Surface mount adhesive: in search of a perfect dot
Surface mount adhesives are used to hold electronic components on printed circuit boards or substrates. When dispensed onto a surface, the adhesive dots have to meet distinctive geometric requirements, such as dot diameter and height. The present paper provides an insight into how the manipulation o...
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Main Authors: | , , , |
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Format: | Article |
Published: |
Springer Verlag
2017
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Subjects: | |
Online Access: | http://eprints.um.edu.my/22879/ https://doi.org/10.1007/s00170-016-9549-5 |
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Institution: | Universiti Malaya |
Summary: | Surface mount adhesives are used to hold electronic components on printed circuit boards or substrates. When dispensed onto a surface, the adhesive dots have to meet distinctive geometric requirements, such as dot diameter and height. The present paper provides an insight into how the manipulation of three key components in the formulation of a surface mount adhesive can affect the characteristics of the dispensed dot. In the search for a formulation that yields perfect dots, the rheological and time-pressure dispensing characteristics of 12 adhesive samples of different formulations based on a mixture design were investigated. All formulations were subject to viscosity and dispensing tests. From the test results, the adhesive samples were found to be shear thinning and thixotropic. The break-up length, dot diameter and volume were found to decrease with increasing viscosity, while the dot height showed otherwise. From the regression of data, most of the responses can be correlated with a linear model to the composition of the adhesive samples. Increasing component C3 has a significant positive effect on the viscosity, break-up length, dot diameter and volume compared to component C2, while increasing component C1 has a negative effect on the responses. The adhesive sample, which was found to approach ‘perfection’, has the formulation of C1 = 0.6, C2 = 0.32, and C3 = 0.08. This finding agrees with the optimal formulation calculated by the optimization of the responses. |
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