The process modelling of epoxy dispensing for microchip encapsulation using fuzzy linear regression with fuzzy intervals

Ip, Connie; Kwong, C. K.; Bai, Hongliang; Tsim, Y. C.

doi:10.1007/s00170-002-1517-6

Cited by 21 publications

(11 citation statements)

References 9 publications

(8 reference statements)

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“…However, previous studies also found that the performance of a developed neural network is quite dependent on the training algorithm used, the neural network architectural design as well as on the setting of the neural network parameters. Ip, Kwong, Bai, and Tsim (2003) have attempted the modeling of fluid dispensing using fuzzy regression with fuzzy intervals. In Kwong's recent work, fuzzy regression-based process models for epoxy dispensing were developed by which optimal setting of process parameters can be determined with the use of fuzzy linear programming techniques (Kwong & Bai, 2005).…”

Section: Introductionmentioning

confidence: 99%

Takagi–Sugeno neural fuzzy modeling approach to fluid dispensing for electronic packaging

Kwong

Chan

Wong

2008

Expert Systems with Applications

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Section: Introductionmentioning

confidence: 99%

Takagi–Sugeno neural fuzzy modeling approach to fluid dispensing for electronic packaging

Kwong

Chan

Wong

2008

Expert Systems with Applications

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“…Based on P-FR, the estimated interval on the generated fuzzy linear model is influenced by all training data and the generated fuzzy linear model is robust in the presence of outliers. P-FR has been used in modelling the transfer moulding for microchip encapsulation in electronic packaging (Ip et al 2003b). (c) Chang's fuzzy regression (C-FR) (Chang 2001), which can generate fuzzy regression model in which the fuzzy coefficients can address both fuzziness and randomness of the experimental data.…”

Section: Implementation Of the Hybrid Swarm Intelligence Algorithmmentioning

confidence: 99%

Handling uncertainties in modelling manufacturing processes with hybrid swarm intelligence

Chan

Dillon

Kwong

2012

International Journal of Production Research

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Seldom has research regarding manufacturing process modelling considered the two common types of uncertainties which are caused by randomness as in material properties and by fuzziness as in the inexact knowledge in manufacturing processes. Accuracies of process models can be downgraded if these uncertainties are ignored in the development of process models. In this paper, a hybrid swarm intelligence algorithm for developing process models which intends to achieve significant accuracies for manufacturing process modelling by addressing these two uncertainties is proposed. The hybrid swarm intelligence algorithm first applies the mechanism of particle swarm optimisation to generate structures of process models in polynomial forms, and then it applies the mechanism of fuzzy least square regression algorithm to determine fuzzy coefficients on polynomials so as to address the two uncertainties, fuzziness and randomness. Apart from addressing the two uncertainties, the common feature in manufacturing processes, nonlinearities between process parameters, which are not inevitable in manufacturing processes, can also be addressed. The effectiveness of the hybrid swarm algorithm is demonstrated by modelling of the solder paste dispensing process.

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“…Lai and Chang [28] applied fuzzy linear regression to model the die casting process. Ip et al [16] used fuzzy linear regression to develop a process model for epoxy dispensing. Modeling of transfer molding using fuzzy linear regression was also reported by Ip et al [17].…”

Section: Introductionmentioning

confidence: 99%

Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers

Chan¹,

Kwong²,

Fogarty³

2010

Information Sciences

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a b s t r a c tFuzzy regression (FR) been demonstrated as a promising technique for modeling manufacturing processes where availability of data is limited. FR can only yield linear type FR models which have a higher degree of fuzziness, but FR ignores higher order or interaction terms and the influence of outliers, all of which usually exist in the manufacturing process data. Genetic programming (GP), on the other hand, can be used to generate models with higher order and interaction terms but it cannot address the fuzziness of the manufacturing process data. In this paper, genetic programming-based fuzzy regression (GP-FR), which combines the advantages of the two approaches to overcome the deficiencies of the commonly used existing modeling methods, is proposed in order to model manufacturing processes. GP-FR uses GP to generate model structures based on tree representation which can represent interaction and higher order terms of models, and it uses an FR generator based on fuzzy regression to determine outliers in experimental data sets. It determines the contribution and fuzziness of each term in the model by using experimental data excluding the outliers. To evaluate the effectiveness of GP-FR in modeling manufacturing processes, it was used to model a non-linear system and an epoxy dispensing process. The results were compared with those based on two commonly used FR methods, Tanka's FR and Peters' FR. The prediction accuracy of the models developed based on GP-FR was shown to be better than that of models based on the other two FR methods.

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The process modelling of epoxy dispensing for microchip encapsulation using fuzzy linear regression with fuzzy intervals

Cited by 21 publications

References 9 publications

Takagi–Sugeno neural fuzzy modeling approach to fluid dispensing for electronic packaging

Takagi–Sugeno neural fuzzy modeling approach to fluid dispensing for electronic packaging

Handling uncertainties in modelling manufacturing processes with hybrid swarm intelligence

Modeling manufacturing processes using a genetic programming-based fuzzy regression with detection of outliers

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