Modeling and implementation of a neurofuzzy system for surface mount assembly defect prediction and control

A high-speed surface mount assembly can reduce both production cost and time; however, it could allow an enormous number of boards to be built before a problem is detected. Therefore, early detection and assessment of a surface mount assembly problem is critical for cost-effective manufacturing. This paper proposes a neurofuzzy system for surface mount assembly defect prediction and control. Hybrid data from both in-process quality control database and from a fractional factorial experimental design are collected for neurofuzzy learning and modeling. Customized programming codes are generated for rule retrieval and for graphical user interface modeling. The proposed system is successfully implemented at a surface mount assembly plant. It significantly improves plant throughput by the downtime reduction that is a result of a better defect prediction and control.