Application of Machine Learning Within the Integrative Design and Fabrication of Robotic Rod Bending Processes

Abstract

This paper presents the results of independent research that aims to investigate the potential and methodology of using Machine Learning (ML) algorithms for precision control of material deformation and increased geometrical and structural performances in robotic rod bending technology (RBT). Another focus lies in integrative methods where design, material properties analysis, structural analysis, optimization and fabrication of robotically rod bended space-frames are merged into one coherent data model and allows for bi-directional information flows, shifting from absolute dimensional architectural descriptions towards the definition of relational systems. The working methodology thus combines robotic RBT and ML with integrated fabrication methods as an alternative to over-specialized and enclosed industrial processes. A design project for the front desk of a gallery in Paris serves as a proof of concept of this research and becomes the starting point for future developments of this methodology.