Modeling of the Fluid Volume Transferred in Contact Dispensing Processes

Chen, Daniel; Li, M.G.; Cao, N.

doi:10.1109/tepm.2009.2020515

Cited by 14 publications

(5 citation statements)

References 11 publications

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“…The contact dispensing process is typically composed of four stages, namely, extrusion, deposition, stretching, and breakage (Chen et al, 2009;Liu et al, 2022). The dispensing tip first approaches the substrate, and the needle gradually approaches the substrate to allow the conductive adhesive to contact the substrate.…”

Section: Open Accessmentioning

confidence: 99%

“…They found that increasing the lifting speed of the needle led to an increase in the radius of the droplet on the substrate. Chen et al (2009) showed that when the effect of shear rate on apparent viscosity was taken into account, the accuracy of dispensing volume was significantly improved. Moreover, some studies have been conducted to improve the contact dispensing process.…”

Section: Open Accessmentioning

confidence: 99%

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Modeling the contact dispensing process of conductive adhesives with different viscosities and optimization of droplet deposition

et al. 2023

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Introduction: The contact dispensing process is composed of extrusion, stretching, and liquid bridge breakage, which is greatly impacted by the viscosity and surface tension of the dispensed liquid and the contact angle between the liquid and the substrate. Regarding contact dispensing of conductive adhesives, few studies have investigated the influence of the viscosity of conductive adhesives on the dispensing process.Methods: In the present study, computer simulation was used to explore the contact dispensing process of high-viscosity adhesives, and a dispensing device was designed to verify the simulation results.Results and discussion: The results showed that the viscosity of the adhesives had the greatest influence on the dispensing process, and the higher the viscosity, the more difficult it was to break the liquid bridge, which seriously affected the stability of the contact dispensing process. In the dispensing process, once the adhesive had filled the gap between the needle tip and the substrate, increasing the dispensing time caused the diameter of the droplet to increase. Decreasing the lifting speed of the needle allowed sufficient time for the adhesive surface to restore changes caused by stretching, thereby eliminating unstable droplets and achieving droplets with regular morphology. In conclusion, our results demonstrated that computer simulation is a powerful tool for providing key information to improve the contact dispensing process, obtain droplets with optimal morphology, and achieve excellent bonding between the conductive adhesive and the substrate.

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Section: Open Accessmentioning

confidence: 99%

Section: Open Accessmentioning

confidence: 99%

Modeling the contact dispensing process of conductive adhesives with different viscosities and optimization of droplet deposition

et al. 2023

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“…The adhesive will start to leak into the crevice of the hole and spread around the glass tube once the dispensing needle touches the silicon substrate or the glass tube because of the wetting phenomenon. Before the dispensing needle touches the silicon substrate or the glass tube, the adhesive will cling to the tip of the needle firmly due to strong surface tension when the diameter of the adhesive at the tip of the needle is very small [16,17] .…”

Section: Adhesive Dispensing Controlmentioning

confidence: 99%

Realization of an Automated Microassembly Task Involving Micro Adhesive Bonding

Zhang

et al. 2013

Int. J. Autom. Comput.

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An automated approach is proposed for a microassembly task, which is to insert a 10 µm diameter glass tube into a 12 µm diameter hole in a silicon substrate, and bond them together with ultraviolet (UV) curable adhesive. Two three-degree-of-freedom micromanipulators are used to move the glass tube and the dispensing needle, respectively. Visual feedback is provided by an optical microscope. The angle of the microscope axis is precisely calibrated using an autofocus strategy. Robust image segmentation method and feature extraction algorithm are developed to obtain the features of the hole, the glass tube and the dispensing needle. Visual servo control is employed to achieve accurate aligning for the tube and the hole. Automated adhesive dispensing is used to bond the glass tube and the silicon substrate together after the insertion. On-line monitoring ensures that the diameter of the adhesive spot is within a desired range. Experimental results demonstrate the effectiveness of the proposed strategy.

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“…8 and 9, respectively a (φ) = 0.0003φ + 0.0014 Step 7: Investigate the relationship between the proexponential factor and the curing agent ratio. Considering the function of pro-exponential factor, C(T ), in Table II, the linear relationship between the parameter slope "a " and the intersection "b" of C, and the curing agent ratio φ are shown in (8) and (9) and Figs. 10 and 11, respectively…”

Section: (A)-(d)mentioning

confidence: 99%

“…Chen et al [9] developed a model to investigate the fluid volume extruded from the needle in a dispensing process. Li et al [10] presented a conceptual approach to integrate the design and control methodology for complex processes with specific application in an epoxydispensing control system.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Parametric Design for a Robust LED Encapsulation Process

Huang

2012

IEEE Trans. Compon., Packag. Manufact. Technol.

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Light-emitting diodes (LEDs) have been adopted in a wide variety of lighting situations. As lighting technology evolves, illumination devices are moving toward miniaturization and high-power applications. Packaging of illumination devices is essential to providing a path for heat dispersion. A full understanding of the viscosity change of the encapsulation material is critical to ensure manufacturability and improved process yield. The typical viscosity model is the initial viscosity plus the change in viscosity over time at a given temperature. The change in viscosity is a function of the reaction rate and the pro-exponential factor. This paper includes the pro-exponential factor in the regression model to improve the prediction accuracy. Through experimental data, the material's viscosity is determined as a function of process temperature, the ratio of the curing agent, and the curing time. A computer-aided parametric design is used to assess the effect of the variation of individual factors on the quality function and proposes a robust LED encapsulation process. An artificial neural network that considers interactions between parameters and establishes fitness functions is used to further enhance the accuracy of predictions and determine the optimal process solution through an iterative training algorithm. The optimal process scenario suggested by this research is a 10% curing agent preheated at 333°K for 40 min. This robust process can improve the signal-to-noise ratios by approximately 43%.Index Terms-Artificial neural network, computer-aided parametric design, light-emitting diode (LED) encapsulation, viscosity model.

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Modeling of the Fluid Volume Transferred in Contact Dispensing Processes

Cited by 14 publications

References 11 publications

Modeling the contact dispensing process of conductive adhesives with different viscosities and optimization of droplet deposition

Modeling the contact dispensing process of conductive adhesives with different viscosities and optimization of droplet deposition

Realization of an Automated Microassembly Task Involving Micro Adhesive Bonding

Intelligent Parametric Design for a Robust LED Encapsulation Process

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