Thermal parameters optimization of a reflow soldering profile in printed circuit board assembly: A comparative study

摘要

This paper presents a comparative study for optimizing the thermal parameters of the reflow soldering process using traditional and artificial intelligence (AI) approaches. High yields in the reflow soldering process are essential to a profitable printed circuit board (PCB) assembly operation. A reflow thermal profile is a time-temperature graph which is used to properly control the thermal mass and heat distribution to form robust solder joints between the PCB and electronics components during reflow soldering. An inhomogeneous temperature distribution for a reflow thermal profile can cause various soldering defects, which can jeopardize product reliability and lead to significant productivity loss. In the multi-objective optimization problem, three alternative optimization methods are discussed and compared: response surface methodology (RSM), nonlinear programming (NLP), and a hybrid AI technique. A dataset was gathered using a 3~(8-4) experimental design for the development of meta-models through response surface quadratic modeling. In the first method, RSM is used to acquire the optimal heating parameters, while in the second method NLP is used to derive a global solution based on the meta-models. The back-propagation neural network (BPN) is used in the third method to formulate the nonlinear relationship between the heating inputs and responses. A genetic algorithm (GA) is then used to elicit the optimal heating parameters from the established BPN model. The evaluation results show that all three methods provide satisfactory soldering performance in terms of the process capability, sigma level, and process window indices (PWIs). Particularly, the hybrid AI approach provides superior nonlinear formulation capability and optimization performance.