3D drill hole geometry prediction from force and torque data

Drilling is one of the most frequently used manufacturing methods. In aircraft manufacturing, common passenger planes require a very large number of drill holes. Verifying whether these holes meet specifications is a labor-intensive task that could benefit from automation. However, due to the large number of holes, in-process measurement is time-consuming and can delay production, rendering it infeasible. An alternative approach is to predict hole quality using process data collected during drilling. Previous studies have demonstrated the feasibility of extracting valuable information about manufacturing processes, tools, and products from such data. However, only single features of the drill holes are predicted. In this work, we propose a method to predict the hole geometry based on force and torque time series data recorded during the drilling process. The resulting point cloud can then be used to predict multiple features or serve as the basis for a more advanced quality control process that is not solely based on verifying whether a few geometric parameters fall within specification.

Subjects

deep learning

drilling

hole quality prediction

manufacturing

DDC Class

621: Applied Physics

629.13: Aviation Engineering

006: Special computer methods

Funding(s)

LuFo VI-3

Lizenz

https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication version

publishedVersion

Name

1-s2.0-S2212827126012096-main.pdf

Type

Main Article

Size

1023.41 KB

Format

Adobe PDF

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3D drill hole geometry prediction from force and torque data