Fuzzy logic and neural networks for design of process parameters: A grinding process application

Chen, Y.-T.; Kumara, S.R.T.

doi:10.1080/002075498193804

Cited by 27 publications

(8 citation statements)

References 7 publications

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“…The process was then modelled using ANNs, fuzzy sets, standard multiple regression methods and deterministic methods which were finally optimised using genetic algorithms (GAs). Chen and Kumara (1998) developed an advisory system to optimise surface grinding processes using fuzzy and neural networks. They first built a fuzzy optimiser, and used the trajectory it generated to train the corresponding neural optimiser.…”

Section: Introductionmentioning

confidence: 99%

Neural modelling, control and optimisation of an industrial grinding process

Govindhasamy

McLoone

Irwin

et al. 2005

Control Engineering Practice

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This paper describes the development of neural model-based control strategies for the optimisation of an industrial aluminium substrate disk grinding process. The grindstone removal rate varies considerably over a stone life and is a highly nonlinear function of process variables. Using historical grindstone performance data, a NARX-based neural network model is developed. This model is then used to implement a direct inverse controller and an internal model controller based on the process settings and previous removal rates. Preliminary plant investigations show that thickness defects can be reduced by 50% or more, compared to other schemes employed. r

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Section: Introductionmentioning

confidence: 99%

Neural modelling, control and optimisation of an industrial grinding process

Govindhasamy

McLoone

Irwin

et al. 2005

Control Engineering Practice

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“…Where Δh i -ceramic element height deviation on the ith grinding wheel, f Δh -function determining the relationship (16) between the grinding parameters and the deviation of grinding elements height.…”

Section: Assumptions Of the Optimization Of The Process Of Sequentialmentioning

confidence: 99%

“…The use of fuzzy logic methods allows for ambiguity and uncertainty in the description of the analyzed phenomena. The paper [16] presents the use of the fuzzy logic methods in the procedure for parameters selection of the surface grinding process. The developed approach enabled the determination of a set of optimal design variables in order to achieve a set of desired process variables.…”

Section: Introductionmentioning

confidence: 99%

Optimization of sequential grinding process in a fuzzy environment using genetic algorithms

Lipiński

Kacalak

Bałasz

2019

J Braz. Soc. Mech. Sci. Eng.

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The paper presents the methodology of optimization of the sequential grinding process with the application of fuzzy logic for the definition of objectives and constraints imposed on the machining process. The presented method includes the succession of several subsequent operations and the dimensional and shape inaccuracies between them. The use of the fuzzy sets theory enabled the definition of not only the space of expectable solutions, but also the space of acceptable solutions for which the goals and limits imposed on the grinding process are partially met. The presented methodology was used to optimize the process of sequential grinding of small ceramic elements (corundum ceramics with Al 2 O 3 content of 92-99%.) The definition of fuzzy objective and constraints in the process of sequential grinding of small ceramics elements was proposed. The influence of the speed of the rotary grinding table and the machining allowance on the deviation of the flatness and height of the grinding elements and the value of the component of the normal grinding force were determined. Using the developed relationships, the definition of fuzzy objectives and constraints defined in the process output parameter space was transferred to the process parameter set space. In such a defined space, the optimization process was carried out using the genetic algorithm. The analysis of the impact of the applied t-norm functions used for aggregation of the fuzzy objective and constraints on the obtained results was performed. It was shown that in the case of sequential grinding of small ceramic elements, the use of minimum t-norm for an aggregation of grinding objective and constraints allows to achieve the highest process efficiency.

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“…Kamatala, Baumgartner, and Moon (1996) develop a fuzzy set theory-based system for predicting surface roughness in a finished turning operation. Chen & Kumara (1998) use a hybrid approach of fuzzy set and ANN-based technique for designing a grinding process and its control. Hashmi, El Baradie, and Ryan (1998) apply fuzzy set theory logic for selection of cutting conditions in machining.…”

Section: Fuzzy Set Theory-based Modellingmentioning

confidence: 99%