Prediction of surface roughness in single point incremental forming of AA3003-O alloy using artificial neural network

Abstract

Single point incremental forming (SPIF) is an emerging sheet metal forming process. Though the process is quite flexible as it does not require dedicated tools and dies, it has limitation with respect to surface quality of the finished product. In the present work, a statistical experimental investigation is carried out to determine the factors which profoundly affect the process performance. Subsequently, artificial neural network (ANN) approach is being utilised for prediction of the surface quality in relation with six input variables: step depth, feed rate, spindle speed, sheet thickness, wall angle and density of lubricant. The generalised model is being validated experimentally and is found in good agreement with experimental data.