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.
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.
Empirical results illustrate the efficiency and effectiveness of the proposed BPN in solving the second‐side thermal‐profile prediction and control problem.
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.
