The extraction of welding type for body in white based on association rules

To extract the reusable process knowledge of body in white (BIW) from process data, the association rule is employed to capture typical welding type. An association rule model for typical welding type acquisition is established. The attributes related to welding type are classified and quantitative attributes are partitioned into several intervals. Apriori algorithm is applied to extract the frequent itemsets. The strong rules are generated according to the threshold of confidence. Finally, a computational example mining typical welding process is analyzed. The result indicates that the approach can capture typical welding type effectively.