An Adaptive Algorithm and Additively Manufactured Punch Used to Form Aluminum Sheet Metal Parts

Abstract

Self-adaptive mechanisms are gaining momentum in industrial processes. It is understandable that as the complexity increases, the human work must be augmented. Considering this, the authors have developed one such solution for the punch-forming process, using additive manufacturing, i.e., a 3D-printed punch, to draw into shape 6061-T6 aluminum sheets. This paper aims to highlight the topological study used to optimize the punch form shape, the methodology of the 3D printing process, and the material used. For the adaptive algorithm, a complex Python-to-C++ bridge was created. It was necessary as the script has computer vision (used for calculating stroke and speed), punch force, and hydraulic pressure measurement capabilities. The algorithm uses the input data to control its subsequent actions. Two approaches are used in this experimental paper, a pre-programmed direction and an adaptive one, for comparison purposes. The results, namely the drawing radius and flange angle, were statistically analyzed using the ANOVA methodology for significance. The results indicate significant improvements when using the adaptive algorithm.