Stencil Printing Optimization using a Hybrid of Support Vector Regression and Mixed-integer Linear Programming

Abstract

This research proposes an optimization approach to enhance the stencil printing process (SPP) in surface mount printed circuit board (PCB) assembly. Stencil printing behavior is affected by many variables including stencil design, solder paste composition, squeegee speed and pressure, and other environmental conditions. In this research, support vector regression (SVR) model is trained to capture the complex relationships among these variables, based on historical data. A mixed-integer linear programming (MILP) model is proposed to minimize the total absolute predicted deviation of average volume transfer from target. The optimal printing settings are retrieved for different sample problems with low computational cost.