The intelligent design of reflow soldering based on an improved high-dimensional surrogate assisted optimization method

Abstract

The Efficient Global Optimization (EGO) optimizes the temperature curve of the reflow soldering process by constructing the Kriging model. Compared with the traditional Surrogate Assisted Optimization (SAO), it can meet two different requirements: exploitation of optima and exploration of uncertain areas. However, due to the inversion of the covariance correlation matrix and the solving of Kriging-related parameters, the Kriging approximation process for high-dimensional problems is time consuming and even impossible to construct. For this reason, a High-Dimensional Kriging Modeling method through Linear Discriminant Analysis (HDKRG-LDA) method is proposed by considering the correlation parameters and the design variables of a Kriging model. It uses LDA to transform a high-dimensional correlation parameter vector in Kriging into low-dimensional one, which is used to reconstruct a new correlation function. In this study, the HDKRG-LDA method assisted EGO algorithm is applied to optimize the temperature curve of the reflow soldering process. In the optimization results of the reflow soldering, the method can effectively predict the temperature curve, and provide guidance for the production process of the reflow soldering.