Motion control for laser machining via reinforcement learning

Abstract

Laser processing techniques such as laser machining, marking, cutting, welding, polishing and sintering have become important tools in modern manufacturing. A key step in these processes is to take the intended design and convert it into coordinates or toolpaths that are useable by the motion control hardware and result in efficient processing with a sufficiently high quality of finish. Toolpath design can require considerable amounts of skilled manual labor even when assisted by proprietary software. In addition, blind execution of predetermined toolpaths is unforgiving, in the sense that there is no compensation for machining errors that may compromise the quality of the final product. In this work, a novel laser machining approach is demonstrated, utilizing reinforcement learning (RL) to control and supervise the laser machining process. This autonomous RL-controlled system can laser machine arbitrary pre-defined patterns whilst simultaneously detecting and compensating for incorrectly executed actions, in real time.

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Identifiers

ORCID for Matthew Praeger: orcid.org/0000-0002-5814-6155

ORCID for James Grant-Jacob: orcid.org/0000-0002-4270-4247

ORCID for R.W. Eason: orcid.org/0000-0001-9704-2204

ORCID for Benjamin Mills: orcid.org/0000-0002-1784-1012

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