A Boosting-Based Intelligent Model for Stencil Cleaning Prediction in Surface Mount Technology

Abstract

This research proposes a stencil cleaning decision-making model in surface mount technology. Stencil cleaning is a critical process that influences the quality and efficiency of printing circuit boards. Stencil cleaning operation depends on various process variables, such as printing speed, printing pressure, and aperture shape. The objective of this research is to develop an intelligent model to guide stencil cleaning decision-making to reduce process defects. The stencil cleaning process is considered as a sequential detection problem in this study. Based on quality measures of printed historical boards, such as solder paste volume and the number of defects, a novel feature space is proposed by considering both short-term and long-term process trend. A gradient boosting model is applied to make the stencil cleaning decision. To validate the effectiveness of the proposed model, different scenarios are designed in the experimental test. State-of-art data mining models are also compared to the proposed cleaning decision-making model. Experimental results show that the proposed boosting-based intelligent model outperforms other models and can effectively provide the cleaning suggestion even the board design is changed in the future.