Modeling and optimization of surface roughness in single point incremental forming process

Abstract

Single point incremental forming (SPIF) is a novel and potential process for sheet metal prototyping and low volume production applications. This article is focuses on the development of predictive models for surface roughness estimation in SPIF process. Surface roughness in SPIF has been modeled using three different techniques namely, Artificial Neural Networks (ANN), Support Vector Regression (SVR) and Genetic Programming (GP). In the development of these predictive models, tool diameter, step depth, wall angle, feed rate and lubricant type have been considered as model variables. Arithmetic mean surface roughness (Ra) and maximum peak to valley height (Rz) are used as response variables to assess the surface roughness of incrementally formed parts. The data required to generate, compare and evaluate the proposed models have been obtained from SPIF experiments performed on Computer Numerical Control (CNC) milling machine using Box–Behnken design. The developed models are having satisfactory goodness of fit in predicting the surface roughness. Further, the GP model has been used for optimization of Ra and Rz using genetic algorithm. The optimum process parameters for minimum surface roughness in SPIF have been obtained and validated with the experiments and found highly satisfactory results within 10% error.