Vision-based Oxy-fuel Torch Control for Robotic Metal Cutting

TL;DR

This paper introduces a vision-based control system for automated oxy-fuel metal cutting using a robot with an eye-in-hand camera, enabling precise cutting without prior knowledge of material thickness.

Contribution

The paper presents a novel vision-based control algorithm for oxy-fuel torch automation, including stability analysis and real-world validation on different metal thicknesses.

Findings

Successful cutting of metal plates across three thicknesses

Control relies solely on visual feedback without prior thickness knowledge

Stability of the control system is theoretically proven

Abstract

The automation of key processes in metal cutting would substantially benefit many industries such as manufacturing and metal recycling. We present a vision-based control scheme for automated metal cutting with oxy-fuel torches, an established cutting medium in industry. The system consists of a robot equipped with a cutting torch and an eye-in-hand camera observing the scene behind a tinted visor. We develop a vision-based control algorithm to servo the torch's motion by visually observing its effects on the metal surface. As such, the vision system processes the metal surface's heat pool and computes its associated features, specifically pool convexity and intensity, which are then used for control. The operating conditions of the control problem are defined within which the stability is proven. In addition, metal cutting experiments are performed using a physical 1-DOF robot and oxy-fuel cutting equipment. Our results demonstrate the successful cutting of metal plates across three different plate thicknesses, relying purely on visual information without a priori knowledge of the thicknesses.