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

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 3^8-4 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.