A neuro-computing approach to the thermal profile control of the second-side reflow process in surface mount assembly

Tsung Nan Tsai, Ta-Ho Yang

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Purpose - A neural-network-based predictive model is proposed to model the second-side thermal profile reflow process in surface mount assembly with a view to facilitating the oven set-up procedure and improving production yield. Design/methodology/approach - This study performs a 38-4 fractional factorial experimental twice to collect the thermal-profile data from a second-side board. The first experiment has components on the second side only, while the second experiment also has additional components on the primary side. A back-propagation neural network (BPN) is then used to model the relationship between control variables and thermal-profile measures. Findings - Empirical results illustrate the efficiency and effectiveness of the proposed BPN in solving the second-side thermal-profile prediction and control problem. Originality/value - There is no study dedicated to the investigation of the second-side thermal-profile variance with and without the presence of primary-side components. The study suggests that a variant oven-setting strategy for the second-side reflow process is important to ensure reflow-soldering quality.

Original languageEnglish
Pages (from-to)343-359
Number of pages17
JournalJournal of Manufacturing Technology Management
Volume16
Issue number3
DOIs
Publication statusPublished - 2005 May 30

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Computer Science Applications
  • Strategy and Management
  • Industrial and Manufacturing Engineering

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