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Innovative parametric design for environmentally conscious adhesive dispensing process

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

Due to increasing environmental consciousness, the European Union has prohibited the use of lead substances in electronics soldering material. 58Bi/42Sn solder with a melting temperature of only (Formula Presented.)C helps achieve a lower process temperature to resolve the board warpage issue. Curing the adhesive simultaneously with solder reflow helps simplify the assembly process and reduces the manufacturing cost. When a low soldering temperature profile is used, the impact on the adhesion performance of the cured adhesive becomes a major concern. This research investigates the influence of adhesive dispensing process parameters on the shear strength of 0805 chip capacitors. Experimental data is analyzed using the principal component analysis (PCA) technique and PCA integrated with grey relational analysis algorithm. This study also proposes an innovative parametric design for artificial neural network (ANN) modeling for the multi-quality function problem to determine the optimal process scenarios. Results of the confirmation test indicate that the samples prepared using the process parameters identified by ANN are superior to the others. Thus, the optimal process parameters are adhesive dispense location beneath the component body, placement time of 0 s, a curing temperature of (Formula Presented.)C and a conveyor speed of 1 m/min. The implementation of the optimal process has improved chip capacitor fall-off from 2.5 to 0.88 %.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalJournal of Intelligent Manufacturing
Volume26
Issue number1
DOIs
StatePublished - Feb 2013

Bibliographical note

Publisher Copyright:
© 2013, Springer Science+Business Media New York.

Keywords

  • 58Bi/42Sn solder alloy
  • Adhesive dispensing
  • Artificial neural network
  • Electronics manufacturing
  • Innovative parametric design
  • Principal component analysis

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