Holistic process monitoring with machine learning classification methods using internal machine sensors for semi-automatic drilling

Since one third of rivet holes during aircraft assembly are produced with semi-automatic drilling units, in this work reliable and efficient methods for process state prediction using Machine Learning (ML) classification methods were developed for this application. Process states were holistically varied in the experiments, gathering motor current and machine vibration data. These data were used as input to identify the optimal combination of five data feature preparation and nine ML methods for process state prediction. K-nearest-neighbour, decision tree and artificial neural network models provided reliable predictions of the process states: workpiece material, rotational speed, feed, peck-feed amplitude and lubrication state. Data preprocessing through sequential feature selection and principal components analysis proved to be favourably for these applications. The prediction of the workpiece clamping distance revealed frequent misclassifications and thus, was not reliable.

Subjects

Classification

Data dimensionality reduction

Efficient Machine Learning

Internal sensors

Process monitoring

Semi-automatic drilling

DDC Class

600: Technik

Publication version

publishedVersion

Lizenz

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

Name

1-s2.0-S221282712200378X-main.pdf

Size

1.24 MB

Format

Adobe PDF

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Holistic process monitoring with machine learning classification methods using internal machine sensors for semi-automatic drilling