Virtual Sensor-Based Geometry Prediction of Complex Sheet Metal Parts Formed by Robotic Rollforming

Abstract

Sheet metal parts can often replace milled components, strongly improving the buy-to-fly ratio in the aeronautical sector. However, the sheet metal forming of complex parts traditionally requires expensive tooling, which is usually prohibitive for low manufacturing rates. To achieve precise parts, non-productive and cost-intensive geometry straightening processes are additionally often required after forming. Rollforming is a possible technology for producing profiles at large rates. For low manufacturing rates, robotic rollforming can be an interesting option, significantly reducing investment at the cost of higher manufacturing times while keeping a high process flexibility. Forming is performed incrementally by a single roller set moved by the robot along predefined bending curves. The present work’s contribution to the overall solution is the development of an intelligent algorithm to calculate geometry after a robotic rollforming process based on process reaction forces. This information is required for in-process geometric distortion correction. Reaction forces and torques are acquired during the process, and geometry is calculated based on artificial intelligence (AI) applied to that information. The present paper describes the AI development for this virtual geometry sensing system.

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“…For the present study, incremental RoRoFo was chosen as example manufacturing process (see, e.g., [59,60]). It represents a forming process to produce angled profiles (in L, C or Z shape) from plain metal sheets.…”

Virtual Sensor for Estimating the Strain-Hardening Rate of Austenitic Stainless Steels Using a Machine Learning Approach

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