Modeling of Soldering Quality by Using Artificial Neural Networks

Multilayer perceptrons (MLPs ) are well-known artificial neural networks (ANNs) that are used in many different applications. In this paper, MLP neural networks were used to predict product quality in a wave soldering research case. The aims were to construct process models and to determine whether the formation of soldering defects could be predicted reliably by using the method. In addition, the scope of the research included demonstrating the prediction performance of the created models. A MLP-based variable selection procedure with a back-propagation algorithm was used to create defect formation models and to find the most important factors affecting the number of detected defects. The process parameters were used as inputs for the MLP network and each defect type in turn as a model output. In conclusion, the results were promising, and the method used showed potential considering the wider use of the data processing procedure in the electronics or any other industry.