Machine learning for robotic accuracy improvement in drilling operations

Drilling of rivet/fastener holes in aircraft presents a major manufacturing challenge where manual processes are heavily relied upon. It is estimated that modern aircraft can require upwards of 1.5 million holes to be drilled using methods that involve some form of manual input. This introduces concerns over both hole accuracy and worker wellbeing. Industrial robotic arms offer a potentially promising solution due to their reach and flexibility. However, limitations in their positional accuracy can be a barrier. This paper presents an open-loop methodology to address these limitations by improving the positional accuracy of a robotic drilling platform using Gaussian process regression (GPR) models, without the need for permanently installing costly metrology equipment, such as laser trackers or secondary encoders. The models demonstrated an average reduction in the positioning error of the platform from 0.993 mm down to 0.022 mm (97.7%) in x, and from 0.209 mm down to 0.055 mm (73.5%) in y in free air. This methodology is then demonstrated on physical drilling trials, where the average hole position error was reduced from 0.688 mm to 0.323mm (53.0%) in x. However, due to limitations in the training of the models, the error in y increased from 0.261 mm to 0.378 (45.1%). Despite these results being less successful, it is intended that they serve as a baseline for future development of the methodology so that it can include the effects of process (drilling) forces.