Artificial neural network (ANN), a Computational tool that is frequently applied in the modeling and simulation of manufacturing processes. The emerging forming technique of sheet metal which is typically called single point incremental forming (SPIF) comes into the map and the research interest towards its technological parameters. The surface quality of the end product is a major issue in SPIF, which is more critical with the hard metals. The part of the brass metal is demanded in many industrial uses because of its high load-carrying capacity and its wear resistance property. Considering the industrial interest and demand of the brass metal products, the present study is done with the SPIF experiment on calamine brass Cu67Zn33 followed by an ANN analysis for predicting the absolute surface roughness. The modeling result shows a close agreement with the measured data. The minimum and maximum errors are found in experiment 3 and experiment 7 respectively. The error of predicted roughness is found in the range of –30.87 to 20.23 and the overall coefficient of performance of ANN modeling is 0.947 which is quite acceptable.

Single point incremental forming process is a most economical Die-less forming process. The major constraint of it is that it is a time consuming process. In this work, a new attempt was made in incremental forming process using Multipoint tool for SS430 sheets to increase the formability and to reduce forming time. Fractography analysis was made to study the size of voids that were formed during fracture. The forming limit diagrams were drawn and compared for single point incremental forming and the multipoint incremental forming of SS430 sheet. It was proved that the formability of SS430 sheet in the multipoint forming was better than the formability of that in single point forming and the time consumed was reduced. The strain distribution in both processes had also been studied along with surface roughness.