Coefficient of friction in service is strongly influenced by surface roughness parameters. The objective of this work is to maximize the surface roughness parameters Sku and minimize Sq, SHTp, Ssk to lower the coefficient of friction in lubricated case. Multi objective genetic algorithm is used to find the Pareto front consisting of a number of non dominated solutions. The number of solutions found is large. Agglomerative hierarchical clustering method is used to obtain 3,4 and 5 clusters. Two linkage methods- centroid and complete are used to generate clusters with population 45 and 100. The Pareto optimal points closest to the cluster centroids are obtained. Complete linkage, population 45, cross over:0.8 represents the population well. The cluster1 and cluster 2 (complete linkage, population 45, cross over:0.8) which have low values of Sq and Shtp can be further analyzed to select a high value of Sku and a low value of Ssk.
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