Quick and precise clustering of arbitrarily shaped flat patterns based on stringy effect

Grouping a given number of arbitrarily shaped flat patterns to form a cluster which occupies minimal-area convex enclosure is very useful in solving cutting stock problem. This study is aimed at improving the effectiveness of conventional clustering processes by incorporating a new technique for the determination of optimal conditions for the sliding process. The new technique is referred to as ‘stringy effect’ which is based on minimizing the distance between centroids of the patterns during clustering. The efficiency of the proposed method is shown with the help of some typical multiple flat patterns.