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

Kwong, C. K.; Chan, Kit Yan; Wong, Hie Hua

doi:10.1016/j.eswa.2007.02.035

Cited by 19 publications

(10 citation statements)

References 10 publications

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“…Takagi, Sugeno and Kang in [29,30] presented a mathematical tool to procure a fuzzy model of a system. The fuzzy model is based on rules usually presenting the following structure: 1 1 If is and and is Then is ,…”

Section: Fuzzy Compression System Modelmentioning

confidence: 99%

A Numerical structural approach to surge detection and isolation in compression systems using fuzzy logic controller

Hafaifa

Laaouad

Laroussi

2011

Int. J. Control Autom. Syst.

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Fuzzy control is a practical alternative for a variety of challenging control applications since it provides a convenient method for constructing nonlinear controllers via the use of heuristic information. This approach provides a formal methodology for representing, manipulating, and implementing a human's heuristic knowledge about how to modeled and to control a system. This paper focuses on the application of fuzzy control approach to the surge phenomena in centrifugal compression system. Fuzzy controller is designed to consist of an active surge control and phase control without any explicit system models, but driven in the human thinking mechanism. This fuzzy control methodology suggested in this work reproduced well the main characteristics of the turbo compressor dynamic model developed by Moore and Gretzer and give place to a more precise and easy to handle representation. Application and simulation results are performed to demonstrate the effectiveness of this controller. Studies show that, the proposed controller provides good tracking control performance for the compression system at different operating conditions and moreover improves the dynamic performances of the system.

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Section: Fuzzy Compression System Modelmentioning

confidence: 99%

A Numerical structural approach to surge detection and isolation in compression systems using fuzzy logic controller

Hafaifa

Laaouad

Laroussi

2011

Int. J. Control Autom. Syst.

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“…Various modelling approaches have been introduced to develop epoxy dispensing models based on experimental data. Statistical regression, neural networks [7] and fuzzy neural networks [8] have been employed for modelling epoxy dispensing processes.…”

Section: Introductionmentioning

confidence: 99%

Modeling of epoxy dispensing process using a hybrid fuzzy regression approach

Chan

Kwong

2012

Int J Adv Manuf Technol

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In the semiconductor manufacturing industry, epoxy dispensing is a popular process commonly used in die-bonding as well as in microchip encapsulation for electronic packaging. Modelling the epoxy dispensing process is important because it enables us to understand the process behaviour, as well as determine the optimum operating conditions of the process for a high yield, low cost and robust operation. Previous studies of epoxy dispensing have mainly focused on the development of analytical models. However, an analytical model for epoxy dispensing is difficult to develop, because of its complex behaviour and high degree of uncertainty associated with the process in a real world environment. Previous studies of modelling the epoxy dispensing process have not addressed the development of explicit models involving high order and interaction terms, as well as fuzziness between process parameters. In this paper, a hybrid fuzzy regression (HFR) method integrating fuzzy regression with genetic programming is proposed to make up the deficiency. Two process models are generated for the two quality characteristics of the process, encapsulation weight and encapsulation thickness based on the HFR, respectively. Validation 2 tests are performed. The performance of the models developed based on the HFR outperforms the performance of those based on statistical regression and fuzzy regression.

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“…In [18], Zadeh presented the concepts of linguistic variables and fuzzy rules to approximate reasoning. In recent years, some forecasting methods have been presented based on fuzzy rules [2], [4], [5], [7], [10], [12], [13], [15].…”

Section: Introductionmentioning

confidence: 99%

Temperature prediction based on fuzzy clustering and fuzzy rules interpolation techniques

Chang

Chen

2009

2009 IEEE International Conference on Systems, Man and Cybernetics

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In this paper, we present a new method to deal with temperature prediction based on fuzzy clustering and fuzzy rules interpolation techniques. First, the proposed method constructs fuzzy rules from training samples based on the fuzzy C-Means clustering algorithm, where each fuzzy rule corresponds to a cluster and the linguistic terms appearing in the fuzzy rules are represented by triangular fuzzy sets. Then, it performs fuzzy inference based on the multiple fuzzy rules interpolation scheme, where it calculates the weight of each fuzzy rule with respect to the input observation based on the defuzzified values of triangular fuzzy sets. Finally, it uses the weight of each fuzzy rule to calculate the forecasted output. We also apply the proposed method to handle the temperature prediction problem. The experimental result shows that the proposed method gets higher average forecasting accuracy rates than Chen and Hwang's method [7].

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

Cited by 19 publications

References 10 publications

A Numerical structural approach to surge detection and isolation in compression systems using fuzzy logic controller

A Numerical structural approach to surge detection and isolation in compression systems using fuzzy logic controller

Modeling of epoxy dispensing process using a hybrid fuzzy regression approach

Temperature prediction based on fuzzy clustering and fuzzy rules interpolation techniques

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