Three‐dimensional simulation of microchip encapsulation process

Han, Ruihua; Shi, Linhuo; Gupta, Mahesh

doi:10.1002/pen.11207

Cited by 34 publications

(13 citation statements)

References 14 publications

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“…Physical models, which establish a physical understanding of the manufacturing process and deploy the various physical laws, can be used to represent these relationships between process variables and process responses for manufacturing processes such as epoxy dispensing (Li et al 2001, Chen 2002, injection moulding process (Chiang et al 1991), solder paste dispensing process (Geren and Ekere 1994) and transfer moulding process (Han et al 2000). However, physical models usually consist of partial differential equations with respect to both process variables and process responses, which may not be developed easily due to the complex behaviour of certain manufacturing processes.…”

Section: Introductionmentioning

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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Section: Introductionmentioning

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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“…This can be achieved by developing appropriate physical models to represent the manufacturing process. Physical models [5,9,12,29] are based on a physical understanding of the process, and they typically consist of a set of governing partial differential equations. They are attractive because they provide a fundamental understanding of the relationships between the input and output parameters.…”

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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“…On establishing the three‐dimensional CAE simulation program, various attempts to predict the actual processing condition in injection molding were reported. Attention has been primarily paid to the analysis of the melt temperature distributions, pressure generated during mold filling, and velocity of the melt injected . Undeniably, it is crucial to determine the rheological data in order to observe the behavior of polymeric materials.…”

Section: Introductionmentioning

confidence: 99%

Viscosity analysis of polypropylene-kaolin composites measured using single-screw extruder

Rahim

Ariff

Ariffin

et al. 2014

J Vinyl Addit Technol

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Deviations between data for the apparent and true viscosity measurement are commonly observed in the rheological studies of thermoplastics. To validate the significance on the usage of these data on the processing side, filling analysis for the apparent viscosity and true viscosity of polypropylene‐kaolin (PP‐kaolin) composites was conducted by using CadMold® software on the dumbbell Computer Aided Design model for comparative purposes. The raw apparent viscosity data were generated from a single‐screw extruder by using different sets of die geometries. Bagley and Rabinowitsch corrections were implemented to get a corrected set of true viscosity measurements. It was found that the true viscosity curves are lower compared with the apparent viscosity curves of PP‐kaolin. By constructing the corresponding data from PP‐kaolin composite rheological results, it was discovered that the high viscosity of the apparent viscosity melt data has shown a large variation in the mold distributions filling temperature, reduces the total pressure loss and melt shear stress, and increases the melt velocity during mold filling. Although the corrections were made on the calculated results, however, the significance is relatively small and it does not prove the changes in the overall physical property of the composite. Therefore, correcting the data by using the Bagley and Rabinowitsch corrections is not necessary in mold‐filling analysis. J. VINYL ADDIT. TECHNOL., 20:275–283, 2014. © 2014 Society of Plastics Engineers

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Three‐dimensional simulation of microchip encapsulation process

Cited by 34 publications

References 14 publications

Handling uncertainties in modelling manufacturing processes with hybrid swarm intelligence

Handling uncertainties in modelling manufacturing processes with hybrid swarm intelligence

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

Viscosity analysis of polypropylene-kaolin composites measured using single-screw extruder

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