Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.4552
Publisher DOI: 10.1016/j.procir.2022.05.094
Title: Holistic process monitoring with machine learning classification methods using internal machine sensors for semi-automatic drilling
Language: English
Authors: Hintze, Wolfgang 
Romanenko, Denys 
Molkentin, Lukas 
Köttner, Lars 
Mehnen, Jan Philipp 
Keywords: Classification; Data dimensionality reduction; Efficient Machine Learning; Internal sensors; Process monitoring; Semi-automatic drilling
Issue Date: 26-May-2022
Publisher: Elsevier
Source: Procedia CIRPs 107: 972-977 (2022)
Abstract (english): 
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.
Conference: 55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022 
URI: http://hdl.handle.net/11420/13345
DOI: 10.15480/882.4552
ISSN: 2212-8271
Journal: Procedia CIRP 
Institute: Produktionsmanagement und -technik M-18 
Document Type: Article
License: CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives) CC BY-NC-ND 4.0 (Attribution-NonCommercial-NoDerivatives)
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