A neurofuzzy-based quality-control system for fine pitch stencil printing process in surface mount assembly

Abstract

Surface mount assembly defect problems can cause significant production-time losses. About 60% of surface mount assembly defects can be attributed to the solder paste stencil printing process. This paper proposes a neurofuzzy-based quality-control system for the fine pitch stencil printing process. The neurofuzzy approach is used to model the nonlinear behavior of the stencil printing process. Eight control variables are defined for process planning and control, including stencil thickness, component pitch, aperture area, snap-off height, squeegee speed, squeegee pressure, solder paste viscosity, and solder paste particle size. The response variables are the volume and height of solder paste deposited. The values of the response variables provide indicators for identifying potential quality problems. A 38–3 fractional factorial experimental design is conducted to collect structured data to augment those collected from the production line for neurofuzzy learning and modeling. Visual basic programming language is then used for both rule retrieval and graphical-user-interface modeling. The effectiveness of the proposed system is illustrated through a real-world application.