Accurate estimation of the peel force of the hot-air soldering ribbon interconnects has been recognized as an important issue for the combined tabber/ stringer (CTS) soldering process. Although there are empirical formulas available for quality of adhesive interconnects estimation, but their performances are not all satisfactory due to the complicated nature of the soldering process and the data availability. For this purpose, artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models were developed to estimate force of solder ribbon interconnects on silicon solar cells is implemented. The solderability and quality of the solar cell interconnection zone is an important criterion which has to be ensured. Pulling of ribbons from pre-damaged cells leads to large silicon disruptions. Therefore, instead of testing the solder interconnection, the ribbon peel force test of the solar cells is estimated. The paper focuses on a development of an innovative ANFIS estimator, evaluation of the test method and results for the interconnection quality. The result also indicated that the ANFIS estimator could evaluate the output response in high prediction accuracy even using limited training data.
Global Perspective on Engineering Management – World Academic Publishing Co.
Published: Aug 30, 2012
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