009), learning parametric design (Matthews et al ., 2006), human-competitive design challenge (Spector, 2008), mechanical vibration absorbers (Hu et al ., 2008), quantum computing circuits (Spector and Klein, 2008), and creativity in automated design (Koza et al ., 2004).…”

Section: Intelligent Optimization Proceduresmentioning

confidence: 99%

Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2

Pourmostaghimi

Zadshakoyan

Badamchizadeh

2020

AIEDAM

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This paper proposes an intelligent model-based optimization methodology for optimizing the production cost and material removal rate subjected to surface quality constraint in turning operation of hardened AISI D2. Unlike traditional approaches, this paper deals with finding optimum cutting parameters considering the real condition of the cutting tool. Tool flank wear is predicted by the model obtained using genetic programming. On the basis of the predicted flank wear value, the surface roughness of work piece is estimated by neural networks. Applying the particle swarm optimization algorithm, the optimum machining parameters are determined. The simulation and experimental results show that machining with proposed intelligent optimization methodology has higher efficiency than conventional techniques with constant optimized cutting parameters.

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“…Beyond modeling there is a large number of tasks where the correct, or even good, solutions are not known, but need to be discovered. For instance designing engineering solutions that perform well at low costs, or web pages that serve the users well, or even growth recipes for agriculture in controlled greenhouses are all tasks where human expertise is scarce and good solutions difficult to come by [5,[12][13][14]25]. Methods for machine creativity have existed for decades.…”

Section: Introductionmentioning

confidence: 99%

Creative AI Through Evolutionary Computation: Principles and Examples

Miikkulainen

2021

SN COMPUT. SCI.

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The main power of artificial intelligence is not in modeling what we already know, but in creating solutions that are new. Such solutions exist in extremely large, high-dimensional, and complex search spaces. Population-based search techniques, i.e. variants of evolutionary computation, are well suited to finding them. These techniques make it possible to find creative solutions to practical problems in the real world, making creative AI through evolutionary computation the likely "next deep learning."

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Automated synthesis of mechanical vibration absorbers using genetic programming

Cited by 11 publications

References 22 publications

Design evolution of engineering systems using bond graphs and genetic programming

Design evolution of engineering systems using bond graphs and genetic programming

Intelligent model-based optimization of cutting parameters for high quality turning of hardened AISI D2

Creative AI Through Evolutionary Computation: Principles and Examples

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